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A systematic review on sensor-based driver
behaviour studies: coherent taxonomy,
motivations, challenges,
recommendations, substantial analysis and
future directions
Ward Ahmed Al-Hussein
1
, Miss Laiha Mat Kiah
1
, Por Lip Yee
1
and
B B. Zaidan
2
1Department of Computer System and Technology, Faculty of Computer Science and Information
Technology, University of Malaya, Kuala Lumpur, Malaysia
2Department of Computing, Faculty of Arts, Universiti Pendidikan Sultan Idris, Perak, Malaysia
ABSTRACT
In the plan and development of Intelligent Transportation Systems (ITS),
understanding drivers behaviour is considered highly valuable. Reckless driving,
incompetent preventive measures, and the reliance on slow and incompetent
assistance systems are attributed to the increasing rates of traffic accidents. This
survey aims to review and scrutinize the literature related to sensor-based driver
behaviour domain and to answer questions that are not covered so far by existing
reviews. It covers the factors that are required in improving the understanding of
various appropriate characteristics of this domain and outlines the common
incentives, open confrontations, and imminent commendations from former
researchers. Systematic scanning of the literature, from January 2014 to December
2020, mainly from four main databases, namely, IEEEXplore, ScienceDirect, Scopus
and Web of Science to locate highly credible peer-reviewed articles. Amongst the
5,962 articles found, a total of 83 articles are selected based on the author’s predefined
inclusion and exclusion criteria. Then, a taxonomy of existing literature is presented
to recognize the various aspects of this relevant research area. Common issues,
motivations, and recommendations of previous studies are identified and discussed.
Moreover, substantial analysis is performed to identify gaps and weaknesses in
current literature and guide future researchers into planning their experiments
appropriately. Finally, future directions are provided for researchers interested in
driver profiling and recognition. This survey is expected to aid in emphasizing
existing research prospects and create further research directions in the near future.
Subjects Adaptive and Self-Organizing Systems, Autonomous Systems, Embedded Computing,
Spatial and Geographic Information Systems
Keywords Driver behaviour, Sensors, ADAS, Intelligent transportation systems, Naturalistic
driving, Traffic safety
INTRODUCTION
Traffic accidents are one of the main sources of injuries in the 21st century (Carmona et al.,
2015). Internationally, road accidents are considered the third widespread cause of death
next to cardiovascular diseases and cancer (Foo, 2015). In the United States alone, traffic
How to cite this article Ahmed Al-Hussein W, Mat Kiah ML, Lip Yee P, Zaidan BB. 2021. A systematic review on sensor-based driver
behaviour studies: coherent taxonomy, motivations, challenges, recommendations, substantial analysis and future directions. PeerJ Comput.
Sci. 7:e632 DOI 10.7717/peerj-cs.632
Submitted 22 April 2021
Accepted 18 June 2021
Published 25 August 2021
Corresponding author
Por Lip Yee, porlip@um.edu.my
Academic editor
Arun Somani
Additional Information and
Declarations can be found on
page 43
DOI 10.7717/peerj-cs.632
Copyright
2021 Ahmed Al-Hussein et al.
Distributed under
Creative Commons CC-BY 4.0
crashes cause 30,000 to 40,000 fatalities annually and are the number one cause of
death among the 8 to 24 years old (Guo, 2019). The majority of accidents are
ascribed to human-related factors, such as inclination towards aggressive driving (Demir,
Demir & Özkan, 2016). Understanding the behaviour of drivers during certain events on
the road is important to reduce accidents rate and improve traffic congestions (Derbel,
2018).
There are several methods to acquire driving data from drivers, such as the use of
simulations, questionnaires, observations, and crash reports. However, most of these
methods are criticized in the literature for being biased (Ziakopoulos et al., 2020). To solve
this issue, researchers aim to collect driving data from drivers in real-time experiments,
using sensors installed inside their vehicles. Studies that collect driving data from such
experimental settings are usually referred to as naturalistic driving studies (NDS) (Cai
et al., 2018). Some researchers utilize an existing NDS dataset, that is available online, in
their studies (Chen, Kusano & Gabler, 2015), while others collect data derived from in-
vehicle sensors (Park et al., 2016), thus producing their datasets.
The current literature lacks guidelines on how to plan experiments fittingly for data
collection. This is because existing surveys that covered data collection using in-vehicle
sensors have few limitations of their own. They frequently review a specific area in the
driver behaviour domain, such as car-following behaviour (Saifuzzaman & Zheng, 2014),
lane changing behaviour (Koesdwiady et al., 2016), intersection behaviour (Shirazi &
Morris, 2016), or specific driver behaviour traits such as impulsiveness (Bıçaksız & Özkan,
2016), sensation seeking (Zhang et al., 2019), and aggressiveness (Alkinani, Khan &
Arshad, 2020). Thus, their recommendations are limited to specific areas and cannot be
generalized. Likewise, the few surveys that have covered several areas in the driver
behaviour domain still need to provide in-depth analysis on reported experiments. This
arises questions, such as: What sample sizes are previously used in the literature? What is
the ratio of females to males in those presented samples? What are their age ranges and
driving experiences? How many vehicles are used in those experiments? on which types
of roads, these experiments are conducted? What is the time and duration of those
experiments? Which driving data are collected in those experiments and which sensors are
used for collecting these data? Answering such questions would certainly help future
researchers in designing their experiments appropriately and in developing more efficient
data acquisition systems (DAS). The current literature also lacks a survey that summarizes
in-vehicle sensor-based driver behaviour articles, that consider the presence of various
driver behaviour topics. The main motivation of this survey is a thorough examination of
the publications, in which researchers have collected driving data using in-vehicle sensors,
to provide a summary of its related topics, and to answer the aforementioned survey
questions. The foremost objectives of this work are to suggest a classification that
recognizes various aspects of this pertinent research area, summarize the common benefits,
issues, and recommendations found in the literature; report researchers’findings, present
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 2/50
recommendations for researchers on how to plan their experiments adequately for data
collection; finally, provide guidelines for future researchers interested in driver profiling
and recognition.
The current survey is structured as follows. The introduction section provides the reader
with a brief discussion about the topic, an explanation of existing gaps in the literature,
states the motivation behind addressing these gaps, and outlines the objectives. The Survey
Methodology section details the databases and the search query used for finding related
articles, and also the criteria for filtering the resultant articles. The results of the proposed
literature taxonomy are presented next to help researchers understand the main topics of
this domain. The distribution of results shows the resultant statistics of the filtered
articles. The Discussion section identifies the challenges faced by previous researchers,
summarizes the benefits of conducting such researches, and outlines future recommendations
for researchers. Significant scrutiny of the screened literature and recommendations on
how to plan experiments appropriately is presented in the Substantial Analysis section.
Then, future directions for researchers who are interested in driver profiling and
recognition are outlined. Finally, the Conclusions section presents a summary of the
conclusions formulated from this survey and the proposed future work.
SURVEY METHODOLOGY
Systematic review adopts methods to select, evaluate and synthesize ongoing experimental
trials in the literature. It helps to answer research questions and presents an unbiased
summary of findings through the use of inclusion/exclusion criteria.
Information sources
The databases that are chosen for searching related articles, are IEEEXplore, ScienceDirect,
Scopus, and Web of Science. These databases are selected due to their academic reliability
and because they sufficiently cover the driver behaviour domain and offer a broad view of
current literature.
Search query
Since the area of interest is driver behaviour, the search query is set to (“driver behavior”
OR “drivers behavior”OR “driver behaviour”OR “drivers behaviour”). The reason for
using the words “driver”,“drivers”,“behavior”and “behaviour”is because those words are
used interchangeably in the literature. Fig. 1 shows the search query that is used in the
selected databases. Table 1 shows the settings of the search query on the selected databases.
Articles selection
The filtration process of articles consists of three repetitions of screening and filtering.
Deletion of duplicated articles is performed during the first repetition. Unrelated articles
are deleted in the second repetition after examining their titles and abstracts. The
remaining filtered articles are then carefully read in the last repetition.
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Eligibility criteria for articles selection
A set of criteria is introduced to help decide which articles are to be included or excluded
during the three repetitions of screening and filtrating.
Eligibility criteria on research domain: articles are excluded if they are in eco-driving,
vehicle to vehicle, autonomous driving, or augmented reality domains.
Eligibility criteria on data sources: only experimental studies are included, thus
excluding articles in which driving data are obtained from predesigned questionnaires,
observations, simulations, or data from existing NDS.
Eligibility criteria on sensors used: articles that have not identified driver behaviour
based on vehicular sensors are excluded, such as articles that rely on wearables, eye
tracking, or smartphone sensors.
Eligibility criteria on vehicles’type: articles on which experiments are done specifically
on electric vehicles, or heavy vehicles such as buses, are excluded.
Eligibility criteria on articles’language: only English-written articles are included.
Table 2 shows detailed inclusion/exclusion criteria.
Search results
The result of applying the search query is 5,962 articles from the four selected databases in
the first repetition. The remaining articles after removing the duplicates in the first
repetition are 5,449. The result of scanning articles’titles and abstracts in the second
Figure 1 Search query. Full-size
DOI: 10.7717/peerj-cs.632/fig-1
Table 1 Settings of search query.
Digital library IEEE ScienceDirect Scopus WoS
Search period Jan 2014–Dec 2020 Jan 2014–Dec 2020 Jan 2014–Dec 2020 Jan 2014–Dec 2020
Language English English English English
Run on Full text & metadata Title, abstract & keywords Title, abstract & keywords Topic
Date of running search query February 2021 February 2021 February 2021 February 2021
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repetition is the exclusion of 4,081 articles. Further 1,285 articles are excluded following
full-text reading on the third repetition. The remaining articles are 83. Figure 2 illustrated
the entire process of search results.
Table 2 Inclusion/exclusion criteria.
Inclusion criteria Exclusion criteria
Only English written articles Non-English written articles
From January 2014 to December 2020 Articles before 2014 and after 2020
IEEEXplore papers were journals and magazines.
ScienceDirect papers were research articles.
Scopus papers were articles.
Web of Science papers were articles.
Eco driving, vehicle to vehicle, autonomous driving or augmented reality domains.
Predesigned questionnaires, observations, simulations or data from existing NDS.
Head movement, wearables, gloves, heart rate, eye tracking, EEG/ECG or smartphone sensors.
Electric vehicles or heavy vehicles.
Figure 2 Search results. Full-size
DOI: 10.7717/peerj-cs.632/fig-2
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 5/50
RESULTS OF LITERATURE TAXONOMY
A taxonomy is constructed and presented based on the authors’perspective of the 83
articles. This is to achieve one of the objectives of this survey, which is a classification for
in-vehicle sensor-based driver behaviour publications, in order to help future researchers
understand the various topics of this domain. The taxonomy that is proposed in Fig. 3,is
constructed based on the articles’main topic. The presented taxonomy confirms three
main categories, the first category is named ‘overall behaviour’, which contains articles that
Figure 3 Taxonomy of final articles. Full-size
DOI: 10.7717/peerj-cs.632/fig-3
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aim to study the overall behaviour of drivers instead of focusing on one particular
behavioural aspect. The second category is named ‘specific behaviour’, which contains
articles that are directed to the study of specific behavioural aspects instead of the overall
behaviour of drivers. The third and final category is named ‘overlap’which contains
articles that traverse the two previous categories.
Overall behaviour
The first main category in the taxonomy is related to articles that study the overall
behaviour of drivers instead of focusing on one particular behavioural aspect. As expected,
such studies have different goals which is why this category has six subcategories, namely,
external, systems, data, overall driving pattern, medical, and age. The number of articles in
this category is (n= 42/83).
External
External subcategory (n= 2/42) is related to studies that measure the impact of engaging in
a secondary task on driver behavior, such as using a cellphone or adjusting radio channels.
A portable system for controlling and monitoring a driver’s cellphone is proposed in
Khandakar et al. (2019), the system gathers data and sends it to a mobile phone to detect
driver behaviour. In Bastos et al. (2020), driving data were collected to investigate
cellphone use in Brazil and results are compared with those reported in international
studies.
Systems
Systems subcategory (n= 5/42) is related to studies that measure the impact and benefits of
vehicle assistance systems on driver behaviour. Researchers compare the impact of
adaptive cruise control (ACC) system on driver behaviour and trafficflow when the ACC
is on and off in Schakel et al. (2017). Similarly, Varotto et al. (2017) identify the main
factors that influence driver’s choice in activating and deactivating the ACC system. A
comprehensive modeling framework is proposed in Varotto et al. (2018) which describes
the underlying decision-making process of drivers, with full range ACC at the operational
level, based on risk allostasis theory (RAT). Galarza & Paradells (2018) propose and
implement a system that reduces the amount of information offered to drivers when
interacting with in-vehicle information systems (IVIS). Improvements on visual warning
systems are proposed in Tonguç, Akçay & Gürbüz (2018), to identify adverse driving
conditions and warn other drivers moving on the same route.
Data
Data subcategory (n= 6/42) is related to studies that aim to store and process collected
driving data. Two low-cost DASs that fused driving data, for driver behaviour analysis, are
proposed in Carmona et al. (2015) and Andria et al. (2016); The first gathered data from
on-board diagnostics (OBDII) and inertial measurement unit is (IMU) (Carmona et al.,
2015), while the latter gathered data is from controller area network (CAN) bus, IMU and
GPS (Andria et al., 2016). To help reduce synchronization errors of sensors’data, an
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automated method that relies on offline and online synchronization of data streams is
proposed in Fridman et al. (2016). Moreover, Xiao et al. (2020) propose a data fusion-
based approach, which integrated GPS data with OBDII readings, to build a model that
adapts to data fluctuations and errors in real-time experiments. Kim & Baek (2020) also
propose a system that automatically extracts data from sensor-based vehicles for
behavioural analysis. Furthermore, an unsupervised method for segmenting naturalistic
driving data, that targeted the formation of high-level understanding of driver behaviour,
from low-level sensor data, is introduced in Bender et al. (2015).
Overall driving pattern
This subcategory (n= 24/42) describes articles related to identifying, analyzing, and
modeling driving behavioural patterns. Articles in this subcategory are further classified
into three subgroups, namely, driver identification, overall behavioural modeling, and
aggressive driving recognition.
The first subgroup, driver identification, is related to studies that analyzed driving
data to identify an individual’s driving pattern. The first three articles (Cai et al., 2018;
Li et al., 2018;Büyükyildiz et al., 2017) propose methods to identify drivers based on their
driving data, i.e. personal ‘fingerprint’; the first two studies, Cai et al. (2018) and Li et al.
(2018) use machine learning methods, while the third study (Büyükyildiz et al., 2017)
use statistical methods to identify, and draw conclusions on, various driving patterns.
