# Context-Aware Quantitative Risk Assessment Machine Learning Model for
Drivers Distraction

Adebamigbe Fasanmade<EMAIL_ADDRESS>Ali H. Al-Bayatti
<EMAIL_ADDRESS>Jarrad Neil Morden<EMAIL_ADDRESS>Fabio
Caraffini<EMAIL_ADDRESS>
###### Abstract

Risk mitigation techniques are critical to avoiding accidents associated with
driving behaviour. We provide a novel Multi-Class Driver Distraction Risk
Assessment (MDDRA) model that considers the vehicle, driver, and environmental
data during a journey. MDDRA categorises the driver on a risk matrix as safe,
careless, or dangerous. It offers flexibility in adjusting the parameters and
weights to consider each event on a specific severity level. We collect real-
world data using the Field Operation Test (TeleFOT), covering drivers using
the same routes in the East Midlands, United Kingdom (UK). The results show
that reducing road accidents caused by driver distraction is possible. We also
study the correlation between distraction (driver, vehicle, and environment)
and the classification severity based on a continuous distraction severity
score. Furthermore, we apply machine learning techniques to classify and
predict driver distraction according to severity levels to aid the transition
of control from the driver to the vehicle (vehicle takeover) when a situation
is deemed risky. The Ensemble Bagged Trees algorithm performed best, with an
accuracy of 96.2%.

###### keywords:

Risk assessment , Decision support , Driver behaviour , Severity rating ,
Driver distraction

††journal: arXiv

[inst1]organization=School of Computer Science and Informatics, De Montfort
University, addressline=Tthe Gateway, city=Leicester, postcode=LE1 9BH,
state=England, country=UK

[inst2]organization=Department of Computer Science, Swansea University,
addressline=Bay Campus, Computational Foundry, Fabian Way, city=Swansea,
postcode=SA1 8EN, state=Wales, country=UK

##  1 Introduction

Since the emergence of new vehicle technologies, distracted drivers have
become the main problem in road accidents. Meanwhile, intelligent
transportation systems are getting closer to allowing vehicles to take control
to a semiautonomous level 4 (or the level approved by the authorities) out of
either necessity or driver choice. Thus, drivers may become more dependent on
having the vehicle perform in-vehicle tasks, meaning they will become more
relaxed and distracted, thus opening up many risks. In light of this, relevant
contextual information (including vehicle performance and environmental
conditions, which directly affect driver safety) can be used to help the
driver cope with many situations. This implies the need for an Advanced
Driving Assistance System (ADAS) to mitigate risks before an accident occurs
by providing a qualitative- and quantitative-based risk assessment.

The European Commission for Mobility and Road Transport Safety highlights that
a significant proportion of road accidents occur when the driver is
distracted, with common distractions encompassing handheld mobile devices,
using the radio, eating, talking to passengers, smoking, and glancing at in-
vehicle navigation systems [1]. According to [2], in-vehicle interfaces can
also overload the driver. Additionally, fatigued drivers pose a significant
risk on the road. In recent years, driver’s eyes have become an efficient
metric for measuring driver distraction, and driver’s ability to keep their
eyes on the road is crucial. A statistical analysis by the Department of
Transport shows that of the $1,456$ fatal car accidents, $383$ involved
careless tendencies of pedestrians, while 110 were the result of reduced
attention of drivers on the road [3]. Inexperienced drivers are another
significant factor that has caused the number of road accidents to increase.
Young and inexperienced drivers are particularly at risk, unlike skilled
drivers, who adjust their driving strategy in time and predict different
driving scenarios [4]. In comparison, the higher incidence of accidents among
young drivers is attributed to low cognitive ability [5] and a loss of
attention due to distraction [6]. Consequently, there is a strong need for
driver risk assessment [7] which can provide an easy control change to
automatic driving, especially in cases where the driver is intoxicated,
unconscious, or not available in the situation. Although risk mitigation is
tricky and difficult to model as official accident reports are relatively
undetermined due to the possibility of numerous definitions of distractions or
a country simply not collecting the data [8]. Additionally, driver distraction
can be influenced by the situation in which driving occurs. Therefore, there
is a significant gap in the available mechanism that accommodates the context-
aware risk model. The model should be sufficiently concise with the ability of
intelligent image recognition to detect and form a risk matrix to profile
drivers into distraction classifications. This can reduce the occurrence of an
accident by a significant margin. There is a wide range of literature [9, 10,
11, 12, 13, 14] on the importance and urgency of such driving risk mitigation
techniques to prevent driving behaviour-related accidents and shift control.
For a robust false-proof alert system, a precise classification of driving
behaviour is needed. However, to the best of our knowledge, the current work
lacks complexity, rigidity, synthesised data set, more focus on a particular
side of perspective (vehicle, driver, or environment), false-positive classes,
and low accuracy. Drivers can be classified into three groups, namely safe,
careless, and dangerous drivers. Capturing driver behaviour is crucial to risk
mitigation, and developing context-aware ADAS can influence risk levels and
prevent accidents. Moreover, a real-time novel risk assessment determines
Consequently, the key contributions of this study are:

  * 1.

The development of a definition of an adaptive severity level for driver
distraction.

  * 2.

A frame-by-frame analysis of driver behaviour severity level in an ADAS.

  * 3.

A proposed model to characterise driver behaviour considering contextual
factors such as speed, acceleration, and surrounding vehicles.

  * 4.

Development of the MDDRA model for driving behaviour and its evaluation using
machine learning (ML).

The following sections of the paper are structured accordingly.

  * 1.

Section 2 presents the background of risk assessment related to driver’s
distraction and a detailed literature review on drivers’ distraction.

  * 2.

Section 3 describes the methodology of the MDDRA model, including a rigorous
risk assessment.

  * 3.

4 presents the experiments and procedures, dataset, and technical setup.

  * 4.

The findings and discussions of the experimental procedures are presented in
Section 5.

  * 5.

Finally, Section 6 concludes and suggests future work.

##  2 Background

We systematically organise the existing literature in relevant areas.

###  2.1 Risk assessment for driver’sdistraction

Risk assessment can be defined as a process evaluating the adverse effects of
a natural phenomenon, activity, or substance [15]. Berdica stated that risk
constitutes the likelihood and probability of an incident occurring [16, 17].
Relative risk ratio is used to quantify vehicle crashing risks under bad
weather conditions; its calculation requires a large dataset of crashes
arising from adverse weather conditions [18]. However, using a risk matrix,
which combines probability and consequences, overcomes the former method in
popularity [16]. A risk matrix can be used to determine the level of driving
risk. Understandably, most risk indicators related to driver distractions have
been modelled after crash events. However, the main flaw in modelling driving
risk assessment via post-crash data is that it is a reaction strategy rather
than a prevention method. According to Cai et al. [19], drivers’ subjective
assessment of the driving risks - particularly those related to various
weather scenarios - is consistent with collision-based studies. In [19], the
authors assume that the driver’s perceived risks are consistent with the
actual crash statistics, especially for incidences related to rainy
conditions. Various factors can impact driving capability; these can be
extracted from the driving context, i.e., from the driver, vehicle, and
environment, and include the weather, road, speed, manoeuvres, pedestrians,
driver state, and braking. However, there is currently a lack of adequate data
and facilities to ensure the development and implementation of an efficient
and robust risk assessment model for the driving context. In response to this,
this paper proposes using the Naturalistic Driving Study (NDS) TeleFOT, which
is sufficiently complete for the environment, vehicle, and driver monitoring.
The proposed approach uses the following mathematical model:

$C_{i}=\sum_{j=1}^{J}\left(x\cdot
X_{i,j}\cdot\beta_{j}\right)+\varepsilon_{i},\quad C_{i}=1,2\dots,M$ (1)

where $C_{i}$ denotes a discrete model-dependent variable that represents the
level of a distraction impact on driving. This variable’s various impact
levels include minor impact, overall impact, profound impact, and disastrous
impact. The $i$ included in this variable represents the $i^{th}$ driver with
non-observable $\varepsilon_{i}$ variables, including the volume of traffic,
vehicle type, road type, and rain intensity. A non-observable variable is
selected to fit a logistic distribution for generating a continuous latent
variable $C_{i}$ denoting the influence on driving.

Another proposed approach is the Rank Order Cluster Analysis, which sorts
driving risk $R_{i}$ in ascending order, as indicated by
$R_{1},R_{2},R_{3},\dots,R_{n}$. Consideration of categories $(G)$, including
$R_{i},R_{(}(i+1)),\dots,R_{j}$ and satisfying $j>i$ can be denoted as
$G={i,i+1,\dots,j}$. Consequently, the diameter $D(I,j)$ of $G$ is calculated
from the equation:

$D(i,j)={\sum_{t=1}^{j}{(R_{t}}-R_{G})^{2}},$ (2)

where $R_{G}$ represents the mean driving risk, and the driving hazard is
segmented into $k$ segments expressed as:

$\begin{array}[]{c}G_{1}={i_{1},i_{1},+1,\dots,i_{2}-1},\\\
G_{2}={i_{2},i_{2},+1,\dots,i_{3}-1},\\\ \dots\\\
G_{k}={i_{k},k,+1,\dots,i_{k+1}-1}\end{array}$ (3)

Where the variable $i$ satisfies the following condition:

$I=i_{1}<i_{2},<\dots<i_{k},<i_{k+1}=n+1.$ (4)

There is also a minimal loss function with a recursion relationship
represented by the equation:

$L[b(n,k)]=\sum_{t=1}^{k}D(i_{t},i_{t+1}-1)$ (5)

Where $b(n,k)$ denotes a special function returning a classification method
based on the values of $n$ and $k$. This function appears in the recurrence
relation for the minimal loss function formulated in Equation 6 below,

$L\left[b(n,k)_{k\leq n}\right]=\min_{{k\leq j\leq
n}}\left\\{L[P(j-1,k-1)]+D(j,n)\right\\},$ (6)

where $P(n,k)$ denotes the method to minimise the loss function; if the values
of $n$ and $k$ are given; $P(n-1,k-1)$, with different descriptions depicts
the optimal driving risk categories. However, our proposed model assumes that
driving is a discrete and time-series event; therefore, it takes the risk in
the previous frame to compute the severity level of driving risk in the
current frame. Furthermore, we consider the sequence of occurrence and the
duration of the distracted driving event in computing risk. We address the
limitations, thereby enhancing our proposed model.