Similarly, Liu et al. (2017) proposed a visualization method, called deep sparse autoencoder
(DSAE), to recognize of distinctive driving behavioural patterns in continuous driving
data. Moreover, a graphical modeling method that can illustrate individuals’driving
habits by extracting and ranking their typical driving patterns is proposed in Chen et al.
(2019).
The second subgroup, overall behavioural modeling, is related to studies that analyzed
driving data for modeling various behavioural patterns. Park et al. (2016) modeled
behavioural patterns during unexpected traffic emergencies by analyzing drivers’pedal
behaviour in remodeled test vehicles. An improved unsupervised deep learning model that
analyzed interactions between road environment and driver behaviour throughout the
generation of graphical representations is proposed in Bichicchi et al. (2020). Moreover, an
unsupervised learning method, that presumes a two-layered hierarchical structure, called a
double articulation analyzer with a temporal prediction (DAA-TP), analyzed driving data
and predicted how drivers behaviour changes on a temporal axis, is proposed in Taniguchi
et al. (2014). Also, Wu & Jovanis (2016) proposed a flexible cohort-based analysis
structure, that analyzes and models behavioural patterns in NDS and announces warnings
through the onboard safety warning systems. Furthermore, Bella & Nobili (2020) model
drivers’behaviour with relation to a pedestrian crossing, a designated zebra crossings.
The third subgroup, aggressive driving recognition, is related to studies that analyze
driver behaviour to identify and differentiate aggressive driving patterns from normal
driving patterns. Zylius (2017) proposed a machine learning approach to identify the
aggressive and safe driving pattern by using features extracted from inertial signals
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(3-axis acceleration sensor); two levels of driving patterns are proposed (safe driving,
aggressive driving). A driving behaviour-based event data recorder (DBEDR), which
recognizes seven different driving behaviours and a danger level, is proposed in Wu,
Chen & Yeh (2013). Similarly, Gündüz et al. (2017) present a risk assessment model that
differentiates various driving patterns. The model categorizes aggressiveness into three
groups (low risk, medium risk, and high risk), the study relies on legal authority reports
that identified risks based on possible collision damages. Also, a clustering-based grading
method that evaluated driving performances of novice drivers against an experienced
driver baseline, producing a GPA on a 0–4 scale, is proposed in Liu & Hansen (2019).
Moreover, Zheng et al. (2017) assessed how driver type and driver behaviour are related in
an environment of high levels of pedestrian-vehicle interactions; this is done via the use of
a 1 to 10 scale; with 10 corresponding to the most aggressive style and 1 corresponding to
the least aggressive style. Furthermore, four major risky driving behaviours, are given a
score of 1 to 10, and their associated risk levels are identified and evaluated in Yuksel &
Atmaca (2020); a fuzzy logic-based risk assessment model is established based on the study
findings. Silva & Eugenio Naranjo (2020) introduced a data-driven machine learning
methodology for classifying driving patterns into three types (calm, normal, and
aggressive). Also, Lee & Jang (2017) developed a framework for identifying potentially
aggressive driving behaviours and provided drivers with feedbacks that guide them
towards adopting less aggressive driving; drivers are categorized into three levels of
aggressive behaviours (high, medium, low). Moreover, Li et al. (2015) allowed risk
consulting experts to assign risk scores to participate drivers (from 1 to 5, where 1 being
least aggressive and 5 being the most aggressive). Furthermore, González et al. (2014)
model the effect of aggressiveness on driving parameters, such as speed, longitudinal and
sideways accelerations, which cause a scaling transformation on their recorded files.
A methodology to track acceptable and non-acceptable driving behaviour and calculate
risk model using the envelope of the data and nota prior thresholds, is proposed in Joubert,
Beer & Koker (2016).Zardosht, Beauchemin & Bauer (2018) proposed a method that
analyzes statistical features of CAN signals to explore two distinct driving patters
(aggressive and moderate). An algorithm that evaluates the driver aggressiveness using
scores, called handling risk factors (HRF), to characterize driver behaviour is presented in
Hong et al. (2016a); high HRF values imply aggressive driving pattern, while low HRF
values imply safe driving pattern. Finally, a method that adapts dynamic time wrapping
(DTW) and hidden Markov model (HMM) to classify drivers into five different
behavioural patterns (timid, cautious, aggressive, best, assertive) is proposed in Yao et al.
(2020).
Medical
Both studies in the medical subcategory (n= 2/42) are related to the effects of certain
diseases on driver behaviour. Multiple sclerosis (MS) is a potential disease that disables the
brain and spinal cord, and as such, Krasniuk et al. (2019) undertook an on-road assessment
that incorporated strategic driving maneuvers to understand the underlying differences
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between MS drivers who passed the driving maneuvers against those who failed them.
Sleep apnea is a serious sleep disorder, and McLaurin et al. (2018) used topic modeling to
identify the effect of sleep apnea on driving, by comparing trips of drivers who suffer from
obstructive sleep apnea against drivers who do not have it.
Age
Age subcategory (n= 3/42) is related to studies that aim at understating the effect of age on
driver behaviour. Svetina (2016) displayed how driver’s behaviour tends to progress over
the years, by comparing the reaction times of drivers of different age groups in
experimental settings. Also, Merickel et al. (2019) assessed driving patterns in people with
age-related dysfunction, by analyzing vehicle control data such as steering, braking, and
accelerating in older adults with a range of cognitive and visual functional abilities.
Moreover, Farah et al. (2014) evaluated the effect of parents guiding teens to adopt vigilant
care in driving, especially during their first year of driving.
Specific behaviour
The second main category contains articles (n= 34/83) that study specific behavioural
aspects instead of the overall behaviour of drivers. This category has three subcategories
based on their goals, namely, specific location behaviour, specific driving pattern, and
specific action behaviour.
Specific location behaviour
This subcategory (n= 9/34) is related to studies that focus on analyzing driver behaviour
solely on specific geometric locations. Articles in this subcategory are further classified into
three subgroups namely, intersection behaviour, roundabout behaviour, and curves
behaviour.
The first subgroup, intersection behaviour, contains articles that analyze driver
behaviour solely at intersections. A hybrid state estimation (HSS)+HMM framework is
proposed to estimate driver behaviour near intersections (Gadepally, Krishnamurthy &
Ozguner, 2013). A methodology for analyzing driver behaviour by distance gaps accepted
by the majority of participating drivers, at a signal-based intersection and two-way
stop-controlled intersections, is proposed in Dabbour (2015). Moreover, a system
dedicated to predicting driver intention at non-signalized t-intersections, by integrating
clustering and classification models, is proposed in Yi et al. (2018). Furthermore, a method
for estimating driver behaviour with relation to pedestrians crossing intersections is
proposed in Lubbe & Rosén (2014).
The second subgroup, roundabout behaviour, contains articles that aim to analyze
driver behaviour solely at roundabout locations. Statistical analysis and evaluation of
driver’s behaviour when crossing roundabouts is presented in detail in Silva et al. (2014).
Also, Zyner, Worrall & Nebot (2019) presented a method for predicting driver intention at
roundabouts through the use of recurrent neural networks combined with a mixture
density network output function.
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The third subgroup, curves behaviour, contains articles that aim to analyze driver
behaviour solely at geometric curves. Li et al. (2019) developed a data-driven trajectory
model based on long short-term memory neural network (LSTM NN) to study the path
characteristics for experienced drivers when driving on curved two-lane roads. Moreover,
two driver behaviour models are proposed in Cerni & Bassani (2017) to investigate the
causes of differences between vehicle trajectories and road alignments and to estimate
vehicle trajectories on curve roads. Furthermore, Montella et al. (2014) developed a model
for predicting drivers’speed on curves, by using deceleration and acceleration rates in the
operating speed profiles.
Specific driving pattern
This subcategory (n= 21/34) describe articles that are related to analyzing specific
behavioural pattern. The articles in this subcategory are organized into three subgroups
namely, lane change behaviour, steering behaviour, and car-following behaviour.
The first subgroup, lane change behaviour, contains articles that aim to analyze drivers’
lane changing behaviour. Satzoda & Trivedi (2014) proposed a method that extracts data
from sensors, to analyse risky lane change behaviour from highway drives. A fusion
approach that utilizes data, from a camera and an OBDII, which detects drivers’lane
changing behaviour using a collaborative representation classifier (CRC) is proposed in
Gao, Murphey & Zhu (2018). Also, driving behaviour awareness (DBA) model, on a
dynamic Bayesian network and distributed GA, to estimate driver’s behaviour in lane
changing scenarios is proposed in Xie et al. (2018). Moreover, Hill, Elefteriadou & Kondyli
(2014) measured distributions of duration and gap acceptance in lane-changing scenarios
and examine the characteristics of conservative and aggressive drivers, according to their
lane-changing behaviour. Zhao et al. (2016) and Yao et al. (2016) studied lane changing
behaviour at the tactical level with on-road perspective. The first publication (Zhao et al.,
2016) addresses vehicle trajectory collection, while the second one (Yao et al., 2016)
proposes an automatic method for extracting lane change segments from continuous
driving. A fusion approach that utilizes multiple differing modality data for detecting
driver’s lane change behaviour via the use of a dimensionality reduction model, is
proposed in Gao, Murphey & Zhu (2019). Moreover, Wang et al. (2019) proposed a
theoretical lane change warning model, that determines warning thresholds for dangerous
lane changing behaviours. Also, researchers have examined the effect of integrated
collision, on teenage drivers, in lane-changing maneuvers, in Jermakian et al. (2017).
Finally, researchers compute left lane occupation time, by performing calibration of
overtaking acceleration models, in Llorca, Moreno & Garcia (2016).
The second subgroup, steering behaviour, contains articles that aim to analyse drivers’
steering behaviour. Katzourakis et al. (2013) proposed a method to quantify driver’s arm
neuromuscular admittance during steering in real-time driving. A deep learning-based
method to identify driver’s steering behaviour during near rear-end collision maneuvers is
proposed in Hassan et al. (2017). As well, the time-dependent concept of driver
decoupling/recoupling using a steer-by-wire system combined with override recognition
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by counter steering above a certain threshold is proposed in Heesen et al. (2014). Moreover,
a method to aid drivers in handling critical understeering situations is developed and
verified in Hildebrandt et al. (2015).
The third subgroup, car-following behaviour, contains articles that analyze and model
drivers’car-following behaviour. A driver-vehicle diagnostics system for car-following
behaviour based on Gaussian mixed model (GMM) and probability distribution functions
of dynamic characteristics is proposed in Butakov & Ioannou (2014).Bifulco et al. (2014)
presented an NDS, focusing on car-following behaviour for single vehicles on long
road segments, to obtain microscopic data to be used in statistical analysis and calibration
of driving models. Similarly, through a genetic algorithm calibration procedure,
experimental data are collected and used to calibrate the parameters of the prospect theory
car-following model, in Schorr, Hamdar & Silverstein (2014). Also, Errampalli, Mallela &
Chandra (2020) developed a modified car-following model for heterogeneous traffic
conditions in India, based on the general motors (GM) model and Hidas model, using data
collected on urban and non-urban corridors. Moreover, a coupla-based methodological
framework is presented in Das et al. (2019) for the safety evaluation of car-following
behaviour in non-lane-based traffic. Furthermore, Qi et al. (2015) identified common and
individual driving patterns (moderate, cautious, and aggressive patterns) between drivers
in car-following scenarios by developing an ensemble clustering method (ECM) for
data mining; based on fuzzy c-mean algorithm (KFCM) and modified latent dirichlet
allocation (LDA). Finally, an analysis of the impact of roadway characteristics on drivers’
car following behaviour is presented in Wang et al. (2014).
Specific action behaviour
This subcategory (n= 4/34) is related to studies that focused on analyzing a specific driving
action. Articles in this subcategory are further classified into two subgroups namely,
speeding behaviour and braking behaviour.
The first subgroup, speeding behaviour, contains articles that focus solely on speeding
behavior analysis. A two-level strategy, that predicts the risk of overspeeding before the
occurrence of accidents, using the dempster-shafer theory (DST) and the fuzzy theory
(FT), is presented in Derbel (2018). Moreover, Stigson et al. (2014) apply not speeding as
inceptive for pay as you speed (PAYS) concepts. Furthermore, Stephens et al. (2015)
examined the effect of anxiety, mood, and anger on speeding behaviour.
The second subgroup, braking behaviour, contains an article that focuses solely on
analyzing braking behaviour. Peng et al. (2019) propose a rough set-based method for
collision risk assessment by inferring braking action in near-crash situations.
Overlap
The third and final category of the taxonomy (n= 7/83) contains articles that traverse the
two previous main categories or their subcategories. The study that measured stopping
behaviour at intersections is Hong et al. (2016b), which measures how sensitive elder
drivers are, in their approaching and stopping behaviour at intersections, when compared
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with younger drivers. Two studies provide initial results on the effect of speeding
behaviour on different types of geometrical curves (Turner, Woolley & Cairney, 2015;
Maljković& Cvitanić, 2016). One study uses the results of a small-scale NDS to evaluate
the performance of existing models on two specific behaviours, car-following behaviour
and curve behaviour (Fleming et al., 2019). A novel collision warning algorithm that
employs a new index, named RDSI, to evaluate driving risk levels on two specific
behaviours, car-following and lane changing, is proposed in Wang et al. (2016).Lyu et al.
(2019) have explored the effect of advanced driver assistance systems (ADAS) on braking
behaviour and driving performance in lane-changing scenarios. The last article in this
category (Salmon et al., 2017) examines the impact of providing concurrent verbal
protocols on driving performance, on two different locations, i.e. signalized intersections
and roundabouts.
DISTRIBUTION OF RESULTS
The distribution of the finalized articles, across the main categories of the proposed
taxonomy, is presented in Fig. 4. The distribution of the finalized articles, in the four
selected databases, from 2014 until 2020 is presented in Fig. 5.
DISCUSSIONS
This section discusses the common motivations, challenges, and recommendations in the
reported literature because they are particularly valuable for future studies. The
motivations section informs imminent researchers on why the researchers before them
committed themselves to this field of science and what inspired them to follow their
Figure 4 Distribution of the finalized articles across the main categories of the proposed taxonomy.
Full-size
DOI: 10.7717/peerj-cs.632/fig-4
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 13/50
treasured research in this area. The challenges section helps future researchers in
identifying the common issues they could face during their research journey. Since
previous researchers admit their limitations yet define critical suggestions, the
recommendations section serves as a guide that helps resolve certain issues. It represents
the association connecting the new researchers with previously involved ones. This section
also contains an additional part that compares various survey articles, related to the driver
behaviour domain in existing literature, to the current survey.
Motivations
Investigating drivers’behaviour have numerous benefits. This section lists some of the
important benefits that encouraged researchers toward this domain. They are grouped into
four subsections. The corresponding references are cited for further discussions.
Benefits of improving traffic safety
Road traffic injuries remain the leading cause of death for young people between the age
range of 17 to 29. Furthermore, drivers over the age of 76 have the highest fatal crash rates
per mile of driving. A major factor that contributes to crashes is distractions, and it is
becoming paramount; at this point, to analyze the effects of distractions on driving
performance, especially in scenarios where drivers are talking while driving, such studies
can provide insight on how to limit the use of secondary tasks while driving (Khandakar
et al., 2019). Researchers need to pursue this domain, to better understand the human
factors that contribute to crashes, with the hopes of reducing future traffic accidents and
improving pedestrians’and passengers’well-being, because drivers’behaviour has a strong
impact on traffic safety (Silva & Eugenio Naranjo, 2020). Moreover, analyzing specific
Figure 5 Distribution of the finalized articles from 2014 to 2020.
Full-size
DOI: 10.7717/peerj-cs.632/fig-5
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 14/50
behaviours, such as car-following, or analyzing drivers’behaviour concerning specific
geometric locations, such as intersections, would help understand how certain behaviours
contribute more than others to crashes, and why crashes occur more often on particular
locations Gadepally, Krishnamurthy & Ozguner (2013) and Silva et al. (2014). Also,
identifying different driving styles and patterns would help make tailored policies and
regulations for drivers of certain ages and driving experiences, and guidelines for
correcting dangerous behaviours. Researchers should also study the impact of geometric
roadway design on driver behaviour, which would help build more suitable road
constructions for safe driving (Cerni & Bassani, 2017).
Benefits of managing data
During experiments, sensors collect raw data. The more experiments the researchers
perform, the more data filtration must be done, which is time-consuming and costly.
Developing a DAS that fuses sensors efficiently together and decreases raw data overhead,
is vital in NDS. Therefore, researchers should aim to improve data management processes
such as data extraction, sensors’fusion, and synchronization (Fridman et al., 2016).
Benefits related to enhancing assistance and navigation systems
Vehicles nowadays are packed with various assistance/navigation systems, such as ACC,
collision avoidance systems (CAS), and ADAS. These systems assist drivers in parking and
driving. Because such systems interact with drivers, it is possible to study the effects of
existing assistance systems on their behaviour, such as, understanding the effect of ACC
activation/ deactivation on traffic congestion and measuring the influence of ACC on the
efficiency of trafficflow (Schakel et al., 2017). Furthermore, researchers can introduce
enhancements for the current assistance systems, such as developing a prediction
system that is dedicated to measuring drivers’intention at specific locations, such as
non-signalized T-intersections (Yi et al., 2018) or determining the effects of integrated
collision warning systems on teenage drivers (Jermakian et al., 2017). Researchers can also
measure the impact of introducing new assistance systems on drivers’behaviour, such
as measuring the impact of using steer-by-wire systems as a concept for driver de-/
recoupling in automatic evasive maneuvers (Heesen et al., 2014). Furthermore, researchers
can propose a novel algorithm, that employs a new index in its evaluation of risky driving,
in collision warning systems (Wang et al., 2016). Researchers should also aim to
develop a conceptual model that clarifies driver decisions when interacting with collision
and control systems.
Benefits related to the health sector
Some diseases have an adverse effect on patients’cognitive and neuromuscular functions
which would inevitably affect their driving performance. The study of the effect of certain
diseases on driving is of prime importance for advancing the medical sector and the
safety of patients. For instance, MS attacks the protective sheath (myelin) covering the
nerve fibers, thus, causing problems in communication between the brain and the
remaining parts of the body. Researchers compare MS patients who succeeded versus those
who failed on-road assessment in Krasniuk et al. (2019). Similarly, sleep apnea, affects
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 15/50
patients’alertness and performance, so researchers have used topic modeling to
identify patterns from drivers with OSA during driving trips (McLaurin et al., 2018). More
studies on how other diseases affect driving behaviour should be investigated as well to
provide more insight for medical practitioners on how to find better treatments for their
patients.
Challenges
Researchers face numerous challenges when investigating this domain. This section
presents some of the up-to-date issues, which are grouped into four subsections, along with
citations for further discussions.
Issues relevant to modeling driver behaviour
Relying on official records can provide very limited insight on drivers’behaviour
during accidents, for example, police reports rarely provide data on when drivers are
distracted before accidents, and the occurrences of such distractions (Bastos et al., 2020).
One of the reasons why driver behaviour results vary; from one study to another, is the
difficulty of distinguishing different driving styles and anticipating other drivers’behaviour
on the road. This can lead to invalid assumptions, contradictory findings, and data that
cannot be used globally. Another issue is the use of small sample sizes which can
also lead to similar issues (Farah et al., 2014). Moreover, it is at times difficult to
deliberately induce critical or unsafe behaviour in road experiments, due to ethical reasons,
resulting in experiments being simulated and conclusions being limited or biased
(Bifulco et al., 2014).
Issues related to assistance and navigation systems
There is a need to develop more reliable and fast responding assistance systems. ADAS
development is not straightforward and has many issues. They are limited by user’s
acceptance due to their unreliability, such as detection mistakes occurring in bad weather
conditions, which lane-keeping systems are usually prone to (Khandakar et al., 2019).
Moreover, ACC systems have an opposing effect on trafficflow efficiency (Schakel et al.,
2017). Also, most ADAS are preconfigured to be used by average drivers while dismissing
the individual characteristics of particular drivers, i.e. no personalization features
because they use simplistic algorithms (Wang et al., 2016). Furthermore, forward collision
warning (FCW) systems suffer from a high rate of false-positive alarms (Lyu et al., 2019).
Such issues are major factors in limiting ADAS acceptance in the driving community.
Future researchers need to address these issues to improve drivers’trust in those systems.
Issues of existing systems
There are very few systems available on the market which claim to monitor driver
behaviour. Most of these systems use a simple sensor to monitor the drivers’behaviour,
such as a GPS. They are mostly used for tracking drivers in fleet management. Those
systems are not effective in preventing accidents, and they are used for monitoring
purposes only. Currently, there are no sophisticated commercial systems, that utilize
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 16/50
multiple sensors, such as lidars, accelerometers, and OBDII, to reliably collect data and
measure driving behaviour. However, researchers that have introduced DAS in their
studies, reported complications in using some of the sensors. Those issues are reported in
the section “issues related to data acquisition and handling”. Other vehicular systems, such
as ADAS and FCW have issues of their own and can impact negatively on drivers’
behaviour, those challenges are reported in section “issues related to assistance and
navigation systems”. One of the areas that future researchers can work on, is building a
reliable driver behaviour evaluation system that consists of two subsystems. The first
subsystem is a DAS that utilizes multiple sensors to collects driving data. The sensors of the
DAS subsystem should be able to operate on most vehicle brands, thus making the system
platform-independent. The second subsystem is an AI-based recognition system that
analyzes the collected data to evaluate drivers’performances and help prevent accidents.
Manufacturers should aim to develop such a system and make it available on the market.
Issues related to data acquisition and handling
There are several methods to acquire data, such simulations, questionnaires, and
experiments. Here are the issues researchers need to consider when choosing a method for
data collection. The use of simulators, throughout the literature, is mentioned as a
limitation (Katzourakis et al., 2013). Shortcomings in simulation use, include physical
limitations and realism, simulator sickness, and data accuracy. Questionnaires provide
subjective and biased judgments of drivers (Qi et al., 2015). It is preferable to collect data in
real-time experiments. However, there are challenges that researchers need to consider
when deciding to collect data in a naturalistic manner. For example, a smartphone’s
accelerometer requires high-performance computational capability (Khandakar et al.,
2019). Physiological/biomedical sensors add complexity because of the additional
instrumentation required and their effect on driver’s comfort and performance (Galarza &
Paradells, 2018). There are also other factors that researchers need to consider when
selecting the sensors for their proposed DAS, such as transmission overhead, complexity,
reliability, and cost. GPS poses an unaffordable burden for transmission overhead and
data storage (Xiao et al., 2020). Smartphone sensors have overestimation issues when
compared with fixed devices (Joubert, Beer & Koker, 2016). Researchers also need to think
about the synchronization of incoming data streams and the exclusion of irrelevant data
that sensors usually record. Such issues make the analysis of experimental data time-
consuming and costly (Satzoda & Trivedi, 2014). Moreover, it is challenging to use
computer vision techniques while maintaining accurate and robust results because the line
of sight must be aligned with the camera’sfield of view and cannot be blocked (Gao,
Murphey & Zhu, 2018). Furthermore, there are few distance sensors, such as 3D lidars, that
are considered very costly (Zhao et al., 2016). Another issue is that some experiments
require the presence of the researcher in the vehicle along with the driver, which could
influence the results (Schorr, Hamdar & Silverstein, 2014). Therefore, researchers should
carefully plan and select the sensors of their proposed DAS with the aforementioned
factors in mind.
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 17/50
Recommendations
This section presents a summary of the important recommendations that mitigates current
challenges, grouped into three subsections based on the party they are addressed to.
Recommendations to researchers
With regards to data collection, it is recommended to:
Develop a general model which determines the amount of data required in experimental
studies (Schakel et al., 2017).
Use large sample sizes, in experiments, for more reliable results (Galarza &
Paradells, 2018).
Include more sensors in proposed DAS to increase data collection accuracy
(Li et al., 2018).
Include a wider range of drivers to ensure the sample is diverse in age and gender
representation (Büyükyildiz et al., 2017).
Adapt blockchain technology to securely store driver history and data (Yuksel &
Atmaca, 2020).
With regards to driver behaviour modeling, it is recommended to:
Combine driving signals with psychological models (Li et al., 2015).
Add more parameters when analyzing data for information, and more features when
proposing classification models, and more metrics when evaluating these proposed
models (González et al., 2014;Zardosht, Beauchemin & Bauer, 2018;Krasniuk et al.,
2019).
Investigate whether aging has a significant effect on driving performance (Svetina, 2016).
Improve lane-change detection accuracy in current models while fully capturing the lane
change decision-making process (Hill, Elefteriadou & Kondyli, 2014).
With regards to existing research, previous researchers have either recommended
overcoming current research limitations or extending the current research
Researchers have suggested future researchers overcome some of the limitations in their
research, such as overcoming the concern of how aggregating driving data, over several
driving trips, would mask the effect of individual trip data (Wu & Jovanis, 2016),
overcoming the assumption of clear driver decision in the estimation process of driver
decision in near intersection scenario (Gadepally, Krishnamurthy & Ozguner, 2013) and
overcoming the issue that humans may change their neuromuscular behaviour
during the estimation window of driver’s arm admittance (Katzourakis et al., 2013).
Researchers have suggested extending their experimental research to other countries
using different types of vehicles and experimental setups (Schorr, Hamdar & Silverstein,
2014).
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Also, researchers have oftentimes encouraged future researchers to undertake research,
similar to their own, intending to validate their findings and confirm their conclusions
Wang et al. (2016) and Salmon et al. (2017).
Recommendations to developers
Car manufacturers should aim to develop an ADAS, with personalization features, that
reliably evaluate drivers’performance. Developers need to understand the reasons why
drivers choose to activate or deactivate assistance/navigation systems, such as ACC
(Varotto et al., 2017), in certain scenarios, and how to encourage them to rely more often
on these systems. They should also aim to address the issues behind ADAS low acceptance
in the driving community (Lyu et al., 2019), such as false alarms and inaccurate lane
detection.
Recommendations to safety and policy makers
More training courses should be implemented by policy makers before issuing licenses to
drivers, especially adolescences, such as, guidelines on how to keep minimum safe
distances between vehicles at intersections, and educational programs that increase the
overall awareness of the importance of safe driving and the importance of following traffic
rules and regulations (Galarza & Paradells, 2018).
Survey analysis
In this section, various survey articles, related to the driver behaviour domain in existing
literature, are compared to the current survey. Existing surveys mostly focus on reviewing
specific topics in the driver behaviour domain, rather than the entire domain, such as
investigating drivers’anger (Demir, Demir & Özkan, 2016). Some articles review specific
behaviours, such as car-following behaviour (Saifuzzaman & Zheng, 2014), lane changing
behaviour (Koesdwiady et al., 2016), and intersection behaviour (Shirazi & Morris,
2016), while others review specific driver traits such as impulsiveness (Bıçaksız & Özkan,
2016), sensation seeking (Zhang et al., 2019), and aggressive driving behaviour
(Alkinani, Khan & Arshad, 2020). Few articles review the use of machine learning-based
technology in ITS Nguyen et al. (2018),Martinez et al. (2017),Alsrehin, Klaib & Magableh
(2019) and Pamuła (2016), the evolution of vehicles’sensing technology and their effect
on safety (Massaro et al., 2016), and collision avoidance in assistance systems for intelligent
vehicles Dahl et al. (2018),Zhao et al. (2017) and Mukhtar, Xia & Tang (2015). The effect
of policies on driver behaviour and safety has been reviewed in Shinar & Gurion
(2019). The effect of roadside vegetation, roadside advertisements, and road markings on
safety have been reviewed in Van Treese et al. (2017),Oviedo-Trespalacios et al. (2019)
and Babićet al. (2020) respectively. Furthermore, one article reviews the importance of big
data and its analysis in the driver behaviour domain (Terzi, Sagiroglu & Demirezen, 2018).
It is important to note, only a few survey articles aim to cover various topics in this
domain, such as examining various driving patterns to identify links between driving styles
and road safety (Sagberg et al., 2015), examining driver behaviour in mixed traffic
conditions Munigety & Mathew (2016) and Asaithambi, Kanagaraj & Toledo (2016),
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 19/50
looking into trends and developments in road user behaviour and traffic safety (Van
Haperen et al., 2019) and systematically reviewing articles based on knowledge graph to
identify trends in driver behaviour domain (Liu et al., 2020). However, this complete
survey is prepared to provide material on the major driver behaviour research topics that
are covered in part or not covered in the above surveys. This present survey covers most
topics related to driver behaviour domain and suggests new directions for research and
development in the future. It creates a new classification system for recently completed
works on the basis of data analysis obtained from various studies. It also embodies what the
present literature lacks, which is a substantial analysis that provides guidelines for
researchers on how to set their experiments appropriately. Moreover, it explores the
current trends’challenges, motivations of those trends and suggests recommendations for
researchers. Furthermore, it proposes future research directions for driver profiling and
recognition.
The main contributions of the current survey are:
1. Provides a coherent taxonomy that classifies existing literature with regards to sensor-
based driver behaviour domain into several categories and subcategories based on
related topics.
2. Identifies the common challenges in current trends, explores the motivations in using
those trends, and summarizes recommendations for future trends.
3. Provides a substantial analysis section that provides answers to gaps found in the current
literature.