###  2.2 Face Orientation

Dong et al. [20] measure driver fatigue based on physiological features,
including facial expression and eye activity, as well as the number of times
that drivers touch their faces. Assuming that when drivers are tired they
exhibit less frequent head motions, it is possible to measure driver fatigue
based on the frequency of face turns during a journey; at this moment, the
starting duration depends on the deduction from a consecutive number of
frames. Hu et al. [21] state that careless driving is a significant cause of
road accidents and tracked driver behaviour using face orientation and facial
features taken from infrared images. However, the study uses only a single
type of driver distraction, which represents a significant limitation.

Sato et al. [22] infer driver distraction and concentration states using
driver body information states. They assessed near misses when drivers
approached an intersection. Time series data on different eye-gaze movements
and face orientations before the collision or near-miss were logged.
Meanwhile, Rasouli et al. [23] analyzed pedestrian behaviour at crossing
points under various weather conditions and road types. Their findings show
the vital significance of the head orientation of pedestrians before their
crossing intention. Pedestrians make an inference about traffic dynamics
(vehicle speed) and crosswalk characteristics (width), and the pedestrian
demographics impact their behaviour after the initial purpose of the crossing
had been displayed. The result showed an interrelation in the contextual
elements, with one factor potentially decreasing or increasing the influence
of other factors.

Fasanmade et al. [24] used the multi-class distraction to classify driver
distraction into severity levels using drivers’ physiological features. The
approach involved the use of an image-processing rule-based fuzzy logic
system. They found that a combination of face orientation and eye glance
increases the degree of driver distraction. Furthermore, the results showed
that driver distraction could transition from careless driving to dangerous
driving when a certain threshold is reached and when multi-class distraction
occurs.

###  2.3 Hand State

Hands are vital in driving tasks, such as steering and changing gears. During
operation, the state of hands is even more critical in a changing context.
Thus, monitoring this state is crucial to the prevention of accidents. Das et
al. [25] describe a naturalistic driving study to detect drivers’ hands using
bounding boxes and hand annotation. The validation involved checking for false
positives that may arise due to illumination conditions, no-hand objects of
similar colour, occlusion, and truncation. For the detection, Aggregate
Channel Features (ACF) were used as the detector, and the hand detector’s
accuracy was measured using precision-recall (PR) to evaluate the parameter
performance. The initial results suffered from missed detections and false
positives, although a cross-dataset comparison yielded better accuracy.

Dong et al. [26] state that fatigued drivers assume more comfortable hand
positions on the steering wheel. However, Carsten and Brookhius [73] noted
that the impact of cognitive distraction on driving performance differs from
that of visual distraction. Specifically, visual distraction adversely impacts
a driver’s steering ability and lateral vehicle control, particularly car
following. Le et al. [27, 28] use a novel multiple-scale region-based fully
convolutional neural network (MSRFCN) for human region detection in
illuminated and low-resolution conditions. They further use a pre-trained
network called the “Oxford” hand dataset and compare it with several hand
detection approaches. The proposed MSFRCN algorithm has an Average Precision
(AP) and Average Recall (AR) of 95.1% and 94.5%. Besides, it brings an
improvement in the AP and AR of 7% and 13%, respectively, to classify both the
left and right hands.

###  2.4 Eye Glances

According to an eye-tracking study by French carmaker Peugeot, during a one-
hour trip in urban traffic, car drivers take their eyes off the road for a
total of about two miles. The study involves several drivers on 25 parallel
six-mile drives utilising special driver-mounted glasses to investigate where
their eyes alighted when operating a range of SUV vehicles. The outcome
results found that drivers had eyes off the road for about 7% of the trip
[29].

Yuan et al. [30] suggest an approach to classify existing driving situations
and forecast off-road vehicle situations using a Hidden Markov Model (HMM).
The experiment is carried out using a driving simulator analysis involving 26
participants in three driving scenarios: rural, urban, and motorway
environments. Three different occlusion durations ($0s$, $1s$, and $2s$) are
added to measure the eyes-off-road period durations. The results reveal that
existing driving situations can be optimally defined using glance position
sequences, with up to 89.3% accuracy. The motorway is the most
distinguishable, with over 90% precision. Moreover, in driver’s eyes-off-road
period estimation, using HMM-based algorithms with two inputs (namely look
duration and look position sequence)s, gives the highest accuracy rate of
92.7%. Vehicles of 42 newly approved adolescent drivers are fitted with
sensors, accelerometers, and Global Positioning Systems (GPS) to collect data
continuously for 18 months period. Crash and near-crash (CNCs) situations were
reported through the investigation of significantly elevated gravitational
force incidents. Analysis of the video assigns 6 seconds previous to each CNC,
and random samples of non-CNC road fragments are coded for the period of eye-
glances off the front road and occurrence of secondary mission participation.
The likelihood (odds ratio) of CNCs due to eye-glance activity is determined
by contrasting the prevalence of secondary task participation and the length
of off-road eyes before CNC with the prevalence and period of off-road looks
non-CNC road segments. The crash incidence improved with the period of the
single-most prolonged glimpse during all secondary tasks (OR$=3.8$ for $>2s$)
and wireless secondary task presence (OR $=5.5$ for $>2s$). The single-most
extended glimpse offers a constant estimation of an accident’s likelihood than
absolute eyes off the forward roadway [31].

###  2.5 Road Type (Urban, Highway)

In [32], Doshi et al. develop an algorithm that includes critical vehicle
data, such as the brake switch status, throttle position, and wheel speed. It
also uses the inputs to calculate several parameters, namely shifts per given
time interval, throttle variations, mean velocity, and acceleration. The
resulting parameters help the algorithm to identify the road type on which the
assigned vehicle was travelling. This road identification process is achieved
through parameter comparisons with reference values that defined various road
types. Additionally, the algorithm identified the driver type using driver
inputs, such as gear shift patterns and the driver’s handling of the brake and
accelerator pedals. Doshi et al.’s algorithm attained a Receiver Operating
Characteristic (ROC) value of 85% accuracy in road-type identification.

The behavioural analysis on road rage in China by Chai et al. [33] reports an
inverse proportionality between cases of road rage and lane number on a given
road. In other words, with fewer lanes, there are more incidences of road
rage. Additionally, the study reveals that road rage increases with an
increase in the number of nonmotorised vehicles. Road rage generally involves
fewer trucks, although more trucks are involved in road rage on highways and
daytime driving, which leads to fewer incidences involving non-motorised
vehicles. A limitation of the study is its small sample size and lack of
demographic and environmental variables, which require a more detailed
analysis in the future. To characterise road types and measure the degree of
driver aggression, Messeguer et al.

[34] design and implement a neural network-based algorithm to assist drivers
by highlighting unacceptable driving behaviours. Their test results
demonstrate the ability of neural networks to achieve a degree of precision in
the classification of driver and road types. Since contextual information
plays a critical role in the accurate performance of various road
classification and driver distraction identification algorithms, useful
contextual information collection is vital.

Rakotonirainy et al. [35] and Khan [36] propose a context-aware system for the
real-time collection and analysis of contextual information related to a
vehicle, the immediate environment, and the driver. The system also gathered
information from questionnaires. A Bayesian network model is employed to
analyse contextual information through a learning model. This facilitates the
observation and prediction of a driver’s future moves. The model attains a
comparable accuracy in predicting future driver behaviours and warning other
road users. However, the system is too complicated to implement in real life;
besides, false alerts are a key problem of this system.

Methods for recognising and classifying road traffic accident severity play an
essential role in understanding accidents, their causes, and possible
mitigation strategies. To that effect, Jianfeng et al. [37] design a set
theory-based accident recognition and classification method that supports
vector machines. Their model employs rough set theory in calculating the
significance of the driving environment, road, vehicle, and human attributes,
and the results demonstrated the model’s ability to improve recognition
accuracy and reduce computational workloads. The study’s limitation is that
human physical behaviour, such as the driver’s face orientation, is not
considered.

###  2.6 Weather

The travel weather warning system (TWWS) by Cai et al. [19] is similar to a
Road Weather Information System (RWIS) for sharing weather safety information
and disseminating safety warnings to drivers. This system is made up of risk
estimate models that were based on extensive weather-related crash data.
Weather-related data are collected using questionnaires, wherein drivers
identify the various risks encountered while driving under different driving
conditions. The severity of each type of weather is measured on a four-point
scale ranging from slight to catastrophic. Metrics such as the intensity of
rain and traffic volume are also considered.

Malin et al. [38] state that rainy weather is a significant factor in traffic
accidents, with the risk of accidents increasing under poor road weather
conditions.

Sherretz and Farhar [39] report a positive linear correlation between rainfall
and the frequency of road traffic crashes. Bergel-Hayat et al. [40] also
revealed a significant correlation between the aggregate number of traffic
accident injuries and weather variables. They observe that the correlation
between these two parameters varies depending on road type.