4. Offers future directions for researchers interested in driver profiling and recognition.
SUBSTANTIAL ANALYSIS
A substantial analysis is presented to answer the survey questions, presented in the
introduction section. This section provides guidelines that would help researchers design
their experiments and provides suggestions for further future work. This analysis is
categorized into eight parts to provide detailed information on the reported experiments,
as shown in Fig. 6.
Countries
Figure 7 shows the distribution of finalized articles according to the first authors’
affiliation. It is fair to state that the majority of countries, in which in-vehicle sensor based-
experiments were conducted, are developed countries. Such a report should motivate
researchers in countries with minimal or no reported studies. A model that compares
drivers’behaviour of multiple groups of people in different countries is a research gap in
the current literature that needs to be further explored.
Real-time experiments as a data source
During the filtration process, studies that rely on questionnaires, interviews, or simulations
as data sources are excluded. Moreover, studies that rely on existing NDS data are also
excluded, leaving only studies that generate their datasets, based on real-time experiments,
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 20/50
in the final set of filtered articles. Table 3 lists those studies along with information related
to participating samples, number of vehicles used, and experiments’settings. Table 4
lists the sensors used in those experiments and the collected data.
Researchers have used several sensors to collect data in real-time experiments and
generate their respective datasets. Studies that have not specified the sensors used in their
experiments are excluded from Table 4. Ideally, researchers use devices/sensors such as
CAN-bus and OBDII to record vehicle speed; IMU, gyroscopes, and accelerometers to
record steering behaviour; GPS to record vehicle position; lidar and radar to record
distances between the experimental vehicles and the preceding vehicle. Some researchers,
however, use some sensors for more than one purpose, such as using GPS not only to
record vehicle position but also to record vehicle’s speed and steering behaviour. Such a
technique is sometimes adopted by researchers to reduce the number of sensors/devices
used in their proposed DAS, thus reducing its cost. The literature analysis indicates the
need for a reliable low-cost DAS. Currently, there is no study, in the current literature, that
provides clear guidelines on how to build a reliable cost-efficient DAS, nor a study that
specifies which combination of sensors is most suitable to use than others when building
DAS.
Figure 6 The eight parts of the substantial analysis. Full-size
DOI: 10.7717/peerj-cs.632/fig-6
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 21/50
Sample size
Table 3 lists the number of participating drivers, reported in the articles reviewed. The
articles that have not reported the number of participating drivers are excluded from this
analysis. Due to the variable number of sample sizes reported, the authors categorize them
into 11 groups as seen in Fig. 8.
A total of 72 studies have reported the number of drivers employed in their
experiments. About 39% use a sample size of no more than 10 drivers. Almost 53% of
studies use a sample size of 20 drivers at the most. This analysis concludes that when
researchers do real-time experiments, most of them use very limited sample sizes.
The authors recommend that future researchers utilize a sample size of at least 30 drivers
to generalize their findings and ensure their results are not ‘sample size dependent’.
Number of vehicles used in experiments
Most researchers have utilized one vehicle in their experiments for data collection, few
studies have utilized more than one vehicle. The authors are careful not to assume the
number of utilized vehicles unless it is stated in the articles. Table 3 lists the number
of vehicles utilized in the reviewed articles. A total of 58 studies reported the number of
vehicles utilized in their research, and out of those studies, 70% (41 studies) use a single
vehicle. This limited number of vehicles can lead to limited representation of data,
given that one vehicle cannot represent the complete picture of driving behaviours.
Furthermore, the use of a limited number of vehicles prolongs the experiments’duration,
hence drivers must await their turn in a queue. However, increasing the number of
Figure 7 Distribution of articles with relation to authors’affiliation.
Full-size
DOI: 10.7717/peerj-cs.632/fig-7
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Table 3 Information related to real-time experimental studies.
Ref. Drivers Age group
(in years)
Male drivers Female drivers Driving
experience
(in years)
Car Experiments’
settings (type of
roads, time of
experiments,
duration of
experiments)
Carmona et al.
(2015)
10 N/A N/A N/A N/A N/A Type: urban, inter-
urban.
Duration: 2
scenarios, 10 min
each
Derbel (2018) 4 50, 50, 50, 70 N/A N/A N/A N/A Type: residential
area, highway
Cai et al. (2018) 25 22–57 N/A N/A 2–18 1 Type: urban
Park et al. (2016) 80 N/A N/A N/A < 2 1 Test drive road
Khandakar et al.
(2019)
N/A N/A N/A N/A N/A 1 N/A
Bastos et al.
(2020)
619–38 4 2 <1, 4, 7, 10, 21 6 Type: total 1,303 km.
Duration: 58.38 h
Schakel et al.
(2017)
840–49 50–59,
60+
7110–19, 20+ 8 Type: freeway.
Duration: 48 h
Varotto et al.
(2017)
23 25–51 (avg
31.57)
15 8 3–12, 13–33 1 Type: 35.5 km
freeway. Time: 7–9,
16–18, 18–20
Duration: 11 days
Varotto et al.
(2018)
23 N/A 15 8 N/A 1 Type: 46-km,
freeway. Time: 7–9,
16–18, 18–20.
Duration: each
driver 45–90 min
Galarza &
Paradells
(2018)
Two tests (20
& 15)
25–40, 21–35 N/A N/A >=30,000 km N/A Type: route was 38
km. Duration: avg.
70 min for drivers
Tonguç, Akçay &
Gürbüz (2018)
N/A N/A N/A N/A N/A 1 Type: smooth asphalt
road, damaged
asphalt road,
damaged lane road
Fridman et al.
(2016)
1 N/A 1 0 N/A 1 Duration: 5 runs,
duration of each
run 37–68 min
Xiao et al. (2020) N/A N/A N/A N/A N/A 1 Urban
Kim & Baek
(2020)
N/A N/A N/A N/A N/A 10 Type: 5 km road.
Duration: each
vehicle was driven 3
times over the
predestined road
(Continued)
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Table 3 (continued)
Ref. Drivers Age group
(in years)
Male drivers Female drivers Driving
experience
(in years)
Car Experiments’
settings (type of
roads, time of
experiments,
duration of
experiments)
Bender et al.
(2015)
1 N/A N/A N/A N/A 1 Type: suburb.
Duration: 13 min
Andria et al.
(2016)
2 N/A N/A N/A N/A 1 Type: urban
Li et al. (2018) 10 N/A N/A N/A N/A N/A Type: city-urban.
Time: 9–11, 15–17.
Duration: 16 h
Büyükyildiz et al.
(2017)
1
st
test 67, 2
nd
test 51
18–20, 21–26,
>26
N/A N/A 1
st
80000 km,
2
nd
25
1 1st test highway, 2nd
test urban and
highway
Liu et al. (2017) 1 N/A N/A N/A N/A 1 Type: circuit routes
Chen et al.
(2019)
124 N/A N/A N/A N/A 124 Type: city of Beijing.
Time: 7–12.
Duration: each
driver drove
approx. 480 h
Bichicchi et al.
(2020)
10 N/A N/A N/A N/A 1 Type: circuit route,
2,500 m. time:
morning, afternoon
Taniguchi et al.
(2014)
1 N/A N/A N/A N/A 1 Type: circuit routes
Wu & Jovanis
(2016)
76 20–70 (avg 41.6) 47.8% 53.3% <10, 10–30, >30 11 Type: freeway, major
arterial, minor
arterial
Time: daylight.
Duration: each
driver 4 weeks
Bella & Nobili
(2020)
16 23–45 (avg 31) N/A N/A >4 1 Type: 3.5 km route,
11 non-signalized
and 4 signalized
intersections. Time:
Peak-off hours
Zylius (2017) N/A N/A N/A N/A N/A 1 N/A
Wu, Chen & Yeh
(2013)
3 N/A N/A N/A 1 Type: freeway,
highway, suburb
Gündüz et al.
(2017)
N/A N/A N/A N/A N/A 252 Duration: over 10
months
Liu & Hansen
(2019)
20 in 2017, 13
in 2018
N/A N/A N/A experience,
novice drivers
1 Type: 2–3 lanes, local
road. Time:
Summer, sunny,
daytime Duration:
3 rounds, 20 min
each
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Table 3 (continued)
Ref. Drivers Age group
(in years)
Male drivers Female drivers Driving
experience
(in years)
Car Experiments’
settings (type of
roads, time of
experiments,
duration of
experiments)
Zheng et al.
(2017)
15 <25, 25–35, 35–
45, 45–55, >60
8 7 min 1year
experience
N/A Type: 2 routes in
Florida University
Campus
Time: 16:40.
Duration: Sept-Dec
2013
Yuksel &
Atmaca (2020)
3 27, 28, 29 3 0 8–10 3 Type: urban. Time:
Sunny. Duration: 2
weeks
Silva & Eugenio
Naranjo (2020)
50 25–34, 35–54,
55–65 (avg
48.24)
31 19 avg 15.78 1 Type: 8 km, urban
highways,
suburban. Time:
daytime. Duration:
both routes had
duration of 24 min
Lee & Jang
(2017)
43 N/A N/A N/A N/A N/A Type: metropolitan
areas. Duration: 8
days
Li et al. (2015) 78 N/A N/A N/A N/A N/A Type: urban highway.
Duration: avg 105
min per driver
González et al.
(2014)
10 27–48 10 0 N/A N/A Type: road network.
Duration: 2 days
Joubert, Beer &
Koker (2016)
N/A N/A N/A N/A N/A 124 Duration: 1 month
Zardosht,
Beauchemin &
Bauer (2018)
12 20–47 6 6 N/A N/A Type: urban, 28.5 km.
Time: daytime.
Duration: 1 h
Hong et al.
(2016a)
1 N/A 1 0 expert driver 1 Type: snowy road,
asphalt road, local
road
Yao et al. (2020) 120 N/A N/A N/A N/A 120 N/A
Krasniuk et al.
(2019)
35 18–59 (avg 50) 14 21 N/A 1 Type: parking lot,
suburban, highway
McLaurin et al.
(2018)
100 (66 OSA,
34 compare)
N/A 62 (44 OSA, 18
comparison)
38 (22 OSA, 16
comparison)
N/A 100 Type: two-week
period before and
three months after
beginning CPAP
Svetina (2016) 351 20–35, 35–50,
50–65, 65–80
N/A N/A min 2 years N/A Type: practice track
300 m
(Continued)
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Table 3 (continued)
Ref. Drivers Age group
(in years)
Male drivers Female drivers Driving
experience
(in years)
Car Experiments’
settings (type of
roads, time of
experiments,
duration of
experiments)
Merickel et al.
(2019)
77 65–90 41 36 40–76 (avg 58) 77 Type: rural, urban.
Time: daytime,
nighttime
Duration: 2–3
months periods
separated by 1 year
Farah et al.
(2014)
217 families N/A N/A N/A N/A N/A Duration: 11 months
Gadepally,
Krishnamurthy
& Ozguner
(2013)
N/A N/A N/A N/A N/A 1 Type: highway,
urban. Duration: 1
h
Dabbour (2015) 28 19–73 (avg 40.5) 15 13 N/A 28 Duration: 48 h
Yi et al. (2018) 3 N/A N/A N/A N/A N/A Duration: 225 min
Lubbe & Rosén
(2014)
62 20–61 (avg 42) 58% 42% 3–41 (avg 23) N/A Type: urban
Silva et al. (2014) 540–55 3 2 20 1 Type: 3.6 km arterial
urban road
Zyner, Worrall
& Nebot (2019)
N/A N/A N/A N/A N/A 1 Type: 5 roundabout
in suburbs, high
traffic
Time: Morning,
afternoon.
Duration: each
recording of
roundabout was 14
h
Li et al. (2019) 20 avg 41.7 16 4 16.8 3 Type: urban
Cerni & Bassani
(2017)
21 20–50 17 4 N/A N/A Type: two-lane rural
highways
Montella et al.
(2014)
39 23–70 (avg
35.77)
25 14 min. 5,000 km
in year
1 Type: rural
motorway. Time:
9:30 AM–4:30 PM
on workdays.
Duration: 2 months
Satzoda &
Trivedi (2014)
2 N/A N/A N/A N/A 1 Type: highway
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 26/50
Table 3 (continued)
Ref. Drivers Age group
(in years)
Male drivers Female drivers Driving
experience
(in years)
Car Experiments’
settings (type of
roads, time of
experiments,
duration of
experiments)
Gao, Murphey &
Zhu (2018)
10 N/A 5 5 novice to expert N/A Type: 2–3 lanes, local
and freeway. Time:
daytime, sunny,
cloudy. Duration:
20 trips, 10–40 min
each
Xie et al. (2018) 50 N/A N/A N/A N/A N/A Type: highways, ring
road, airport
express, normal city
road (18km)
Hill, Elefteriadou
& Kondyli
(2014)
1st test 15, 2nd
test 31
30–39 1st test 6/2nd
test 19
1st test 9/2nd
test 12
N/A N/A Type: freeway
Zhao et al.
(2016),Yao
et al. (2016)
1 N/A N/A N/A N/A 1 Type: expressway, 64
km
Gao, Murphey &
Zhu (2019)
10 18–40 5 5 novice-to-expert N/A Type: 2–3 lanes, local
and freeway. Time:
daytime, sunny,
cloudy. Duration:
22 trips, 10–40 min
each
Wang et al.
(2019)
15 27–48 (avg 34.7) 13 2 3–23 (avg 8.4), 3
year no
accident
1 Type: expressway
Jermakian et al.
(2017)
40 16-17 20 20 6-9 months 1 Type: highway,
urban, suburban.
Duration: 14
months (3,259 h)
Llorca, Moreno
& Garcia
(2016)
N/A N/A N/A N/A N/A 1 Type: rural
Katzourakis
et al. (2013)
8 N/A 6 2 N/A 1 Duration: each driver
had 16 runs
Hassan et al.
(2017)
4 26, 28, 33, 33 N/A N/A 7, 8, 13, 10 N/A Type: two-sided
straight road, width
3.5 m
Heesen et al.
(2014)
45 23–50 (avg 30.4) 27 18 Min. 1 year 1 Type: test track, 2
km. Duration: 1 h
each driver
Hildebrandt
et al. (2015)
63 N/A 54% 46% N/A 1 N/A
(Continued)
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 27/50
Table 3 (continued)
Ref. Drivers Age group
(in years)
Male drivers Female drivers Driving
experience
(in years)
Car Experiments’
settings (type of
roads, time of
experiments,
duration of
experiments)
Butakov &
Ioannou
(2014)
3 26, 27, 30 3 0 3, 4, 5 1 Duration: 6–18, 4, 3 h
Bifulco et al.
(2014)
100 (20–24), (25–40)
(41–64), >65
N/A N/A N/A N/A Type: highway,
urban. Time: 8:30–
18:30. Duration:
120 h
Schorr, Hamdar
& Silverstein
(2014)
18 20–33 9 9 N/A 1 Type: predefined
loop featured 4
segments
Errampalli,
Mallela &
Chandra
(2020)
1 N/A N/A N/A N/A 1 Type: 3 km urban
corridors and 25
km non-urban
corridors.
Duration: 3 runs on
urban corridors and
3 runs on non-
urban corridors
Das et al. (2019) 2 N/A N/A N/A N/A 2 Type: 37.33 km,
rural. Time: 14:00–
16:30
Qi et al. (2015) 10 N/A N/A N/A N/A 1 Type: urban. Time:
8–9, 9–10. Duration
2 h each driver
Wang et al.
(2014)
36 30–69 (avg 44.9) N/A N/A 1–47 (avg 12) 1 Type: urban, Freeway
Peng et al. (2019) 51 25–56 (avg 37) 45 6 3–16 (avg 12) 1 Type: urban. Time:
7:30–9:30, 17–19.
Duration: 265.8
Stigson et al.
(2014)
196 (128 test,
68 control)
22–66 65.6% 34.5% N/A N/A Duration: 11 months
Stephens et al.
(2015)
19 22–47 (avg
30.84)
12 7 2-30 (avg 12.78) 1 Type: freeway,
arterial road.
Duration: 50 min
Hong et al.
(2016b)
20 22–24 (avg 23),
69–78
7 young, 5 old 3 young, 5 old 1y (young
drivers)
N/A N/A
Turner, Woolley
& Cairney
(2015)
40 N/A 40 0 <3, >=15 1 Type: rural driving
route. Duration:1
h:35 min each
driver
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 28/50
vehicles is also challenging, because it increases the cost of experiments’design and
DAS installation. Also, installing the DAS in participants’vehicles, to include more
vehicles during experiments, makes the collected data susceptible to biases because not all
drivers drive the same vehicles. Figure 9 presents the number of vehicles used in the
reported experiment of the reviewed articles. The authors recommend that future
researchers allow all participating drivers to drive at least two vehicles, to enable more
representation of vehicles and to answer a gap in the current literature. This gap is
presented in the form of two questions: does changing the vehicle affect driver’s behaviour?
and if it does, does the effect of changing vehicles become statistically significant on
drivers’behaviour?
Males to females ratio in participating samples
The number of males and females who participated in real-time experiments have been
reported in Table 3. Of those who reported the ratio of male to female drivers, 32 studies
have reported using more male drivers than female drivers, 6 studies used the same ratio of
male drivers and female drivers, and only 2 studies reported using more female drivers
than male drivers. This survey analysis concludes that most studies (80%) use more male
drivers than female drivers and are unsuccessful in considering gender variation in their
reported samples, as female drivers are currently under-represented in the collected
driving datasets; which makes the inclusion of more female drivers a necessity in potential
Table 3 (continued)
Ref. Drivers Age group
(in years)
Male drivers Female drivers Driving
experience
(in years)
Car Experiments’
settings (type of
roads, time of
experiments,
duration of
experiments)
Maljković&
Cvitanić(2016)
20 25–60 13 7 2–30 20 Type: 24 km long
segment. Time:
Daylight
Duration: Oct
2012–April 2013
Fleming et al.
(2019)
925–40 N/A N/A Min. 3 years N/A Duration: 7 h for car
following scenarios
and 10 h for
cornering scenarios
Wang et al.
(2016)
24 N/A 20 4 N/A 3 Type: urban
Lyu et al. (2019) 32 22–55 (avg 32.2) 21 11 2–18 (avg 6.9) N/A Type: urban,
expressway,
freeway. Time:
8:00–17:30.
Duration: 6,500 km
of data
Salmon et al.
(2017)
20 25–49 (avg 38.3) 10 10 avg 20.6 1 Type: 15 km urban
route in the suburbs
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 29/50
Table 4 Sensors utilized in reported experiments and their correspoding recorded data.
References CAN OBDII IMU Accelerometer Gyroscope Lidar Radar Pedal
sensor
Laser Camera MobileEye GPS Recorded data for
analysis
(A, acceleration;
B, braking; D,
deceleration; DI,
distance; LA,
lateral
acceleration;
RPM, revolutions
per minute; S,
speed; SA,
steering angle;
THW, time
headway; TTC,
time to collision;
VA, vertical
acceleration;
VHW, vehicle
headway; Y, yaw)
Carmona et al.
(2015)
✓✓✓ ✓ ✓S, A, B, RPM, LA,
SA
Derbel (2018) ✓✓ ✓S, A
Cai et al. (2018) ✓✓ ✓✓✓S, A, B, LA, Y, SA
Park et al. (2016) ✓✓✓S, A, D, B, RPM,
C, SA
Khandakar et al.
(2019)
✓✓ S, A, B, RPM
Bastos et al.
(2020)
✓✓S
Schakel et al.
(2017)
✓✓ ✓ S, A, D, THW,
VHW
Varotto et al.
(2017)
✓✓ ✓S, A, D, B, THW,
VHW, DI
Varotto et al.
(2018)
✓✓✓✓S, A, D, B, TTCi,
VHW, DI
Galarza &
Paradells
(2018)
✓ ✓ S, A, B, LA, SA
Tonguç, Akçay &
Gürbüz (2018)
✓✓ ✓ ✓S, A, B, LA, VA,
SA
Fridman et al.
(2016)
✓✓✓ ✓ ✓S, A, SA
Xiao et al. (2020) ✓ ✓ S, SA
Kim & Baek
(2020)
✓✓ ✓ ✓S, A, D, B, SA
Bender et al.
(2015)
✓✓ ✓ S, A, D, B, Y
Andria et al.
(2016)
✓✓✓ ✓ ✓ ✓S, A, D, B, RPM, C
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 30/50
Table 4 (continued)
References CAN OBDII IMU Accelerometer Gyroscope Lidar Radar Pedal
sensor
Laser Camera MobileEye GPS Recorded data for
analysis
(A, acceleration;
B, braking; D,
deceleration; DI,
distance; LA,
lateral
acceleration;
RPM, revolutions
per minute; S,
speed; SA,
steering angle;
THW, time
headway; TTC,
time to collision;
VA, vertical
acceleration;
VHW, vehicle
headway; Y, yaw)
Li et al. (2018) ✓S, A, LA, VA
Büyükyildiz et al.
(2017)
✓✓✓S, A, D, LA
Liu et al. (2017) ✓✓S, A, B, LA, Y, SA
Chen et al.
(2019)
✓ ✓ S, A, D, RPM
Bichicchi et al.
(2020)
✓✓✓S, A, LA, A
Taniguchi et al.
(2014)
✓✓✓S, A, B, TTC,
VHW, SA
Wu & Jovanis
(2016)
✓S
Bella & Nobili
(2020)
✓✓S, DI, D, TTC
Zylius (2017) ✓S, A, LA, VA
Wu, Chen & Yeh
(2013)
✓✓ ✓ ✓S, A, D, LA, DI
Gündüz et al.
(2017)
✓ ✓ S, A, D, LA, SA
Liu & Hansen
(2019)
✓✓✓✓S, A, D, B, SA, DI
Zheng et al.
(2017)
✓✓S, B
Yuksel &
Atmaca (2020)
✓A, D, SA
Silva & Eugenio
Naranjo (2020)
✓S, A, D, B
Lee & Jang
(2017)
✓S, A, D, Y, SA
Li et al. (2015) ✓S, A, D, B, LA
(Continued)
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 31/50
Table 4 (continued)
References CAN OBDII IMU Accelerometer Gyroscope Lidar Radar Pedal
sensor
Laser Camera MobileEye GPS Recorded data for
analysis
(A, acceleration;
B, braking; D,
deceleration; DI,
distance; LA,
lateral
acceleration;
RPM, revolutions
per minute; S,
speed; SA,
steering angle;
THW, time
headway; TTC,
time to collision;
VA, vertical
acceleration;
VHW, vehicle
headway; Y, yaw)
González et al.
(2014)
✓S, A, LA
Joubert, Beer &
Koker (2016)
✓ ✓ S, A, LA, VA
Zardosht,
Beauchemin &
Bauer (2018)
✓✓ ✓ ✓ ✓S, A, B, DI
Hong et al.
(2016a)
✓✓✓ S, Y, LA, SA
Yao et al. (2020) ✓ ✓ S, A, D, Y
Krasniuk et al.
(2019)
✓S, DI
McLaurin et al.
(2018)
✓✓ ✓ ✓S, A, LA
Svetina (2016) ✓✓D
Merickel et al.
(2019)
✓✓ ✓ ✓S, A, D, B, SA
Gadepally,
Krishnamurthy
& Ozguner
(2013)
✓✓✓S, A, RPM, LA, Y
Dabbour (2015) ✓S, A
Yi et al. (2018) ✓✓ ✓S, A
Lubbe & Rosén
(2014)
✓✓S, A, D, B, TTC,
THW, DI
Silva et al. (2014) ✓ ✓ S, A, LA
Zyner, Worrall
& Nebot (2019)
✓DI
Li et al. (2019) ✓ ✓ S, A, LA
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 32/50
Table 4 (continued)
References CAN OBDII IMU Accelerometer Gyroscope Lidar Radar Pedal
sensor
Laser Camera MobileEye GPS Recorded data for
analysis
(A, acceleration;
B, braking; D,
deceleration; DI,
distance; LA,
lateral
acceleration;
RPM, revolutions
per minute; S,
speed; SA,
steering angle;
THW, time
headway; TTC,
time to collision;
VA, vertical
acceleration;
VHW, vehicle
headway; Y, yaw)
Cerni & Bassani
(2017)
✓S, SA
Montella et al.
(2014)
✓✓✓✓✓✓S, A, D, B, C,
VHW, SA
Satzoda &
Trivedi (2014)
✓✓ ✓✓✓S, A, B, Y, SA
Gao, Murphey &
Zhu (2018)
✓✓S, SA
Xie et al. (2018) ✓✓✓S, LA, SA
Hill, Elefteriadou
& Kondyli
(2014)
✓✓S, A, B
Zhao et al.
(2016),Yao
et al. (2016)
✓✓✓ ✓✓ ✓S, A, Y
Gao, Murphey &
Zhu (2019)
✓✓ ✓ ✓S, DI, SA
Wang et al.
(2019)
✓✓✓✓S, D, A, DI
Jermakian et al.
(2017)
✓✓ ✓ ✓✓S, A, THW, DI, Y
Llorca, Moreno
& Garcia
(2016)
✓✓ ✓S, A, D, DI
Katzourakis
et al. (2013)
✓✓✓ ✓ ✓S, A, LA, SA
Hassan et al.
(2017)
✓✓ ✓ S, TTC, DI, Y
Bifulco et al.
(2014)
✓✓✓S, A, TTC, THW,
DI
(Continued)
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 33/50
Table 4 (continued)
References CAN OBDII IMU Accelerometer Gyroscope Lidar Radar Pedal
sensor
Laser Camera MobileEye GPS Recorded data for
analysis
(A, acceleration;
B, braking; D,
deceleration; DI,
distance; LA,
lateral
acceleration;
RPM, revolutions
per minute; S,
speed; SA,
steering angle;
THW, time
headway; TTC,
time to collision;
VA, vertical
acceleration;
VHW, vehicle
headway; Y, yaw)
Schorr, Hamdar
& Silverstein
(2014)
✓✓ ✓S, A, THW, VHW
Errampalli,
Mallela &
Chandra
(2020)
✓✓S, A, D, DI
Das et al. (2019) ✓✓S, TTC, DI
Qi et al. (2015) ✓✓✓S, A, DI
Wang et al.
(2014)
✓✓ ✓ ✓S, A, B, TTC,
TTCi, THW, Y,
SA
Peng et al. (2019) ✓✓✓✓✓✓S, A, D, B, TTC,
VHW, SA
Stigson et al.
(2014)
✓S
Stephens et al.
(2015)
✓✓ ✓S, A, D, B, SA
Hong et al.
(2016b)
✓✓ ✓S, A, D, B, LA
Turner, Woolley
& Cairney
(2015)
✓✓ ✓✓ S, A, D
Maljković&
Cvitanić(2016)
✓S, A
Fleming et al.
(2019)
✓✓S, A, TTC, TTCi,
THW, VHW,
LA, DI
Wang et al.
(2016)
✓✓✓S, A, B, TTC,
TTCi, LA
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 34/50
future researches. The authors recommend that future researchers collect data from an
equal number of male and female drivers to ensure their sample is equally diverse in its
gender representation and to identify the major differences in driving behaviour of male
and female drivers.
Drivers’age
Drivers of various ages (18–90) have participated in previous studies, as seen in Table 3,
however, studies are limited in their use of samples aged above 60 or below 20, since most
studies concentrated on sampling the majority of drivers within the age range of 25 to
50 years old. This makes the representation of adolescent drivers and elder drivers very
limited in the collected datasets. The authors recommend that future researchers
include drivers of ages above 55 years old and below 22 years old to ensure their sample is
diverse in age representation. Also, to identify the root causes of differences in driving
behaviour between drivers of various age ranges.
Drivers’experience
There are various metrics, used in the literature, for determining drivers’experience. The
first metric is the years of driving, which is concluded from the year on which the drivers’
licenses are issued. The second metric is the total amount of distance traveled by the
driver since he started driving. The third metric is the number of times per month the
driver usually drives. There are other notions used by previous researchers to describe
drivers’experience, such as the term ‘experience vs inexperience’; such metrics are very
subjective since they can have a different interpretation for different people. Diversity in
Table 4 (continued)
References CAN OBDII IMU Accelerometer Gyroscope Lidar Radar Pedal
sensor
Laser Camera MobileEye GPS Recorded data for
analysis
(A, acceleration;
B, braking; D,
deceleration; DI,
distance; LA,
lateral
acceleration;
RPM, revolutions
per minute; S,
speed; SA,
steering angle;
THW, time
headway; TTC,
time to collision;
VA, vertical
acceleration;
VHW, vehicle
headway; Y, yaw)
Lyu et al. (2019) ✓✓ ✓✓✓✓S, A, D, B, THW,
VHW, LA, SA
Salmon et al.
(2017)
✓✓ ✓✓S, A, B, SA
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 35/50
utilizing drivers of variable experiences is important in future studies because novice
drivers are disregarded in most experimental studies in present-day literature. Table 3
shows the experience level of drivers reported in the literature. The authors recommend
Figure 8 Distribution of sample sizes across the filtered articles.
Full-size
DOI: 10.7717/peerj-cs.632/fig-8
Figure 9 Reported number of vehicles used in the filtered literature.