Brodsky and Hakkert [41] propose measuring the risk of a road accident during
rainy weather. Their method shows a drastic rise in road traffic accident
injuries during rainy weather compared to dry weather. The increased dangers
under wet conditions that follow a long dry season are well known to drivers,
as was found by Knapper [42].

####  2.6.1 Speed

Maintaining the correct speed continues to be a challenge for many drivers.
Drivers who violate driving rules, such as speeding, are said to crash more
often. Stradling and Auberlet [28], [43] show that vehicle trajectory
variations may reveal valuable details on how spatial restrictions impact
driver behaviour (e.g., lateral location and speed). On the one hand, the
findings reveal that while driving on the vertical curve of the crest, before
encountering oncoming vehicles and narrower lane width, the lateral location
variability is more significant. However, this was reduced according to the
perceptual procedure used. Another study investigated the impact of multiple
factors, such as image size, speed, road shape, driving experience, and
gender, on the perception of speed by drivers.

Wu et al. examine the most miniature image scale (38% of the actual field of
view) in [44] and find that speed calculations are the most reliable. Driving
velocity is gradually undervalued as image scale grows. Participants with
driving experience correctly measured driving speed on both wide and narrow
roads. However, those without driving experience make more underestimates on
broader roads. Furthermore, the environmental conditions concerning speed
performance have been highlighted by Bellis et al. [45]. They challenge
current policies and suggest interventions by teaching drivers about the
inverse illumination-speeding relationship and measuring how better vehicle
headlights and intelligent road lighting can attenuate speed.

Real-world speeding and its association with illumination, an environmental
property described as the incidence of luminous flux on a surface, have been
examined in the literature. Manser and Hancock [46] address the need to
determine whether visual patterns and wall tunnel texture impact driving
performance since maintaining the correct speed continues to be challenging.
The findings show that the relationship between driver speed and reaction is
affected by the visual pattern and the texture of the tunnel wall.

###  2.7 Vehicle

Mishra and Baja [47] and Kamar and Patra[48] adopt ML to predict the driving
patterns of drivers and the impact on their social behaviour using CCTV
cameras installed to monitor traffic. Their observations are carried out
during the day and the metrics used for measurement are instances of traffic
violations due to aggressive patterns.

Lee and Kum [49] propose a feature-based lateral position estimation algorithm
that employs lateral positioning and stereo vision, regardless of changes in
viewpoints and obstructions, resulting from pixel-wise feature extraction. The
algorithm extracts vehicle images through image filtering and thresholding,
and removes the ground portions from images captured by cameras. The
algorithm’s detection component consists of a deep convolutional neural
network with a speeded-up robust feature (SURF) to match successive image
frames. They estimate the lateral position of ground points using an inverse
perspective mapping algorithm (IPM). The testing and validation phases are
performed using urban and highway methods to achieve zero mean error and a
standard deviation of $0.25m$ in the estimation of the lateral position.

[50] detected driver behaviour based on car follower behaviour, which can vary
according to distraction, fatigue, driver habits, and surrounding traffic. On-
road trajectory data obtained in Beijing are used in their study, and
distinctive driver states and car-following models are observed as metrics.
This led to the prediction of the driver’s velocity control with improved
accuracy.

Mittal [1] used object detection and a faster R-CNN model to detect vehicles
of different scales and sizes. An evaluation is performed using the
$FLIR_{ADAS}$ dataset for both RGB and thermal images. Gong et al. [52]instead
propose using the YOLOv3 algorithm to detect vehicles in thermal images,
achieving a 65% higher accuracy and speed than the original YOLOv3-tiny.

####  2.7.1 Pedestrians

Kharjul et al. introduce in [53] an active protection automobile pedestrian
identification device to minimise the amount and intensity of vehicle-
pedestrian collisions. They present a pedestrian identification approach that
segments pedestrian candidates in images. The method uses Ada-Boost and
cascading algorithms to confirm whether each claimant is a pedestrian. The
Support Vector Machine (SVM) is used as a final classifier which exploits the
input features of grey images for training.

Taiwan and Yamada develop in [54, 55] a tool for calculating driver knowledge
and behaviour concerning pedestrian positions at crosswalks and while
crossing, especially at left and right turns at intersections. Their findings
on an appraisal carried out using objective evidence of driving behaviour on
public roads have also been published. In contrast, Rangesh et al. [56]
examine the behaviour of pedestrians. In particular, from a solely vision-
based point of view, they concentrate on detecting pedestrians engaging in
secondary behaviours involving their mobile phones and other handheld
multimedia devices. They propose a pipeline integrating articulated human pose
prediction and gradient-based picture features to detect the presence/absence
in either a pedestrian’s hand. A belief network is used to encode knowledge
from multiple streams and their dependency on each other. This network is then
used to forecast a likelihood score that suggests pedestrians’ engagement with
their devices.

Phan et al. [57] focus on drivers’ actions whenever a person emerges in front
of their car. They used two static parameters-based methods, namely the
Necessary Deceleration Parameter and Time-To-Collision, and compare them to
the proposed approach. They also employ a Hidden Markov Model (HMM) to
characterise driver knowledge and unawareness of pedestrians. Compared with
the baseline algorithms, the outcome indicates a significant enhancement of
the HMM-based process.

###  2.8 Illumination (Day, Night)

In [58], Clarke et al. observe that the rate and severity of road traffic
accidents are influenced by driving. In their study, the visibility conditions
under investigation include rainy and night driving, with the control test
being dry daytime driving. Their findings on the increased rate and severity
of crashes at night and during rainy weather match the conclusions of [59],
where the risk of fatal crashes is shown to increase by a factor of four
during night driving compared to daytime driving.

##  3 The multi-class driver distraction risk assessment model

Based on existing literature, we develop and test our hypothesis that driver
behaviour based on driver distraction has different severity levels, which we
define as ‘safe’, ‘careless’, or ‘dangerous’.

We then justify the weighting metrics for the distractions present in the
TeleFOT dataset [67]. The following observable parameters can characterise
signs of attention deficit and fatigue in the driver: PERCLOS (PERcentage of
eye CLOSure, i.e., the percentage of the time the driver’s eyes are closed)
[60], turning the head to the left/right to the body, tilting the head forward
relative to the body (the moment when the driver is “nodding off”), duration
and frequency of blinking, and the degree of openness of the person’s mouth (a
sign of yawning). In particular, for PERCLOS, there was a discrete number of
parameters defined, namely P70, which is the proportion of time for which the
eyes were closed of at least 70%; P80, which is the proportion of time for
which the eyes were closed of at least 80%; and EYEMEAS (EM), which is the
mean square percentage of the eyelid closure rating [60]. Furthermore, general
information describing a driver helps not only explicitly identify that driver
among all other drivers who installed and used a particular monitoring
software package but also helps to improve the search for and classification
of drivers with similar characteristics (general patterns among groups would
help to predict developing situations). This can be accessed via the database,
with a weight coefficient applied since this is a “common” behaviour rather
than an individual driver’s behaviour. Ginting et al. [61] adopted a 5-point
Likert scale to model anxiety about individual coronary heart disease at
different levels. Lopez-Fernandez et al. [62] also used a scale in assessing
problematic internet entertainment among adolescents. The scale adopted was a
self-administered scale for measuring the behavioural addiction of online
social network users and video gamers regarding the degree of severity. Based
on this, we formulated the distraction severity levels. At this moment, the
ratings of the severity level of distractions were designed using a 5-point
Likert-type scale, as seen in Table 1, [63, 64, 65].

The proposed model considers the severity level of driver distraction based on
an observation of their driving history. While this can be unpredictable, we
analyse the driver’s behaviour frame by frame to obtain intricate details. We
take the following steps:

  * 1.

Decompose the video to a frame-by-frame level.

  * 2.

Study each frame to assess its severity level.

  * 3.

Aggregate the previous severity level of frames to the current frame severity
level.

  * 4.

Provide a precise class of severity based on the calculated severity level.

In the following, we outline the essential aspects of our model for accessing
the severity level of driver distraction. We acquired the knowledge and data
by observing and analyzing individual frames from the input source. We began
by formulating the risk assessment based on driver behaviour according to
$P={p_{1},p_{2},p_{3},\dots,p_{n}}$, as described in Table 1. Each parameter
$P_{i}$ is characterized by some set of action
$A={a_{1},a_{2},a_{3},\dot{,}a_{n}}$, with each action $a_{i}$ having a weight
$W_{i}$.

Table 1: The essential aspects of the proposed model for accessing the severity level of driver distraction. The maximum weight values are reported in boldface. Parameter | Action | $\mathbf{W_{i}}$
---|---|---
State of Hand | Double hand | 0
Single hand | 1
No hands | 2
Road Type | Urban | 1
Dual | 2
Highway | 3
Face Orientation | On road | 1
Off-road | 2
Illumination | Day | 1
Night | 2
Eye Gaze | Eyes on road | 0
Eyes off-road | 1
Eyes shut | 2
Weather | Dry | 1
Rain | 2
Snow | 3
Manoeuvres | Stopped | 0
Turning | 1
Moving | 2
Surroundings | Vehicle not present | 0
Vehicle present | 1
Pedestrians | not present | 0
present | 1

The next stage consists in identifying the severity levels, according to
severity rates, respective colour for identification, and classification label
to start with. For instance, if the severity is 0.0, the risk colour will be
right green, this will be no distraction from the driver has been observed,
and it will have no impact on the driver’s life. While, if the severity level
is 0.9 or above, the risk colour will be red, and it will mean that a severe
causality can be expected, and it is hazardous to keep driving. Table 2
provides these details along with the relevant consequences.