Full-size
DOI: 10.7717/peerj-cs.632/fig-9
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 36/50
avoiding notions that are subjective, such as the second and third metric, as both can be
difficult to accurately estimate, and rely on the first metric when reporting drivers’
experience.
Experiments settings (route, time and duration of experiments)
This section analyzes the information related to the road type, time, and duration of
reported experiments. Table 3 lists the aforementioned information reported in the
reviewed articles. For route of experiments, it is difficult to conclude the most commonly
used routes. This is because not all studies used the same terms to describe route types
and that some studies allowed drivers to drive using their vehicles for weeks or months
in a city, which makes it difficult to pinpoint which routes the drivers drove on. However, it
is safe to say that experiments are conducted on driving areas that can be described as
urban, suburban, rural, expressway, freeway, and highway. Some researchers stated only
the length of the road. Some researchers stated only the shape of the road, such as circuit
road, ring road, minor arterial road, and major arterial road. Some experiments are
conducted at parking lots and university campuses. There is a limited number of studies
conducted in rural areas, as seen in Table 3, which could serve as a motivation for future
researchers. Also, results accumulated from experiments that are performed on private
fields and university campuses may have disputed validity when applied to other routes.
Researchers should aim to conduct experiments on various roads. However, this would
increase the overall cost and duration of experiments, which could be challenging for
researchers with limited funds.
For the time of experiments, some articles specified the exact periods on which the
experiments are conducted, such as at ‘8:00 AM to 12 PM’. Few articles use terms such as
‘daytime’or ‘daylight’while others use terms such as ‘weekends’or ‘workdays. A research
that compares driving behaviour in rush hours and driving behaviour in normal hours
seems necessary.
For the duration of experiments, some studies took as little as 13 min, while others as
much as 480 h for each driver. The duration of experiments is correlated to the objective
of the study. If the study aims to collect naturalistic data using a DAS installed in the
drivers’vehicles, then usually the experiments’duration is somewhat lengthy; however, if
the study aims to examine driver behaviour with relation to a specific aspect, such as
braking behaviour, then the duration of experiments is usually short. Some studies prefer
to mention the total distance traveled by drivers during experiments rather than the actual
duration, such notions are often stated in studies that installed the DAS in the drivers’
vehicle.
Most researchers conducted their experiments in sunny/cloudy conditions. It is
important to note that the use of the terms ‘good’or ‘bad’weather conditions, in the
reported literature, are somehow subjective as they have different interpretations for
different individuals. A research that considers the effect of variable temperatures and
visibility ranges on driver behaviour is deemed necessary; such metrics are seldom cited in
publications.
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 37/50
In summary, the authors recommend that future researchers select a suitable route, that
contains various road types, for their experiments. For instance, a route that contains
highways, intersections, part of it is urban and the other part is rural. It is also
recommended that researchers perform the experiments during the same periods each day
under the same weather conditions so that external factors such as weather, visibility,
and traffic would not affect the data collection process and distort future measurements
and analysis; unless the study is purposely set to analyze the differences of driving in
various weather, visibility and traffic conditions.
FUTURE RESEARCH DIRECTIONS
Based on the authors’comprehension of the available literature, general directions are
presented in this section to help future researchers who are interested in driver profiling
and recognition. Several key points are addressed for future improvements, including
building efficient DAS, establishing reliable and accurate driver profiles, modulating and
evaluating deep learning-based recognition systems that can classify drivers according to
their established profiles.
Phase one: sensors selection, installation and verification for DAS
To collect driving data from real-time experiments, researchers should build their own
DAS. The DAS must be built according to the data that the researchers aim to record.
Different studies record different data for analysis. For example, in a car-following context,
distances between the ego vehicle and the leading vehicle are very important; therefore,
distance sensors, such as lidars or radars, must be used. Also, in lane changing context, the
rotational movement of the vehicle is crucial, therefore, the use of an accelerometer, a
gyroscope, or both (IMU) is essential. For studies that aim to capture overall driver
behaviour, multiple sensors should be used to record the speed, distance, and steerings,
such as OBDII, lidar or radar, and gyroscope or IMU. The choice of which sensors to
assemble in the DAS can be affected by factors, such as cost and performance. For example,
some researchers prefer to use smartphone sensors and GPS to record driving data;
however, smartphone sensors usually require high-performance computational
capabilities (Khandakar et al., 2019) and have overestimation issues (Joubert, Beer &
Koker, 2016) and GPS has similar issues. Also, 3D lidars are considered highly accurate, but
they are considerably costly (Zhao et al., 2016). A substantial cost could be required for
constructing and developing DASs, especially when the study aims to collect naturalistic
driving data from multiple vehicles. Thus, building a reliable, low-cost DAS is a
tremendous step towards research advancements. Hence, certain specifications and
constraints must be set to achieve high-quality results, including:
Proposed DAS should be safe to install with no exposed wiring carrying electrical
currents that might harm the drivers.
Proposed DAS should be easy to install inside the vehicle. It should be as non-visible as
possible, to ensure that it does not influence the naturalistic driving behaviour of drivers.
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 38/50
Each sensor in the proposed DAS must be verified by comparing its readings with the
readings of a secondary sensor. This process is important to ensure that the main
sensors of the proposed DAS have no manufacturing defects. For instance, to validate
the OBDII, a second sensor such as GPS is set to record speed data, and then the
collected speed data of the OBDII and GPS are compared to each other. Even though
GPS is not as accurate as an OBDII, the simultaneous increase and decrease of speed
data in both devices proves that the OBDII is operating correctly and has no
manufacturing defects. The same technique should be used to verify all sensors in the
proposed DAS.
The proposed DAS should be adaptable for integration with other sensors and gadgets in
the future.
Phase two: data collection
To appropriately set the data collection phase, several factors must be identified:
Most studies in the literature utilize a limited sample size between 1 and 20, the authors
recommend using a sample of 30 drivers or above, to ensure the collected data is not
‘sample size dependent’and therefore results are not biased and can be generalized.
Participating drivers should be of age between 20 and 60 years and are divided into four
groups, distributed as follows: group A includes drivers within the age range of 20–30
years old, group B includes drivers within the age range of 31–40 years old, group C
includes drivers within the age range of 41–50 years old and group D includes
drivers within the age range of 51–60 years old. Such grouping would be useful in
providing an in-depth analysis of the effect of age on driving performance (young versus
older drivers).
Female drivers are very underrepresented in the current literature, the authors
recommend using a sample size of 50% men and 50% women. Results from such settings
can provide valuable insights on differences between men’s driver behaviour and
women’s driver behaviour (male versus female drivers).
Participating drivers must carry a driving license covering a minimum of one-year
validity, this is to ensure that participating drivers have the required driving knowledge
to perform experiments with minimal risk.
Experiments carried on routes containing multiple geometric locations, such as
highways and intersections, are preferable. Such data can be used to provide valuable
information on how drivers’behaviour changes with relation to different road types.
Experiments should be carried out on the same periods under similar weather
conditions; this is to ensure that all participating drivers drive under the same
conditions, therefore ensuring consistency in data collection and eliminating external
factors that could skew the future analysis. Unless the study is purposely set to model
driving in various weather, visibility, or traffic conditions.
A recognition system that adapts current deep learning algorithms is presented in phase
4. Neural networks generally require large datasets for training; therefore, the authors
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 39/50
suggest that experiments’duration for each participating driver would be a minimum
of 20 minutes, to ensure enough data is recorded for the training of the recognition
system. Therefore, the route’s length should be planned accordingly by researchers.
Phase three: driver profiling
After data collection, it is recommended to label drivers according to their safe/aggressive
behaviour, this is called driver profiling. Through this labeling process, the driver
behaviour aggressive scale is constructed, which is a scale on which drivers are classified
into different levels of behaviours. Too often, researchers label drivers according to
questionnaires filled by the drivers themselves or by relying on existing profiles from
previously published articles. However, such techniques are not advisable, because asking
the drivers if they are aggressive can be biased and subjective Lee & Jang (2017) and
Qi et al. (2015). Also, most previously published articles either rely on questionnaires as
well or used similar biased labeling techniques, such as observations and crash reports.
Furthermore, what is considered “aggressive behaviour”in one state or country could be
different in another country. For instance, collecting NDS data from Malaysian drivers
in Kuala Lumpur and then label them according to publications from the US presents
many issues; as traffic laws and regulations are different in these two countries, and
what is considered as “aggressive behaviour”in Malaysia could be different from the US.
Therefore, researchers should aim to profile drivers based on the traffic laws and
regulations of their respective countries, to avoid labeling drivers as safe when they are
aggressive in the eyes of the law and vice versa. The authors recommend the labeling
process be done by experts who have considerable knowledge of the laws and traffic
regulations in the country in which the experiments are conducted. Such a technique
would establish driver profiles associated with the country/state traffic laws and
regulations. Very few studies aim to do this technique. For example, in reference (Zylius,
2017), in which Lithuanian experts label the drivers into two categories (aggressive and
safe). Another example is in reference (Gündüz et al., 2017), which categorizes
aggressiveness into three groups, low risk, medium risk, and high risk, based on legal
authority reports in Turkey. Also, in another study in Turkey, in reference (Yuksel &
Atmaca, 2020), where trafficofficers evaluate risky driving behaviours, the risky
behaviours are given a score between 1 and 10. Also, in reference (Lee & Jang, 2017), which
categorizes drivers into three levels of aggressiveness (high, medium, low) according to
the Korean roadway operation guidelines. Moreover, in reference (Silva & Eugenio
Naranjo, 2020), where experts from Spain evaluate driving tests by categorizing each driver
as calm, normal, or aggressive. Furthermore, in reference (Li et al., 2015), where risk
consulting experts in Japan assign aggressiveness scores, and through these different scores
(from 1 to 5, where 1 being least aggressive and 5 being the most aggressive), the driver
behaviour aggressive scale is constructed. The authors recommend that future researchers
follow experts’guidance and directions when labeling and profiling drivers. Table 5
summarizes the aforementioned studies.
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 40/50
Phase four: deep learning-based recognition system
In previous phases, the researchers have developed DAS, collected experimental data from
participating drivers, and profiled the drivers. This phase aims to develop and modulate a
driver behaviour recognition system that would classify drivers, according to the driver
behaviour aggressive scale, using deep learning methods. The reason for using this
approach instead of other approaches (like rule-based approaches) is because deep
learning is an alternative approach that can help address some of the issues with traditional
methods. Rather than attempting to fully emulate the decision process of the experts,
deep learning algorithms typically only take the outcomes from the experts. For example,
the expert may review several driving scenarios and decide which are aggressive and
which are not. Exactly how the expert arrives at his decision is not important for the deep
learning algorithm, only what his decision is. Focusing on the outcomes rather than the
entire decision-making process can make deep learning methods more flexible and less
susceptible to some of the problems encountered with rules-based systems. The authors
recommend using deep learning-based algorithms, instead of machine learning
algorithms, because they provide deeper analysis, and structure algorithms in layers, to
create a neural network that can learn and make intelligent decisions on its own. Moreover,
it is more efficient when the resultant dataset is huge, which can be challenging to train and
model using traditional machine learning algorithms. Before training the classifier, it is
important to use a method that can select or combine variables into features, to effectively
reduce the amount of data that must be processed, while still accurately describing the
original dataset. This is called data cleansing and feature extraction, and it is important
because, during experiments, sensors may record data that are irrelevant to the research.
For example, a DAS built to record steering data from an IMU sensor and speed data from
an OBDII sensor may also record information related to engine and air temperature,
which are most likely irrelevant to the research. This is why this phase is important,
especially if DAS contains multiple sensors, as such irrelevant data would get immense and
subsequently affect the performance of the recognition system. After data cleansing and
feature extraction, researchers should train the recognition system and evaluate its
performance. Various classifiers should be used during the development of the recognition
system, such as convolutional neural networks (CNN), deep neural networks (DNN),
artificial neural networks (ANN) and recurrent neural network (RNN). Part of the dataset
Table 5 Studies in which experts profilied drivers.
Reference Year Levels in the driver behaviour aggressive scale How labelling is done
Zylius (2017) 2017 2 levels (aggressive, safe) Labelling done by experts from Lithuania
Gündüz et al. (2017) 2017 3 levels (low, medium, high) Labelling done by experts from Turkey
Yuksel & Atmaca
(2020)
2020 1–10 risk level score (where 1 is very low risk and
10 is very high risk)
Risk levels were evaluated in accordance with the expert opinions
of trafficofficers in Turkey
Silva & Eugenio
Naranjo (2020)
2020 3 levels (calm, normal, aggressive) Evaluation and categorization of drivers was done by experts in
Spain
Lee & Jang (2017) 2017 3 levels (low, medium, high) Labelling was based on Korean Roadway Operation Guidelines
Li et al. (2015) 2015 1 to 5 (1 is least aggressive and 5 is most) Labelling done by experts from Japan
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 41/50
is used to train those classifiers. Various performance metrics determine which deep-
leaning method is to be recommended for the recognition system. Those performance
metrics are discussed further on the next phase.
Phase five: evaluation
In the last phase, researchers should aim to evaluate the performance of their proposed
recognition system using a confusion matrix. Since drivers are profiled in phase 3,
researchers can use part of the dataset to train the classifiers and measure their
performance according to how they accurately profile the drivers in the untrained dataset.
Researchers can use performance metrics, listed in Table 6, as the basis for evaluating the
performance of the deep learning classifiers of the recognition system.
CONCLUSIONS
This systematic review is beneficial to the research community and is a major step in
summarizing the literature with regards to sensor-based driver behaviour domain.
Research efforts in this area are still ongoing and this survey aims to contribute to its
understanding by reviewing its current research efforts. Four databases are selected for
this purpose and articles are filtered according to a set of eligibility criteria. A taxonomy of
the current literature is presented to facilitate the analysis and categorization of filtered
articles. Discussions on the motivations that drive researchers into this domain are stated,
common issues and challenges are highlighted, and recommendations are provided.
Substantial analysis of the filtered articles is presented to fill certain gaps in the literature.
Furthermore, possible research directions, based on the authors’understanding of the
literature, are presented for future improvements. Those directions include guidelines for
building an efficient DAS, suggestions on how to profile drivers according to their
aggressive/safe behaviours, and the proposition of a deep learning-based recognition
system that would classify drivers according to their established profiles.
For future work, the proposed five-phase methodology, in the future directions section,
will be implemented. The design of the proposed DAS and the data collection process
results will be made available for other researchers. Comparisons between various groups
Table 6 Performance metrics.