Table 2:  Severity level, the risk colour and its impact on the consequences Severity | Risk Color | Impact | Distraction | Consequences
---|---|---|---|---
(0.0 - 1.0)
0.0 | Light Green | No Impact | Safe | No distraction
0.1-0.25 | Green | Slight Impact | Safe | Slight distraction
0.25-0.399 | Yellow | Low | Safe | Noticeable
distraction
0.4-0.599 | Dark Yellow | Medium | Careless | distraction detected
0.6-0.79 | Orange | High | Dangerous | Frequent distractions
0.8-0.9 | Dark Orange | Very High | Dangerous | Casualty prone
0.9-1.0 | Red | Extreme | Extremely | Severe
Dangerous | casualty Prone

####  3.0.1 Risk Assessment Matrix

An approach to the computation of risk assessment in a quantitative model uses
a Risk Assessment (RA) Matrix’s graphical tool. The risk matrix involves
calculating the magnitude of the potential consequences scaled on the vertical
axis (levels of probability) of these consequences occurring; technically, the
probability of these consequences occurring on the horizontal axis. This
facilitates an increase in the visibility of risk and impacts decision-making.
The risk is computed by calculating the Consequence Likelihood of Occurrence
Likelihood. Here, the likelihood depicts the probability of a driver’s
distraction being related to their context awareness. Consequences/Severity
Level: The occurrence of multi-class context-aware distractions is classified
into severity levels of distraction.

####  3.0.2 Probability

Probability is the measure of the likelihood that an event will occur. For
example, there is a possible aggregation to measure the number of times a
driver experiences a particular distraction during a driving course. The
driver may be profiled according to the distraction severity level at the end
of the driving course.

####  3.0.3 Likelihood

The likelihood levels can be described as frequency values (duration course)
and state values (every frame). Four impact levels are considered in this
paper, namely no impact, low impact, medium impact, and high impact. When an
effect has no impact, the likelihood score is one, and the likelihood of that
distraction observes no distraction or a distraction that has not currently
occurred. When a slight distraction is detected, the impact is low, with a
score of 2. A medium result is considered when a minor distraction has
occurred, and the score is then set to 3; 4 implies a medium to significant
distraction occurrence. More impacts can be seen in Table 3 below.

Table 3: Severity Risk Matrix, 5 continuous frames, progression of danger Severity | Severity values
---|---
Extreme | 7 | 7 | 14 | 21 | 28
Very High | 6 | 6 | 12 | 18 | 24
High | 5 | 5 | 10 | 15 | 20
Medium | 4 | 4 | 8 | 12 | 16
Low | 3 | 3 | 6 | 9 | 12
Slight/Very low | 2 | 2 | 4 | 6 | 8
No Impact | 1 | 1 | 2 | 3 | 4

We propose using a weighted average of the parameters to compute the severity
levels per frame, as depicted in Table 1-4. These weights are capped by the
maximum number that a parameter can take. For example, we take “State of Hand”
as a parameter and grade it as follows: 0 - double hands, 1- single hand, 2-
no hands. If the value of a given frame for this parameter is x, then the
weighted value is x/2 since the maximum value, of this parameter, can take 2.
Let us generalise this for any parameter $x_{i}$ with a maximum value $m_{i}$
as $\text{Severity level}=\sum_{i=0}^{n}x_{i}/m_{i}$. Where $n$ is the number
of parameters in consideration.

Table 4: Severity Risk Assessment Matrix RA | Likelihood
---|---
1 | No distraction is observed or occurred yet
2 | A slight distraction has been observed
3 | A minor distraction has occurred
4 | A medium or major distraction has occurred

####  3.0.4 Special considerations

To ensure the safety of drivers [66] developed a lane departure warning system
based on image processing using a mono camera installed inside the car. A
distinctive feature of the system is that it successfully processes several
road conditions, including undesirable situations such as changing the width
of the road lane, the radius of its curve, the direction of the road, and the
complete absence of a road surface. From this we gather that speed depends on
the road type; hence, we multiply it by the weight of its road for speed. We
considered road type in the UK as this conforms to the source of the dataset.
For the metric of road types, we define the threshold according to the speed
limit allowable on the road type, i.e., urban, single carriage, and motorway
at 30 mph, 60 mph, and 70 mph, respectively. Furthermore, we define the
following contextual data:

  * 1.

Vehicle $V$ and driver data with probabilities $P(V)={v1,v2,\dots,v_{m}}$

  * 2.

Environment $E$ and environmental data with probabilities
$P(E)={e1,e2,\dots,e_{n}}$

  * 3.

Speed $a$

  * 4.

Probability of Surrounding $P(S)$

  * 5.

Probability of Pedestrians $P(Pe)$

We formulate the following equations. The speed is computed as described in
equation 7, for example, given that the national speed limit of the UK is 70
mph and the maximum road type weight and the score is 3:

Let the national speed limit of the UK be denoted by $MaxSpeed=70$ mph, and
let the maximum road type weight and score be denoted by $MaxRoadType=3$. Then
the speed on a given road can be computed as follows:

$Speed=\frac{MaxSpeed\cdot
MaxRoadType}{RoadType}\cdot\frac{1}{\frac{RoadType}{MaxRoadType}}$ (7)

where $RoadType$ is the numerical value representing the type of road, such
that higher values indicate more challenging road conditions. The fraction
$\frac{RoadType}{MaxRoadType}$ adjusts the speed based on the road type so
that the speed is reduced on more challenging roads.

We understand there are different data points in each frame; thus, the
severity level of a given frame with k data points where severity $(S^{*})$ of
a given frame is $(fi)$:

$S(f_{i})=\frac{1}{k}\left(\sum_{i=0}^{m}P(V_{i})+\sum_{j=0}^{n}P(E_{i})+aP(S)+P(Pe)\right)$
(8)

We now compute the aggregate severity $(S^{*})$ of a given frame $(fi)$ given
the last $i-t$ frames. This is achieved by taking the average of the current
frame’s severity score compared to the severity score of the last i-t frames:

$S(fi)=\frac{1}{t}\left((S(fi)+\sum_{j=t}^{i-1}S(f_{j})\right)$ (9)

The verification and validation processes for the proposed model typically
include both computational and physical aspects. To assess the degree of
adequacy of the numerical modelling, the following steps can be performed: 1)
Determine the order of convergence of numerical solutions in comparison with a
numerical solution using a reduced number of parameters; and 2) assess the
sensitivity of the sampling algorithm to various uncertainties, including
parameter constraints, grid adaptation to real measurements and boundary
conditions. Furthermore, validation assumes a careful comparison of the
numerical calculation results of the phenomenon under study with experimental
data to obtain an answer to the question “is the numerical solution,
correct?”. Thus, a comparative analysis of the model with all the conditions,
including the uncertainties associated with missing parameters and boundary
conditions from the real world and computational points of view, is carried
out.

A few methods can be used for model validation purposes;

  * 1.

The L2 Loss method consists in evaluating the loss function to compute the
squared error for each training dataset, thus returning the square of the
differences between the actual and the predicted values, i.e.
$L=\left(y-f(x)\right)^{2}$.

  * 2.

Errors (residuals) can be predicated via a cross-correlation test: Are the
residuals uncorrelated with the input?

  * 3.

The model can be applied to unseen data (cross-validation). This strategy may
be helpful since it establishes the robustness of the proposed model. It can
also provide the basis for the hybrid cross-correlation validation since there
is a need to separately investigate how the inputs and outputs are correlated
and how this correlation is affected by our modelling scheme;

  * 4.

“Inverse Problem” approach, i.e., acquire a solution to the problem and solve
the inverse case to obtain the output parameters. This will help to validate
the assigned weight coefficients and the overall parameterisation scheme.

In our case, the reliability of our model is tested by performing a cross-
correlation test to analyze the residuals. This is carried out on the data
obtained over two separate datasets, with the analysis, separately applied to
the inputs and outputs.

##  4 Experimental methods

A discrete-time model is proposed for the application of ML to detect the
pattern in time-series driver distraction data. Consequently, we develop an
adaptive model for the prediction of the driver’s severity level based on
distraction. The MDDRA model architecture illustrated in 1 the state flow of
the data and system modules that constitute the entire system. The
architecture is made up of six states, as described in the following
subsections.

Figure 1: MDDRA Model Architecture

###  4.1 Driver and vehicle data collection

All required data are collected (from the vehicle and driver) using multiple
sensors and video recorders. Sensor-based data are collected in real-time,
including road type, driver movements, face and head direction, vehicle speed,
weather, and the surrounding driving environment.

###  4.2 Object extraction

This architecture module extracts distraction state information (gaze at
something else, Overspeed, etc.), including the changing state of the
distraction, and feeds it into a probabilistic model for labelling.

###  4.3 Data labelling

The probabilistic model is further applied to the labelled extracted data,
which is then used to train the system’s core engine before the ML model is
applied.

###  4.4 Real-time monitoring

Context-aware real-time data from the real-time driving video streams of the
internal and external sensors of the vehicle are monitored. The data are
further analyzed, and feature extraction of both the driver and vehicle state-
based data is performed; this is then fed into the ML model.

###  4.5 ML model

The ML model takes in state-based data (eye gaze, state of hands, speed, face
orientation, manoeuvre) and training datasets to predict the level of
distraction. The resultant model is the probability of the occurrence of
driver distraction in the current distraction frame state $P(C_{t+1}S_{t+1})$,
measured as the state transition from the previous frame state, denoted as
$P(S_{t+1}C_{t})$. If the severity of distraction is high, vehicle takeover
operations take effect.