Metric Definition Equation
Accuracy The ratio of correctly predicted observation to the total observations (True Positives (TP) + True Negatives (TN))/
(Positives+ Negatives)
Recall (sensitivity) The ratio of correctly predicted positive observations to the all
observations in actual class
TP/(TP + False Negatives (FN))
Precision The ratio of correctly predicted positive observations to the total predicted
positive observations
TP/(TP+ False Positive (FP))
f-measure The weighted average of Precision and Recall 2 × (Recall × Precision)/(Recall + Precision)
False Positive Rate
(FPR)
The proportion of samples that test positive which are genuinely negative FP/(FP+TN)
False Negative Rate
(FNR)
The proportion of samples that test negative which are genuinely positive FP/(FP+TN)
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 42/50
will be presented as well, such as comparisons between female drivers and male drivers,
between young drivers and old drivers, between driving on weekdays and weekends, and
between driving in normal traffic and driving in Covid-19 traffic restrictions. The
performance of a deep learning-based recognition system that classifies drivers according
to their behaviour will be presented and discussed.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
This work was supported by Fundamental Research Grant Scheme (FRGS) grant number
FP114-2018A from the Ministry of Higher Education, Malaysia. The funders had no role
in study design, data collection and analysis, decision to publish, or preparation of the
manuscript.
Grant Disclosures
The following grant information was disclosed by the authors:
Fundamental Research Grant Scheme (FRGS): FP114-2018A.
Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Ward Ahmed Al-Hussein conceived and designed the experiments, performed the
experiments, analyzed the data, prepared figures and/or tables, authored or reviewed
drafts of the paper, and approved the final draft.
Miss Laiha Mat Kiah analyzed the data, authored or reviewed drafts of the paper, and
approved the final draft.
Por Lip Yee analyzed the data, authored or reviewed drafts of the paper, and approved
the final draft.
B. B. Zaidan conceived and designed the experiments, performed the experiments,
authored or reviewed drafts of the paper, and approved the final draft.
Data Availability
The following information was supplied regarding data availability:
This article is a Systematic Literature Review without raw data or code.
REFERENCES
Alkinani MH, Khan WZ, Arshad Q. 2020. Detecting human driver inattentive and aggressive
driving behavior using deep learning: recent advances, requirements and open challenges. IEEE
Access 8:105008–105030 DOI 10.1109/ACCESS.2020.2999829.
Alsrehin O, Klaib AF, Magableh A. 2019. Intelligent transportation and control systems
using data mining and machine learning techniques: a comprehensive study. IEEE Access
7:49830–49857 DOI 10.1109/ACCESS.2019.2909114.
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 43/50
Andria G, Attivissimo F, Di Nisio AD, Lanzolla A, Pellegrino A. 2016. Development of
an automotive data acquisition platform for analysis of driving behavior. Measurement
93(6):278–287 DOI 10.1016/j.measurement.2016.07.035.
Asaithambi G, Kanagaraj V, Toledo T. 2016. Driving behaviors: models and challenges for
non-lane based mixed traffic. Transportation in Developing Economies 2(2):19
DOI 10.1007/s40890-016-0025-6.
BabićD, FiolićM, BabićD, Gates T. 2020. Road markings and their impact on driver behaviour
and road safety: a systematic review of current findings. Journal of Advanced Transportation
2020(1):1–19 DOI 10.1155/2020/7843743.
Bastos JT, Santos PABD, Amancio EC, Gadda TMC, Ramalho JA, King MJ,
Oviedo-Trespalacios O. 2020. Naturalistic driving study in Brazil: an analysis of mobile phone
use behavior while driving. International Journal of Environmental Research and Public Health
17(17):6412.
Bella F, Nobili F. 2020. Driver-pedestrian interaction under legal and illegal pedestrian crossings.
Transportation Research Procedia 45(4):451–458 DOI 10.1016/j.trpro.2020.03.038.
Bender A, Agamennoni G, Ward JR, Worrall S, Nebot EM. 2015. An unsupervised approach for
inferring driver behavior from naturalistic driving data. IEEE Transactions on Intelligent
Transportation Systems 16(6):3325–3336 DOI 10.1109/TITS.2015.2449837.
Bichicchi A, Belaroussi R, Simone A, Vignali V, Lantieri C, Li X. 2020. Analysis of road-user
interaction by extraction of driver behavior features using deep learning. IEEE Access
8:19638–19645 DOI 10.1109/ACCESS.2020.2965940.
Bifulco G, Galante F, Pariota L, Spena MR, Del Gais P. 2014. Data collection for traffic and
drivers’behaviour studies: a large-scale survey. Procedia—Social and Behavioral Sciences
111(1):721–730 DOI 10.1016/j.sbspro.2014.01.106.
Bıçaksız P, Özkan T. 2016. Impulsivity and driver behaviors, offences and accident involvement:
a systematic review. Transportation Research Part F: Traffic Psychology and Behaviour
38(1):194–223 DOI 10.1016/j.trf.2015.06.001.
Butakov VA, Ioannou PA. 2014. Driver/vehicle response diagnostic system for the vehicle-
following case. IEEE Transactions on Intelligent Transportation Systems 15(5):1947–1957
DOI 10.1109/TITS.2014.2305735.
Büyükyildiz G, Pion O, Hildebrandt C, Sedlmayr M, Henze R, Küçükay F. 2017. Identification
of the driving style for the adaptation of assistance systems. International Journal of Vehicle
Autonomous Systems 13(3):244–260 DOI 10.1504/IJVAS.2017.10004258.
Cai H, Hu Z, Chen Z, Zhu D. 2018. A driving fingerprint map method of driving characteristic
representation for driver identification. IEEE Access 6:71012–71019
DOI 10.1109/ACCESS.2018.2881722.
Carmona J, García F, Martín D, Escalera A, Armingol J. 2015. Data fusion for driver behaviour
analysis. Sensors 15(10):25968–25991 DOI 10.3390/s151025968.
Cerni G, Bassani M. 2017. Naturalistic driving data collection to investigate into the effects of
road geometrics on track behaviour. Transportation Research Part C: Emerging Technologies
77(4):1–15 DOI 10.1016/j.trc.2017.01.012.
Chen R, Kusano KD, Gabler HC. 2015. Driver behavior during overtaking maneuvers from the
100-car naturalistic driving study. Traffic Injury Prevention 16(Suppl. 2):S176–S181
DOI 10.1080/15389588.2015.1057281.
Chen C, Zhao X, Zhang Y, Rong J, Liu X. 2019. A graphical modeling method for individual
driving behavior and its application in driving safety analysis using GPS data. Transportation
Research Part F: Traffic Psychology and Behaviour 63(3):118–134 DOI 10.1016/j.trf.2019.03.017.
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 44/50
Dabbour E. 2015. Design gap acceptance for right-turning vehicles based on vehicle acceleration
capabilities. Transportation Research Record: Journal of the Transportation Research Board
2521(1):12–21 DOI 10.3141/2521-02.
Dahl J, De Campos GR, Olsson C, Fredriksson J. 2018. Collision avoidance: a literature review on
threat-assessment techniques. IEEE Transactions on Intelligent Vehicles 4(1):101–113
DOI 10.1109/TIV.2018.2886682.
Das S, Budhkar A, Maurya AK, Maji A. 2019. Multivariate analysis on dynamic car-following data
of non-lane-based traffic environments. Transportation in Developing Economies 5(2):1–13
DOI 10.1007/s40890-019-0085-5.
Demir B, Demir S, Özkan T. 2016. A contextual model of driving anger: a meta-analysis.
Transportation Research Part F: Traffic Psychology and Behaviour 42(2):332–349
DOI 10.1016/j.trf.2016.09.020.
Derbel O. 2018. Belief and fuzzy theories for driving behavior assessment in case of accident
scenarios. International Journal of Automotive Technology 19(1):167–177
DOI 10.1007/s12239-018-0016-1.
Errampalli M, Mallela SS, Chandra S. 2020. Calibration of car-following model for Indian traffic
conditions. Transportation Research Procedia 48(2):829–839 DOI 10.1016/j.trpro.2020.08.091.
Farah H, Musicant O, Shimshoni Y, Toledo T, Grimberg E, Omer H, Lotan T. 2014. Can
providing feedback on driving behavior and training on parental vigilant care affect male teen
drivers and their parents? Accident Analysis & Prevention 69:62–70.
Fleming JM, Allison CK, Yan X, Lot R, Stanton NA. 2019. Adaptive driver modelling in ADAS to
improve user acceptance: a study using naturalistic data. Safety Science 119(3):76–83
DOI 10.1016/j.ssci.2018.08.023.
Foo K. 2015. Effects of familial climate on the adolescents’driving habits: a recent literature.
International Journal of Injury Control and Safety Promotion 22(2):127–135
DOI 10.1080/17457300.2013.855795.
Fridman L, Brown DE, Angell W, AbdićI, Reimer B, Noh HY. 2016. Automated
synchronization of driving data using vibration and steering events. Pattern Recognition Letters
75(1):9–15 DOI 10.1016/j.patrec.2016.02.011.
Gadepally V, Krishnamurthy A, Ozguner U. 2013. A framework for estimating driver decisions
near intersections. IEEE Transactions on Intelligent Transportation Systems 15(2):637–646
DOI 10.1109/TITS.2013.2285159.
Galarza M, Paradells J. 2018. Improving road safety and user experience by employing dynamic
in-vehicle information systems. IET Intelligent Transport Systems 13(4):738–744
DOI 10.1049/iet-its.2018.5022.
Gao J, Murphey YL, Zhu H. 2018. Detection of lane-changing behavior using collaborative
representation classifier-based sensor fusion. SAE International Journal of Transportation Safety
6(2):147–162 DOI 10.4271/09-06-02-0010.
Gao J, Murphey YL, Zhu H. 2019. Personalized detection of lane changing behavior using
multisensor data fusion. Computing 101(12):1837–1860 DOI 10.1007/s00607-019-00712-9.
González ABR, Wilby MR, Díaz JJV, Ávila CS. 2014. Modeling and detecting aggressiveness from
driving signals. IEEE Transactions on Intelligent Transportation Systems 15(4):1419–1428
DOI 10.1109/TITS.2013.2297057.
Guo F. 2019. Statistical methods for naturalistic driving studies. Annual Review of Statistics and Its
Application 6(1):309–328 DOI 10.1146/annurev-statistics-030718-105153.
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 45/50
Gündüz G, Yaman Ç, Peker AU, Acarman T. 2017. Prediction of risk generated by different
driving patterns and their conflict redistribution. IEEE Transactions on Intelligent Vehicles
3(1):71–80.
Hassan N, Zamzuri H, Wahid N, Zulkepli KA, Azmi MZ. 2017. Driver’s steering behaviour
identification and modelling in near rear-end collision. Telkomnika 15(2):861.
Heesen M, Dziennus M, Hesse T, Schieben A, Brunken C, Löper C, Kelsch J, Baumann M. 2014.
Interaction design of automatic steering for collision avoidance: challenges and potentials of
driver decoupling. IET Intelligent Transport Systems 9(1):95–104.
Hildebrandt C, Schmidt M, Sedlmayr M, Pion O, Büyükyildiz G, Küçükay F. 2015. Intuitive
steering assistance in critical understeer situations. Traffic Injury Prevention 16(5):484–490
DOI 10.1080/15389588.2014.969805.
Hill C, Elefteriadou L, Kondyli A. 2014. Exploratory analysis of lane changing on freeways based
on driver behavior. Journal of Transportation Engineering 141(4):4014090.
Hong S, Lu J, Panigrahi SR, Scott J, Filev DP. 2016a. An interacting multiple-model-based
algorithm for driver behavior characterization using handling risk. IEEE Transactions on
Intelligent Transportation Systems 20(12):4308–4317 DOI 10.1109/TITS.2016.2633254.
Hong S, Min B, Doi S, Suzuki K. 2016b. Approaching and stopping behaviors to the intersections
of aged drivers compared with young drivers. International Journal of Industrial Ergonomics
54(4):32–41 DOI 10.1016/j.ergon.2015.12.002.
Jermakian JS, Bao S, Buonarosa ML, Sayer JR, Farmer CM. 2017. Effects of an integrated
collision warning system on teenage driver behavior. Journal of Safety Research 61(5):65–75
DOI 10.1016/j.jsr.2017.02.013.
Joubert JW, Beer DD, Koker ND. 2016. Combining accelerometer data and contextual variables
to evaluate the risk of driver behaviour. Transportation Research Part F: Traffic Psychology and
Behaviour 41(3):80–96 DOI 10.1016/j.trf.2016.06.006.
Katzourakis DI, Abbink DA, Velenis E, Holweg E, Happee R. 2013. Driver’s arms’time-variant
neuromuscular admittance during real car test-track driving. IEEE Transactions on
Instrumentation and Measurement 63(1):221–230 DOI 10.1109/TIM.2013.2277610.
Khandakar A, Chowdhury MEH, Ahmed R, Dhib A, Mohammed M, Al-Emadi NAMA,
Michelson D. 2019. Portable system for monitoring and controlling driver behavior and the use
of a mobile phone while driving. Sensors 19(7):1536.
Kim B, Baek Y. 2020. Sensor-based extraction approaches of in-vehicle information for driver
behavior analysis. Sensors 20(18):5197.
Koesdwiady A, Soua R, Karray F, Kamel MS. 2016. Recent trends in driver safety
monitoring systems: state of the art and challenges. IEEE Transactions on Vehicular Technology
66(6):4550–4563 DOI 10.1109/TVT.2016.2631604.
Krasniuk S, Classen S, Monahan M, Danter T, He W, Rosehart H, Morrow SA. 2019. A strategic
driving maneuver that predicts on-road outcomes in adults with multiple sclerosis.
Transportation Research Part F: Traffic Psychology and Behaviour 60:147–156.
Lee J, Jang K. 2017. A framework for evaluating aggressive driving behaviors based on in-vehicle
driving records. Transportation Research Part F: Traffic Psychology and Behaviour 65:610–619.
Li A, Jiang H, Zhou J, Zhou X. 2019. Learning human-like trajectory planning on urban two-lane
curved roads from experienced drivers. IEEE Access 7:65828–65838
DOI 10.1109/ACCESS.2019.2918728.
Li Y, Miyajima C, Kitaoka N, Takeda K. 2015. Evaluation method for aggressiveness of driving
behavior using drive recorders. IEEJ Journal of Industry Applications 4(1):59–66
DOI 10.1541/ieejjia.4.59.
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 46/50
Li Z, Zhang K, Chen B, Dong Y, Zhang L. 2018. Driver identification in intelligent vehicle systems
using machine learning algorithms. IET Intelligent Transport Systems 13(1):40–47
DOI 10.1049/iet-its.2017.0254.
Liu Y, Hansen J. 2019. Analysis of driving performance based on driver experience and vehicle
familiarity: a UTDrive/mobile-UTDrive app study. SAE International Journal of Transportation
Safety 7(2):175–190 DOI 10.4271/09-07-02-0010.