###  4.6 Vehicle takeover

The severity informs the decision to perform a vehicle takeover of the
distraction detected by the ML. If the distraction passes the threshold, i.e.,
transitions from careless to dangerous, then the decision for the vehicle to
take over driving is triggered.

A dynamic Bayesian network (DBN), as depicted in Figure 2, is an extension of
a Bayesian network that uses the time (dynamic) concept in modelling
sequential time-series observations. It also uses a probabilistic inference
model in handling uncertain information. An acyclic graphic represents the
conditional independent and latent temporal variables discretely and
continuously. For this case, the inference from the DBN is derived from three
key classes of nodes. Namely, driver features nodes, distraction identifiers,
and contextual data. These inputs are represented in this model by nodes such
as the state changes of the driver, consisting of 5 central nodes, namely face
orientation, speed, manoeuvres, eye gaze, and state of hands. The
environmental changes node, consisting of road type, weather, and time of the
day, forms part of the contextual input data into the model and data on
pedestrians and the surrounding environment. The final input is the
distraction identifier derived from the analysis of the driver features by a
hybrid CNN-LSTM. The output of this acyclic graph is a severity score, which
is the measure of the degree of the distraction of the driver extracted from
the driver features and contextual information.

Figure 2: Context-aware probabilistic model for severity classification

###  4.7 Dataset

The TeleFOT Naturalistic driving study dataset is a European Field Operation
Test (FOT) [67]. The project was designed to enhance research on intelligent
transportation systems [68, 69]. The experiment was conducted in the UK and
involved 27 participants [69]. Each driving video consists of four video
channels that monitor in-vehicle and out-vehicle parameters, including face
orientation, eye gaze, and hand position. The dataset consists of time-series
data, the camera is located inside the vehicle on the dashboard and on the
passenger side of the vehicle, it also has two extra cameras to capture the
environment as can be seen below illustrated in 3.

Figure 3: TeleFOT Dataset

###  4.8 Probabilistic data model

We considered the driver distraction state’s changes frame by frame, as
depicted in Figure 2. Technically, our proposed Context-aware probabilistic
model for severity classification can be described as the probability of the
occurrence of driver distraction in the current frame state
$P(C_{t+1}\|S_{t+1})$ from the previous frame state $P(S_{t+1}C_{t})$. So,
there exists the probability of the occurrence of distractions in the
environmental state $P(E_{t+1}\|S_{t+1})$, if the current frame state
$S_{(}t+1)$ changes from the previous frame state $S_{t}$. The proposed
extended dynamic Bayesian model includes several environmental variables such
as road type, weather, and day. In order to compute the probability severity
scores $P(S_{t+1}\|C_{t+1},E_{t+1},Sr_{t+1},Pe_{t+1},S_{t})$, we utilized the
dynamic linear model 10 and which is a combination of the state change of
driver distraction $C_{t+1}$, environmental changes $E_{t+1}$, distraction
identification $D_{t+1}$, pedestrians $Pe_{t+1}$, and surroundings $Sr_{t+1}$.

$\begin{split}P(S_{t+1}\|C_{t+1},E_{t+1},Sr_{t+1},Pe_{t+1},S_{t})=\\\
P(C_{t+1}\|S_{t+1}).P(E_{t+1}\|S_{t+1}).\\\
P(Sr_{t+1}\|S_{t}).P(Pe_{t+1}\|S_{t+1}).\\\
P(D_{t+1}\|S_{t+1}).P(S_{t+1}\|S_{t})\end{split}$ (10)

###  4.9 Interdependencies Test

We can apply the developed interdependency test for road type and its impact
on driving speed. For example, in Figure 4, the regression analysis
coefficient is calculated as 0.529134, implying a significantly positive
relationship. We assume that the driver would drive within the UK speed limit.
The dataset of the driver may be more biased towards a degree of severity
compared to other databases. Thus, it is necessary to validate the model using
a regression model to test the interdependencies. We perform a correlation
analysis between driver distraction and the severity classification of the
distraction. Also, we conduct a multi-linear regression analysis to estimate
the influence of driver distraction on the degree of severity classification.

Figure 4: Distraction Interdependence State Diagram

###  4.10 Data Normalisation

We log and normalize the vehicle’s speed synchronously with the distraction
state frame; thus, the time-series data of the vehicle are correlated at every
frame. Subsequently, a regression analysis is conducted to validate our
hypothesis, as seen in the results section. The severity level classification
of the baseline drivers is likely to have a lower mean than experienced
drivers. Furthermore, a regression analysis is performed to indicate the mean
of the in-vehicle parameters, mean vehicle data, and mean environmental data
to produce the safe severity level. Meanwhile, in professional drivers, a safe
severity level is likely to be more than the baseline. However, in other
parameters, like per frame, severity means an aggregate severity means. In
contrast, the statistical analysis of all the parameters contributes
significantly toward the severity level considered safe, careless, or
dangerous. However, only the vehicle speed distribution across the journey and
its relation to the road type and driver distraction severity level has a
substantial impact.

###  4.11 Results of the model validation procedure

To provide basic information about the variables in the dataset, the
descriptive statistics for one of the simulated events (driver 1, event 1) are
presented in table 5. The mean, median, kurtosis, and skewness values are
calculated by using equation (11) to (14).

$\text{mean}=\frac{Sum(x)}{Count(n)}$ (11)
$\text{Median}(x)=\begin{cases}X\left[\frac{n}{2}\right]&\text{ if }n=even\\\
\frac{X\left[\frac{n-1}{2}\right]+X\left[\frac{n2+1}{2}\right]}{2}&\text{ if
}n=odd\end{cases}$ (12)

Where X is an ordered list of values in the data set, and n denotes the number
of values in the data set (count). For a univariate data
$y_{1},y_{2},\dots,y_{N}$ the formula for skewness is:

$g_{1}=\frac{\sum_{i=1}^{N}\frac{\left(y_{i}-\overline{y}\right)}{N}^{3}}{S^{3}}$
(13)

Where $\overline{y}$ shows the mean, $S$ is the standard deviation, and $N$ is
the number of data points. Note that in computing the skewness, the s is
computed with $N$ in the denominator rather than $N-1$.

$kurtosis=\frac{\sum_{i=1}^{N}\frac{\left(y_{i}-\overline{y}\right)}{N}^{4}}{S^{4}}$
(14)

$S=\sqrt{\sum\frac{\left(y_{i}-\overline{y}\right)^{2}}{N}}$ (15)

The results of these metrics suggest that this is a symmetrical distribution.
This reflects how the data were modelled. It would be valuable to deploy this
model using real data from the video sensor to access the accurate
distribution of parameters, such as face orientation and eye gaze, and then
analyse the results.

Table 5: Descriptive Statistics Mean | 0.513049625
---|---
Standard Error | 0.007304311
Median | 0.508023896
Standard Deviation | 0.118456024
Sample Variance | 0.01403183
Kurtosis | 0.057262351
Skewness | 0.217203269
Range | 0.725482175
Minimum | 0.162361126
Maximum | 0.887843301
Sum | 134.9320515
Count | 263
Largest (1) | 0.887843301
Smallest (1) | 0.162361126
Confidence Level (95.0%) | 0.014382625

To validate the model, its predictions are tested using correlation analysis,
as suggested in Section 4 This technique is typically used to test
relationships between quantitative or categorical variables. Correlation
coefficients have a value between -1 and 1. A “0” value means that there is no
relationship between the variables, while -1 or 1 means a perfect negative or
positive correlation (negative or positive correlation here refers to the type
of graph the relationship will produce).

Table 6: Correlation Coefficients State of Hand | 0.425847
---|---
Road Type | 0.363796
Face Orientation | 0.420461
Time of day | 0.224532
Eye Gaze | 0.296584
Weather | 0.247372
Maneuver | 0.323121
Speed | 0.053056
Surrounding | 0.441935
Pedestrians | 0.255076

From Table 6, it is clear that there is a positive correlation with all but
one of the parameters used in the model, that is, the speed of the vehicle.

The model is also tested on multiple events, and the results demonstrate a
consistent lack of correlation with velocity. This might indicate a need for a
wider velocity span to be present in the dataset or, if this does not affect
the results, better represent the model’s influence.

##  5 RESULTS AND ANALYSIS

The implementation of our model and architecture in Figure 1 was carried out
to determine which ML model will best predict driver distraction to aid in
vehicle takeover decision-making. Furthermore, to avoid bias, the results of
the experiment were determined using different ML algorithms in the dataset.
We analyse the scatter plot and the confusion matrix of the predicted class.

###  5.1 INTERDEPENDENCY TEST USING REGRESSION ANALYSIS

The regression analysis was performed on the following three context-aware
characteristics:

  1. 1.

The in-vehicle features related to the driver distraction such as hand moment,
gaze;

  2. 2.

The vehicle features such as vehicle speed, manoeuvres;

  3. 3.

The environmental features such as pedestrians, vehicles, and weather.

The association and interdependencies between a pair of distractions need to
be tested. We applied a regression to the prediction of driver distraction
divided into severity levels. Here, we further tested the relationship between
distractions by classifying distraction as either in-vehicle, context-aware,
or environmental distraction.

####  5.1.1 Driver Distraction

if they are meant to be capitalised they will go automatically, but if we
change the forum then we have to do it manually every time Driver distraction
features consist of state of hand, face orientation, and eye gaze. Figure 5
shows driver distraction, showing a strong relationship between eye gaze on
the road (eye gaze 0), off-road face orientation and a single hand on the
wheel, and having a high severity level of distraction leading to dangerous
driving. Eyes closed, the orientation of the face on the road, and double
hands-on wheel also significantly affect the severity score.

Figure 5: In-Vehicle State of Hand, Eye Gaze, and Face Orientation

Table 7 presents the distractions in the vehicle with respect to the
prediction of the severity score. Based on the $P$ value of 0.758, the
probability of the state of hands to predict the severity of distraction is
low. The intercept of 0.529134, which is highly significant, suggests a
relationship between the state of hands, face orientation, and eye gaze. The
statistical predictors use the t-statistics and P-values of each distraction.
The lower P-value of 0.249 for face orientation shows that this is a highly
significant predictor of the severity score. The coefficient of determination
is 0.005668.

Table 7: Human Distractions Regression Analysis Intercepts | Estimated | Std Error | T-Value | P Value
---|---|---|---|---
State of Hand | 0.015526 | 0.015526 | 34.080 | 0.758
Face Orientation | 0.1-0.25 | 0.014414 | -1.156 | 0.249
Eye Gaze | 0.002045 | 0.008981 | 0.228 | 0.820
Intercept | 0.529134 | 0.015526 | 34.080 | <2e-16

####  5.1.2 Environmental Distractions

Figure 6 shows that dry weather, dual carriageway, and a bright day produced
the highest dangerous severity score, while rainy weather, double carriageway,
and night produced a slightly more dangerous situation. Snowy conditions on
the highway and at night had the highest degree of influence on the severity
score.

Figure 6: Road Type, Time of Day, and Weather

The results in Table 8 show an environmental distraction about the prediction
of the severity score. The low $P$-value of 0.175 for the road type shows that
the road type has a highly significant impact on the prediction. The
environment intercept of 0.556982 showed a significant association between the
severity score and the type of distraction road of the result, the time of day
and the weather. However, the lowest average P-value of 0.5897 was the lowest
compared to the other distraction classifications. The least residual standard
error of 0.1165, which is the smallest of all residual standard errors,
indicates that this model best fits the data.

Table 8: Environment Regression Analysis Intercepts | Estimated | Std Error | T-Value | P-Value
---|---|---|---|---
Road Type | -0.011838 | 0.008702 | -1.360 | 0.175
Time of Day | -0.011848 | 0.008579 | -0.215 | 0.830
Weather | -0.002086 | 0.009035 | -0.231 | 0.818
Intercept | 0.556982 | 0.031870 | 17.477 | <2e-16

Figure 7 shows five instances of vehicle presence and pedestrian presence that
result in a hazardous distraction classification, which means that if there
are vehicles or pedestrians present in the surroundings, the chances of
distraction by the driver are significant.

Figure 7: Pedestrian and Surrounding (Vehicle Presence)

Table 9 presents the in-vehicle distractions to the prediction of the severity
score. Based on the P-value of 0.532, the probability that the surroundings
influence the prediction is low. The intercept of 0.529157, which is
significant, suggests a relationship between the surroundings and pedestrians.
The statistical predictors use the t-statistics and P-values of each
distraction. The lower P-value of 0.532 for the surroundings (vehicle
presence) is a highly significant predictor of the severity score. However,
the P-value of 0.830 for pedestrians suggests that there is no association
between pedestrians and the severity score.

Table 9: External Distractions Regression Analysis Intercepts | Estimated | Std Error | T-Value | P-Value
---|---|---|---|---
Surrounding | -0.009065 | 0.014502 | -0.625 | 0.532
Pedestrians | 0.003121 | 0.014487 | 0.215 | 0.830
Intercept | 0.529157 | 0.013145 | 40.255 | <2e-16

Vehicle distractions include manoeuvres and speed. Figure 8 shows the
distraction within the speed range of 23 mph to 26.2 mph due to the high
frequency of speed manoeuvres. There are a few outliers with very high
severity and very high levels of danger.

Figure 8: Vehicle, Speed and Manoeuvre.

The results in Table 10 show the influence of vehicle distractions on the
prediction of a severity score. Based on the p-value of 0.855, the probability
that speed influences the prediction is low because the driver stays within
the speed limit. However, during manoeuvres, there is a higher degree of
significance. The intercept of 0.695812, which is highly significant, suggests
a strong relationship between speed and manoeuvre. The statistical predictors
use the t-statistics and P-values of each distraction. The lower P-value of
0.815 for manoeuvres shows that this is a highly significant predictor of the
severity score.

Table 10: Vehicle Distractions Regression Analysis Intercepts | Estimated | Std Error | T-Value | P-Value
---|---|---|---|---
Speed | -0.009065 | 0.035983 | -0.183 | 0.855
Manoeuvre | 0.002050 | 0.008758 | 0.234 | 0.815
Intercept | 0.695812 | 0.941042 | 0.739 | 0.460

In this case, the driver is tested with the previous severity score and the
predicted actual severity score of the following video frame; in this
experiment, the driver’s overall performance is tested throughout the drive,
whereby there are a total of 262 frames, which is the equivalent of
approximately 11 seconds. In Figure 9, we can assume that the driver has
maintained a primarily constant careless driving behaviour. Additionally, the
regression analysis results in a strong correlation between the previous
severity score and the current severity score.

Figure 9: Severity Score, Previous Severity Score and Video Frames

The results presented in Table 11 show the influence of distractions in the
vehicle on the prediction of the severity score. Based on the P-value with a
lower value of 0.990, the state-of-hand probability predicts that the score is
low. The intercept of 1.828e, which is highly significant, suggests a
relationship between the sequence of video frames and the previous severity
score.

Table 11: Severity Score Regression Analysis

Intercepts Estimated Std Error T-Value P-Value Video Frames -1.329e-06
1.024e-04 -0.013 0.990 Previous Severity Score 9.618e-01 9.618e-01 5.812
1.8e-08 Intercept 1.828e-02 1.828e-02 0.196 0.845

###  5.2 ML MODEL

We implemented different ML models, such as discriminant, Nïve Bayes, SVM,
K-Means Nearest Neighbour (KNN), and Ensemble ML. To better evaluate
performance, the comparison of the classification results is shown in Table
12.

Table 12: Classification performances for the KNN, Discriminant, Naïve Bayes, SVM and Ensemble models. Model | Acc. % | Speed | T-Time
---|---|---|---
Fine KNN | 79.1 | 2700 | 4.4574
Medium KNN | 78.3 | 2500 | 3.5617
KNN Coarse | 59.3 | 2500 | 4.4974
KNN Cosine | 80.6 | 2600 | 4.368
KNN Cubic | 76.4 | 2000 | 4.239
KNN Weighted KNN | 80.6 | 2500 | 3.975
Linear Discriminant | 90.9 | 2700 | 3.5265
Quadratic Discriminant | 82.9 | 2500 | 5.2346
Gaussian Naïve Bayes | 93.2 | 3000 | 5.0814
Kernel Naïve Bayes | 90.1 | 1500 | 5.9402
Linear SVM | 92.0 | 2400 | 4.9151
Quadratic SVM | 92.4 | 2300 | 4.8007
Cubic SVM | 92.4 | 2300 | 4.6915
Fine Gaussian SVM | 58.6 | 2200 | 5.7229
Medium Gaussian SVM | 85.2 | 2100 | 5.5983
Coarse Gaussian SVM | 77.2 | 2300 | 5.4722
Boosted Trees | 58.6 | 3600 | 4.5331
Bagged Trees | 96.2 | 1000 | 6.3019
Subspace Discriminant | 92.4 | 780 | 6.8675
Subspace KNN | 79.8 | 600 | 6.7319
RUSBoosted Trees | 74.5 | 2900 | 4.6438

In Figure 10, the first two diagonal cells show the percentage of correct
classification by the trained network. For example, 142 frames are correctly
classified as careless. This corresponds to 99% of the 262 frames. Similarly,
80 cases are correctly classified as dangerous, which corresponds to 96% of
all edges. Three dangerous and three safe instances are incorrectly
classified, corresponding to 12% of all 264 frames in the data. Similarly, one
of the careless structures is incorrectly classified, corresponding to 1% of
all data. Out of 148 careless predictions, 99% are correct and 1% are wrong.
Of the 80 dangerous predictions, 96% are correct and 4% are wrong. Of 35 safe
cases, 92% are correctly predicted as safe, and 8% are correctly predicted as
false.

|  | Predicted
---|---|---
|  | Careless | Dangerous | Safe
Actual | Careless | 99 | 4 | 8
Dangerous | 0 | 96 | 0
Safe | 1 | 0 | 92
False Discovery Rate | 1 | 4 | 8
Figure 10: Confusion matrix, showing the predicted and actual safe, careless,
and dangerous driving.

####  5.2.1 Time Complexity

Safety in intelligent transportation systems (ITS) is critical, and having a
fast ML model to make an efficient decision is crucial for the safety of road
users. Figure 11 refers to the amount of time it takes to train a model, make
predictions, or perform other operations related to the learning process. The
histogram of residual values displays the frequency distribution of the
residuals in graph (a) indicates the distance between the observed prediction
time from the mean of each classifier’s total time for training. The
significant residual value between -200 and +300 shows an optimal
configuration for the proposed framework when employing ML variants. shows
that the fitted line’s intercept and slope values are projections for the
distribution’s position and residual parameters, respectively. Linear
discriminant in (b) depicts the shortest training time of 3.5265. This means
that this algorithm is able to train a model to classify data points into
different categories more quickly than the other algorithms being compared.
The y-axis of the graph shows the percentage accuracy of the different
algorithms. The sample variance, which is a measure of the spread of the data,
is being used to approximate the accuracy, prediction time, and training time
obtained using several ML algorithms. The residual values in (c) y-axis show
that the prediction was exceedingly low. Fitted values (refer to the x-axis)
show that the prediction was significantly accurate; 0 on the y-axis indicates
a 100% correct positive rate.