Liu H, Li Y, Hong R, Li Z, Li M, Pan W, Glowacz A, He H. 2020. Knowledge graph analysis and
visualization of research trends on driver behavior. Journal of Intelligent & Fuzzy Systems
38(1):495–511.
Liu H, Taniguchi T, Tanaka Y, Takenaka K, Bando T. 2017. Visualization of driving behavior
based on hidden feature extraction by using deep learning. IEEE Transactions on Intelligent
Transportation Systems 18(9):2477–2489 DOI 10.1109/TITS.2017.2649541.
Llorca C, Moreno AT, Garcia A. 2016. Modelling vehicles acceleration during overtaking
manoeuvres. IET Intelligent Transport Systems 10(3):206–215 DOI 10.1049/iet-its.2015.0035.
Lubbe N, Rosén E. 2014. Pedestrian crossing situations: quantification of comfort boundaries to
guide intervention timing. Accident Analysis & Prevention 71(4):261–266
DOI 10.1016/j.aap.2014.05.029.
Lyu N, Deng C, Xie L, Wu C, Duan Z. 2019. Afield operational test in China: exploring the effect
of an advanced driver assistance system on driving performance and braking behavior.
Transportation Research Part F: Traffic Psychology and Behaviour 65(1):730–747
DOI 10.1016/j.trf.2018.01.003.
MaljkovićB, CvitanićD. 2016. Evaluation of design consistency on horizontal curves for two-lane
state roads in terms of vehicle path radius and speed. Baltic Journal of Road and Bridge
Engineering 11(2):127–135.
Martinez M, Heucke M, Wang F-Y, Gao B, Cao D. 2017. Driving style recognition for intelligent
vehicle control and advanced driver assistance: a survey. IEEE Transactions on Intelligent
Transportation Systems 19(3):666–676 DOI 10.1109/TITS.2017.2706978.
Massaro E, Ahn C, Ratti C, Santi P, Stahlmann R, Lamprecht A, Roehder M, Huber M. 2016.
The car as an ambient sensing platform (point of view). Proceedings of the IEEE 105(1):3–7.
McLaurin EJ, Lee JD, McDonald AD, Aksan N, Dawson J, Tippin J, Rizzo M. 2018. Using topic
modeling to develop multi-level descriptions of naturalistic driving data from drivers with
and without sleep apnea. Transportation Research Part F: Traffic Psychology and Behaviour
58:25–38.
Merickel J, High R, Dawson J, Rizzo M. 2019. Real-world risk exposure in older drivers with
cognitive and visual dysfunction. Traffic Injury Prevention 20(Suppl. 2):S110–S115
DOI 10.1080/15389588.2019.1688794.
Montella A, Pariota L, Galante F, Imbriani LL, Mauriello F. 2014. Prediction of drivers’speed
behavior on rural motorways based on an instrumented vehicle study. Transportation Research
Record 2434(1):52–62 DOI 10.3141/2434-07.
Mukhtar A, Xia L, Tang TB. 2015. Vehicle detection techniques for collision avoidance systems: a
review. IEEE Transactions on Intelligent Transportation Systems 16(5):2318–2338
DOI 10.1109/TITS.2015.2409109.
Munigety R, Mathew TV. 2016. Towards behavioral modeling of drivers in mixed traffic
conditions. Transportation in Developing Economies 2(1):6 DOI 10.1007/s40890-016-0012-y.
Nguyen H, Kieu L-M, Wen T, Cai C. 2018. Deep learning methods in transportation domain: a
review. IET Intelligent Transport Systems 12(9):998–1004 DOI 10.1049/iet-its.2018.0064.
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 47/50
Oviedo-Trespalacios O, Truelove V, Watson B, Hinton JA. 2019. The impact of road advertising
signs on driver behaviour and implications for road safety: a critical systematic review.
Transportation Research Part A: Policy and Practice 122(3):85–98
DOI 10.1016/j.tra.2019.01.012.
Pamuła T. 2016. Neural networks in transportation research-recent applications. Transport
Problems 11(2):27–36 DOI 10.20858/tp.2016.11.2.3.
Park S, Park J, Seo B, Kim B. 2016. Development of reproducible test vehicle and evaluation
scenario for driver pedal behavior analysis in unexpected emergency situations. International
Journal of Control and Automation 9(11):385–396 DOI 10.14257/ijca.2016.9.11.33.
Peng L, Sotelo MA, He Y, Ai Y, Li Z. 2019. Rough set based method for vehicle collision risk
assessment through inferring driver’s braking actions in near-crash situations. IEEE Intelligent
Transportation Systems Magazine 11(2):54–69 DOI 10.1109/MITS.2019.2903539.
Qi G, Du Y, Wu J, Xu M. 2015. Leveraging longitudinal driving behaviour data with data mining
techniques for driving style analysis. IET Intelligent Transport Systems 9(8):792–801
DOI 10.1049/iet-its.2014.0139.
Sagberg F, Selpi, Bianchi Piccinini GF, Engström J. 2015. A review of research on driving styles
and road safety. Human Factors: The Journal of the Human Factors and Ergonomics Society
57(7):1248–1275.
Saifuzzaman M, Zheng Z. 2014. Incorporating human-factors in car-following models: a review of
recent developments and research needs. Transportation Research Part C: Emerging
Technologies 48(8):379–403 DOI 10.1016/j.trc.2014.09.008.
Salmon PM, Goode N, Spiertz A, Thomas M, Grant E, Clacy A. 2017. Is it really good to talk?
Testing the impact of providing concurrent verbal protocols on driving performance.
Ergonomics 60(6):770–779 DOI 10.1080/00140139.2016.1214752.
Satzoda RK, Trivedi MM. 2014. Drive analysis using vehicle dynamics and vision-based lane
semantics. IEEE Transactions on Intelligent Transportation Systems 16(1):9–18
DOI 10.1109/TITS.2014.2331259.
Schakel WJ, Gorter CM, De Winter JC, Van Arem B. 2017. Driving characteristics and adaptive
cruise control? A naturalistic driving study. IEEE Intelligent Transportation Systems Magazine
9(2):17–24 DOI 10.1109/MITS.2017.2666582.
Schorr J, Hamdar SH, Silverstein C. 2014. Measuring the safety impact of road infrastructure
systems on driver behavior: vehicle instrumentation and exploratory analysis. Journal of
Intelligent Transportation Systems 21(5):364–374.
Shinar D, Gurion B. 2019. Crash causes, countermeasures, and safety policy implications. Accident
Analysis & Prevention 125(4):224–231 DOI 10.1016/j.aap.2019.02.015.
Shirazi MS, Morris BT. 2016. Looking at intersections: a survey of intersection monitoring,
behavior and safety analysis of recent studies. IEEE Transactions on Intelligent Transportation
Systems 18(1):4–24 DOI 10.1109/TITS.2016.2568920.
Silva I, Eugenio Naranjo J. 2020. A systematic methodology to evaluate prediction models for
driving style classification. Sensors 20(6):1692 DOI 10.3390/s20061692.
Silva AB, Santos S, Vasconcelos L, Seco Á, Silva JP. 2014. Driver behavior characterization in
roundabout crossings. Transportation Research Procedia 3:80–89
DOI 10.1016/j.trpro.2014.10.093.
Stephens AN, Young KL, Logan DB, Lenné MG. 2015. The need for speed? The relationships
between driver traits and speed choices during a naturalistic drive. Procedia Manufacturing
3(5):3200–3207 DOI 10.1016/j.promfg.2015.07.870.
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 48/50
Stigson H, Hagberg J, Kullgren A, Krafft M. 2014. A one year pay-as-you-speed trial with
economic incentives for not speeding. Traffic Injury Prevention 15(6):612–618
DOI 10.1080/15389588.2013.850678.
Svetina M. 2016. The reaction times of drivers aged 20 to 80 during a divided attention driving.
Traffic Injury Prevention 17(8):810–814 DOI 10.1080/15389588.2016.1157590.
Taniguchi T, Nagasaka S, Hitomi K, Takenaka K, Bando T. 2014. Unsupervised hierarchical
modeling of driving behavior and prediction of contextual changing points. IEEE Transactions
on Intelligent Transportation Systems 16(4):1746–1760 DOI 10.1109/TITS.2014.2376525.
Terzi R, Sagiroglu S, Demirezen MU. 2018. Big data perspective for driver/driving behavior. IEEE
Intelligent Transportation Systems Magazine 12(2):20–35.
Tonguç G, Akçay İH, Gürbüz H. 2018. Improvement of the visual warning system for various
driving and road conditions in road transportation. Proceedings of the Institution of Mechanical
Engineers, Part D: Journal of Automobile Engineering 232(2):175–187.
Turner B, Woolley J, Cairney P. 2015. An analysis of driver behaviour through rural curves:
exploratory results on driver speed. Journal of the Australasian College of Road Safety 26(4):31.
Van Haperen W, Riaz MS, Daniels S, Saunier N, Brijs T, Wets G. 2019. Observing the
observation of (vulnerable) road user behaviour and traffic safety: a scoping review. Accident
Analysis & Prevention 123(1):211–221 DOI 10.1016/j.aap.2018.11.021.
Van Treese JW II, Koeser AK, Fitzpatrick GE, Olexa MT, Allen EJ. 2017. A review of the impact
of roadway vegetation on drivers’health and well-being and the risks associated with single-
vehicle crashes. Arboricultural Journal 39(3):179–193 DOI 10.1080/03071375.2017.1374591.
Varotto SF, Farah H, Toledo T, Van Arem B, Hoogendoorn SP. 2017. Resuming manual control
or not?: modeling choices of control transitions in full-range adaptive cruise control.
Transportation Research Record 2622(1):38–47 DOI 10.3141/2622-04.
Varotto SF, Farah H, Toledo T, Van Arem B, Hoogendoorn SP. 2018. Modelling decisions of
control transitions and target speed regulations in full-range Adaptive Cruise control based on
risk allostasis theory. Transportation Research Part B: Methodological 117:318–341
DOI 10.1016/j.trb.2018.09.007.
Wang C, Sun Q, Guo Y, Fu R, Yuan W. 2019. Improving the user acceptability of advanced driver
assistance systems based on different driving styles: a case study of lane change warning systems.
IEEE Transactions on Intelligent Transportation Systems 21(10):4196–4208
DOI 10.1109/TITS.2019.2939188.
Wang J, Wu J, Zheng X, Ni D, Li K. 2016. Driving safety field theory modeling and its application
in pre-collision warning system. Transportation Research Part C: Emerging Technologies
72(1):306–324 DOI 10.1016/j.trc.2016.10.003.
Wang J, Xiong C, Lu M, Li K. 2014. Longitudinal driving behaviour on different roadway
categories: an instrumented-vehicle experiment, data collection and case study in China. IET
Intelligent Transport Systems 9(5):555–563 DOI 10.1049/iet-its.2014.0157.
Wu B-F, Chen Y-H, Yeh C-H. 2013. Driving behaviour-based event data recorder. IET Intelligent
Transport Systems 8(4):361–367 DOI 10.1049/iet-its.2013.0009.
Wu K-F, Jovanis PP. 2016. Cohort-based analysis structure for modeling driver behavior with an
in-vehicle data recorder. Transportation Research Record 2601(1):24–32 DOI 10.3141/2601-04.
Xiao Z, Li F, Wu R, Jiang H, Hu Y, Ren J, Cai C, Iyengar A. 2020. TrajData: on vehicle trajectory
collection with commodity plug-and-play OBU devices. IEEE Internet of Things Journal
7(9):9066–9079.
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 49/50
Xie G, Gao H, Huang B, Qian L, Wang J. 2018. A driving behavior awareness model based on a
dynamic Bayesian network and distributed genetic algorithm. International Journal of
Computational Intelligence Systems 11(1):469–482 DOI 10.2991/ijcis.11.1.35.
Yao W, Zeng Q, Lin Y, Xu D, Zhao H, Guillemard F, Geronimi S, Aioun F. 2016. On-road
vehicle trajectory collection and scene-based lane change analysis: part II. IEEE Transactions on
Intelligent Transportation Systems 18(1):206–220.
Yao Y, Zhao X, Wu Y, Zhang Y, Rong J. 2020. Clustering driver behavior using dynamic time
warping and hidden Markov model. Journal of Intelligent Transportation Systems 25(3):1–14.
Yi D, Su J, Liu C, Chen W-H. 2018. Trajectory clustering aided personalized driver intention
prediction for intelligent vehicles. IEEE Transactions on Industrial Informatics 15(6):3693–3702.
Yuksel A, Atmaca S. 2020. Driver’s black box: a system for driver risk assessment using machine
learning and fuzzy logic. Journal of Intelligent Transportation Systems 3(9):1–48
DOI 10.1080/15472450.2020.1852083.
Zardosht M, Beauchemin S, Bauer M. 2018. Identifying driver behavior in preturning maneuvers
using in-vehicle CANbus signals. Journal of Advanced Transportation 2018(4):1–10
DOI 10.1155/2018/5020648.
Zhang X, Qu X, Tao D, Xue H. 2019. The association between sensation seeking and
driving outcomes: a systematic review and meta-analysis. Accident Analysis & Prevention
123(7):222–234 DOI 10.1016/j.aap.2018.11.023.
Zhao H, Wang C, Lin Y, Guillemard F, Geronimi S, Aioun F. 2016. On-road vehicle trajectory
collection and scene-based lane change analysis: part I. IEEE Transactions on Intelligent
Transportation Systems 18(1):192–205 DOI 10.1109/TITS.2016.2571726.
Zhao Z, Zhou L, Zhu Q, Luo Y, Li K. 2017. A review of essential technologies for collision
avoidance assistance systems. Advances in Mechanical Engineering 9(10):1687814017725246
DOI 10.1177/1687814017725246.
Zheng Y, Chase RT, Elefteriadou L, Sisiopiku V, Schroeder B. 2017. Driver types and
their behaviors within a high level of pedestrian activity environment. Transportation Letters
9(1):1–11 DOI 10.1080/19427867.2015.1131943.
Ziakopoulos A, Tselentis D, Kontaxi A, Yannis G. 2020. A critical overview of driver recording
tools. Journal of Safety Research 72(1):203–212 DOI 10.1016/j.jsr.2019.12.021.
Zylius G. 2017. Investigation of route-independent aggressive and safe driving features obtained
from accelerometer signals. IEEE Intelligent Transportation Systems Magazine 9(2):103–113
DOI 10.1109/MITS.2017.2666583.
Zyner A, Worrall S, Nebot E. 2019. Naturalistic driver intention and path prediction
using recurrent neural networks. IEEE Transactions on Intelligent Transportation Systems
21(4):1584–1594 DOI 10.1109/TITS.2019.2913166.
Ahmed Al-Hussein et al. (2021), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.632 50/50