(a) Histogram of Residuals

(b) Linear discriminant

(c) Approximate Variance Inflation Factor

Figure 11: Residual Plots for Prediction Speed (obs/sec) vs Training Time
(sec).

####  5.2.2 Kruskal-Wallis Results

The Kruskal-Wallis rank test was used to evaluate the algorithms and the
results were presented in 13. The authors observed that the ensemble learning
method with the Bagged model had the highest mean rank of 21 compared to the
other variants of that model and other state-of-the-art ML algorithms. This
suggests that the Bagged model’s complex fitness function helped to extract
rich feature vectors for classification. On the other hand, the Boosted Trees
model had the highest mean rank of 21 compared to the other variants of that
model and other state-of-the-art ML algorithms. However, the authors noted
that this model’s linear fitness function helps to extract poor feature
vectors for classification. Additionally, the authors found that the linear
discriminant variant had the lowest mean rank of 1 in training time and
achieved 90% accuracy. This algorithm used linear functions to evaluate the
previous severity score and the expected total severity score of the following
video frame. Finally, the authors noted that Gaussian Nïve Bayes was the
second-best algorithm in terms of performance compared to others, except for
bounded trees. This algorithm had an average mean rank of 20, achieved 93.2%
accuracy, and had a z-score of 1.49. Overall, the results suggest that the
Bagged model and Gaussian Nïve Bayes performed well in this experiment, while
the Boosted Trees model may not be the best option for feature extraction in
classification tasks.

Table 13: Kruskal- Wallis ranks for the ML Algorithms to confirm Accuracy ($\alpha$), Training Time ($\tau_{T}$), and Prediction Time ($\tau_{P}$), Z-Score ($\zeta$), and the median value ($\mu$). Model | Kruskal-Wallis Average Ranks
---|---
$\boldsymbol{\mu}$ | $\boldsymbol{\alpha}$ | $\boldsymbol{\tau_{P}}$ | $\boldsymbol{\tau_{T}}$ | $\boldsymbol{\zeta}$
Bagged Trees | 96.2 | 21 | 3 | 19 | 1.65
Boosted Trees | 58.6 | 1.5 | 21 | 8 | -1.57
Coarse Gaussian SVM | 77.2 | 6 | 9 | 15 | -0.83
Cubic SVM | 92.4 | 18 | 9 | 10 | 1.16
Fine Gaussian SVM | 58.6 | 1.5 | 7 | 17 | -1.57
Fine KNN | 79.1 | 8 | 17.5 | 6 | -0.5
Gaussian Nïve Bayes | 93.2 | 20 | 20 | 13 | 1.49
Kernel Nïve Bayes | 90.1 | 14 | 4 | 18 | 0.5
KNN Coarse | 59.3 | 3 | 13.5 | 7 | -1.32
KNN Cosine | 80.6 | 10.5 | 16 | 5 | -0.08
KNN Cubic | 76.4 | 5 | 5 | 4 | -0.99
KNN Weighted KNN | 80.6 | 10.5 | 13.5 | 3 | -0.08
Linear Discriminant | 90.9 | 15 | 17.5 | 1 | 0.66
Linear SVM | 92 | 16 | 11 | 12 | 0.83
Medium Gaussian SVM | 85.2 | 13 | 6 | 16 | 0.33
Medium KNN | 78.3 | 7 | 13.5 | 2 | -0.66
Quadratic Discriminant | 82.9 | 12 | 13.5 | 14 | 0.17
Quadratic SVM | 92.4 | 18 | 9 | 11 | 1.16
RUSBoosted Trees | 74.5 | 4 | 19 | 9 | -1.16
Subspace Discriminant | 92.4 | 18 | 2 | 21 | 1.16
Subspace KNN | 79.8 | 9 | 1 | 20 | -0.33

####  5.2.3 Comparison of Models

The graph in Figure 12 compares the accuracy value of the proposed MDDRA, the
work of Mengtao Zhu et al. [70], the work proposed by Yanli Ma et al. [71],
and the work of Tianchi Liu et al. [72]. It can be seen that the proposed
model has outperformed the current state-of-the-art in multiclass distraction
prediction. Moreover, the model has achieved an accuracy of 96.21%, while the
current state-of-the-art claims an accuracy of 95.87%, which is lower than our
proposed methodology. Although Tianchi Liu et al. [72] have achieved slightly
higher accuracy, they have worked on a binary classification problem. The
multiclass classification is a more complex task than a simple binary
classification model, the state-of-the-art model with excellent results in
more than eight classes. Furthermore, the proposed model has provided fast
results as high as 3600 observations per second, making the proposed model
accurate but robust in terms of speed.

[70][71][72]MDDRA$80$$85$$90$$95$$95.87$$89.9$$97.21$$96.21$Accuracy (%)
Figure 12: Accuracy in % Model comparison

This paper deals with the problem by employing machine learning. The authors
have proposed a novel and robust MDDRA model. The model has tackled the driver
with almost possible variants, such as the current state of the hand, which
means whether the driver uses double hands, single hands, or no hands.
Similarly, the type of road on which the vehicle is running, the face
orientation on the road or off-road, whether it is a day or night, the eye
gaze of the driver, if the weather is dry, rainy or snowy, what is a current
manoeuvre, the surrounding vehicles, speed of the vehicle, speed of the
surrounding vehicle, and pedestrians. The suggested model, MDDRA, considers
vehicle, driver, and environmental data that occur during a journey to
categorise drivers into a risk matrix such as safe, careless, and dangerous.
The proposed model offers the flexibility to adjust parameters and weights to
consider each event at a specific severity level. Real-world data was
collected using the Field Operation Test (TeleFOT), which consisted of drivers
using the same routes in the East Midlands, UK. The results have great
potential to reduce road accidents caused by distracted drivers. We have also
tested the correlation of driver distraction (in-vehicle, vehicle, and
environment distractions) on severity classification against the continuous
driver distraction severity score. Furthermore, we have applied several
machine learning techniques to classify and predict driver distraction
according to severity levels to help transition from driver to vehicle. We
implemented different ML models such as Discriminant, Nïve Bayes, Support
Vector Machine (SVM), and K-Means Nearest Neighbour (KNN) Ensemble ML for
classification. Figure 13 shows the comparison of accuracy by applying these
models. It can be seen that the Bagged Trees-based Ensemble model has provided
the highest accuracy of 96.2% for classification, while fine Gaussian SVM and
Boosted Trees-based ensemble methods have resulted in the lowest accuracy of
58.6% for the classification task. The comparison of various ML models is
shown in Figure 13.

FINE KNNMEDIUM KNNKNN COARSEKNN COSINEKNN CUBICKNN WEIGHTEDLINEAR
DISCRIMINANTQUADRATIC DISCRIMINANTGAUSSIAN NAÏVE BAYESKERNEL NAIVE BAYESLINEAR
SVMQUADRATIC CSVMCUBIC SVMFINE GAUSSIAN SVMMEDIUM GAUSSIAN SVMCOARSE GAUSSIAN
SVMBOOSTED TREESBAGGED TREESSUBSPACE DISCRIMINANTSUBSPACE KNNRU BOOSTED
TREES$50$$60$$70$$80$$90$$100$$79.1$$78.3$$59.3$$80.6$$76.4$$80.6$$90.9$$82.9$$93.2$$90.1$$92$$92.4$$92.4$$58.6$$85.2$$77.2$$58.6$$96.2$$92.4$$79.8$$74.5$Accuracy
(%) Figure 13: Accuracy across multiple ML models

##  6 CONCLUSIONS

For a robust false proof alert system, the precise classification of driving
behaviour is needed. However, to the best of our knowledge, the current work
lacks complexity, rigidity, a synthesised dataset, more focus on a particular
side of perspective (vehicle, driver, or environment), false positive classes,
and low accuracy. This paper aimed to provide a novel MDDRA model that
considers vehicle, driver, and environmental data during a trip to categorise
the driver in a risk matrix as safe, careless, or dangerous. The MDDRA model
offers flexibility in adjusting the parameters and weights to consider each
event on a specific severity level. Real-world data was collected using the
Field Operation Test (TeleFOT), consisting of drivers using the same routes in
the East Midlands, United Kingdom (UK). The results showed that it is possible
to reduce road accidents caused by distracted drivers. We also tested the
correlation between distraction (driver, vehicle, and environment) and
classification severity based on a continuous distraction severity score.

Furthermore, we applied machine learning techniques to classify and predict
driver distraction according to severity levels to aid the transition of
control from the driver to the vehicle (vehicle takeover) when a situation is
deemed risky. The experimental results obtained using various ML algorithms
have shown better results than those of the baseline and the previous
literature. The algorithm with the best performance was Ensemble Bagged Trees,
which gave an accuracy of 96.2 %. However, this approach’s limitation is that
deep learning will produce better results regarding speed performance than an
ML technique. The result of the vehicle regression analysis had a higher
degree of correlation and was highly significant. The MDDRA model can be
adjusted to fit any distraction risk assessment considering the driver,
vehicle, and environmental contexts. When assigning weights to pedestrians on
the road, we did not consider accidents or vehicles that hit pedestrians.
However, the results of the regression show that vehicle distraction
constitutes a higher level of significance. Factors such as sample size and
data spread may have influenced the regression analysis’s P-value results.
Confidence intervals around the sample statistics would yield a better result
than P-values alone. In addition, adopting deep learning Convolutional Neural
Network-Long Short-Term Memory (CNN-DBN-LSTM) techniques in detecting and
classifying multiclass driver distraction would yield more effective and
efficient results. Finally, considering the accuracy over time complexity, the
best ML model adopted is the Bagged Trees.

## References

  * [1] “ADAS — Mobility and transport.” https://ec.europa.eu/transport/road/safety/users/young-people/distraction (accessed May 25, 2021).
  * [2] A. S. Kulkarni and S. B. Shinde, “A review paper on monitoring driver distraction in real time using computer vision system”, in Proceedings - 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering, ICEICE 2017, 2017, vol. 2017-Decem, pp. 1-4, doi: 10.1109/ICEICE.2017.8191851.
  * [3] R. R. Herrera, C. Holloway, D. Z. Morgado Ramirez, B. Zhang, and Y. Cho, “Breathing Biofeedback Relaxation Intervention for Wheelchair Users in City Navigation,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2020, vol. 2020-July, pp. 4575-4578, doi: 10.1109/EMBC44109.2020.9176144.
  * [4] P. Ventsislavova and D. Crundall, “The hazard prediction test: A comparison of free-response and multiple-choice formats,” Saf. Sci., vol. 109, pp. 246-255, 2018, doi: 10.1016/j.ssci.2018.06.004.
  * [5] T. M. Senserrick, M. J. Kallan, and F. K. Winston, “Child passenger injury risk in sibling versus non-sibling teen driver crashes: A US study,” Inj. Prev., vol. 13, no. 3, pp. 207-210, Jun. 2007, doi: 10.1136/ip.2006.014332.
  * [6] C. A. R. Insurance, C. A. R. I. Claims, and B. Y. E. Delbridge, “Causes and Effects of Distracted Driving,” pp. 1-10, 2020, Accessed: May 25, 2021. [Online]. Available: https://www.liveabout.com/causes-and-consequences-of-distracted-driving-527085.
  * [7] T. Choudhari and A. Maji, “Risk Assessment of Horizontal Curves Based on Lateral Acceleration Index: A Driving Simulator-Based Study,” Transp. Dev. Econ., vol. 7, no. 1, pp. 1-11, Apr. 2021, doi: 10.1007/s40890-020-00111-2.
  * [8] N. Kinnear and A. Stevens, “The battle for attention Driver distraction - a review of recent research and knowledge,” 2015, Accessed: May 25, 2021. [Online]. Available: https://trid.trb.org/view/1377052.
  * [9] C. Li, S. H. Chan, and Y. T. Chen, “Who make drivers stop? Towards driver-centric risk assessment: Risk object identification via causal inference,” in IEEE International Conference on Intelligent Robots and Systems, Oct. 2020, pp. 10711-10718, doi: 10.1109/IROS45743.2020.9341072.
  * [10] J. Michaels et al., “Driving simulator scenarios and measures to faithfully evaluate risky driving behavior: A comparative study of different driver age groups,” PLoS One, vol. 12, no. 10, Oct. 2017, doi: 10.1371/journal.pone.0185909.
  * [11] J. Liu, F. Jin, Q. Xie, and M. Skitmore, “Improving risk assessment in financial feasibility of international engineering projects: A risk driver perspective,” Int. J. Proj. Manag., vol. 35, no. 2, pp. 204-211, 2017, doi: 10.1016/j.ijproman.2016.11.004.
  * [12] J. L. Yin and B. H. Chen, “An Advanced Driver Risk Measurement System for Usage-Based Insurance on Big Driving Data,” IEEE Trans. Intell. Veh., vol. 3, no. 4, pp. 585-594, 2018, doi: 10.1109/TIV.2018.2874530.
  * [13] H. Mao, F. Guo, X. Deng, and Z. R. Doerzaph, “Decision-adjusted driver risk predictive models using kinematics information,” Accid. Anal. Prev., vol. 156, 2021, doi: 10.1016/j.aap.2021.106088.
  * [14] A. Tefferi and A. M. Vannucchi, “Genetic Risk Assessment in Myeloproliferative Neoplasms,” Mayo Clinic Proceedings, vol. 92, no. 8. pp. 1283-1290, 2017, doi: 10.1016/j.mayocp.2017.06.002.
  * [15] J. D. Paradis, “Reporting on risk: A journalist’s handbook on environmental risk assessment,” J. Crim. Justice, vol. 23, no. 4, p. 391, 1995, doi: 10.1016/0047-2352(95)90043-8.
  * [16] K. Berdica, “An introduction to road vulnerability: What has been done, is done and should be done,” Transp. Policy, vol. 9, no. 2, pp. 117-127, 2002, doi: 10.1016/S0967-070X(02)00011-2.
  * [17] K. Berdica and L. G. Mattsson, “Vulnerability: A model-based case study of the road network in Stockholm,” in Advances in Spatial Science, no. 9783540680550, Springer International Publishing, 2007, pp. 81-106.
  * [18] W. ElDessouki, J. Ivan, and E. Anagnostou, “Using relative risk analysis to improve connecticut freeway traffic safety under adverse weather conditions,” 2004. Accessed: May 25, 2021. [Online]. Available: https://www.researchgate.net/profile/Adel Sadek/publication/242233668 USING RELATIVE RISK ANALYSIS TO IMPROVE CONNECTICUT FREEWAY TRAFFIC SAFETY UNDER ADVERSE WEATHER CONDITIONS/links/56eb0ba508aec6b50016808a.pdf.
  * [19] X. Cai, C. Wang, S. Chen, and J. Lu, “Model development for risk assessment of driving on freeway under rainy weather conditions,” PLoS One, vol. 11, no. 2, Feb. 2016, doi: 10.1371/journal.pone.0149442.
  * [20] C. Dong, C. C. Loy, K. He, and X. Tang, “Image Super-Resolution Using Deep Convolutional Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 2, pp. 295-307, 2016, doi: 10.1109/TPAMI.2015.2439281.
  * [21] Z. Hu, N. Uchida, Y. Wang, and Y. Dong, “Face orientation estimation for driver monitoring with a single depth camera,” in IEEE Intelligent Vehicles Symposium, Proceedings, Aug. 2015, vol. 2015-Augus, pp. 958-963, doi: 10.1109/IVS.2015.7225808.
  * [22] K. Sato, M. Ito, H. Madokoro, and S. Kadowaki, “Driver body information analysis for distraction state detection,” in 2015 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2015, Feb. 2016, pp. 13-18, doi: 10.1109/ICVES.2015.7396886.
  * [23] A. Rasouli, I. Kotseruba, and J. K. Tsotsos, “Understanding Pedestrian Behavior in Complex Traffic Scenes,” IEEE Trans. Intell. Veh., vol. 3, no. 1, pp. 61-70, Mar. 2018, doi: 10.1109/TIV.2017.2788193.
  * [24] A. Fasanmade et al., “A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition,” IEEE Access, vol. 8, pp. 95197-95207, 2020, doi: 10.1109/ACCESS.2020.2994811.
  * [25] N. Das, E. Ohn-Bar, and M. M. Trivedi, “On Performance Evaluation of Driver Hand Detection Algorithms: Challenges, Dataset, and Metrics,” in IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2015, vol. 2015-Octob, pp. 2953-2958, doi: 10.1109/ITSC.2015.473.
  * [26] C. Dong, C. C. Loy, K. He, and X. Tang, “Learning a deep convolutional network for image super-resolution,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8692 LNCS, no. PART 4, pp. 184-199, doi: 10.1007/978-3-319-10593-2 13.
  * [27] T. H. N. Le, C. Zhu, Y. Zheng, K. Luu, and M. Savvides, “Robust hand detection in Vehicles,” in Proceedings - International Conference on Pattern Recognition, Jan. 2016, vol. 0, pp. 573-578, doi: 10.1109/ICPR.2016.7899695.
  * [28] T. H. N. Le, K. G. Quach, C. Zhu, C. N. Duong, K. Luu, and M. Savvides, “Robust Hand Detection and Classification in Vehicles and in the Wild,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2017, vol. 2017-July, pp. 1203-1210, doi: 10.1109/CVPRW.2017.159.
  * [29] B. Kapitaniak, M. Walczak, M. Kosobudzki, Z. Jóźwiak, and A. Bortkiewicz, “Application of eye-tracking in drivers testing: A review of research,” International Journal of Occupational Medicine and Environmental Health, vol. 28, no. 6. pp. 941-954, 2015, doi: 10.13075/ijomeh.1896.00317.
  * [30] W. Yuan, Z. Liu, and R. Fu, “Predicting drivers’ eyes-off-road duration in different driving scenarios,” Discret. Dyn. Nat. Soc., vol. 2018, 2018, doi: 10.1155/2018/3481628.
  * [31] B. G. Simons-Morton, F. Guo, S. G. Klauer, J. P. Ehsani, and A. K. Pradhan, “Keep your eyes on the road: Young driver crash risk increases according to duration of distraction,” J. Adolesc. Heal., vol. 54, no. 5 SUPPL., pp. S61-S67, 2014, doi: 10.1016/j.jadohealth.2013.11.021.
  * [32] P. Doshi, D. Kapur, R. Iyer, and A. Chatterjee, “Smart mobility: Algorithm for road and driver type determination,” in 2017 IEEE Transportation Electrification Conference, ITEC-India 2017, Apr. 2018, vol. 2018-Janua, pp. 1-4, doi: 10.1109/ITEC-India.2017.8333895.
  * [33] C. Chai, R. Zhao, J. Shi, J. Zhou, and Z. Yang, “The effect of road environment and type of vehicles on road rage in China,” in Proceedings - 2019 12th International Symposium on Computational Intelligence and Design, ISCID 2019, 2019, pp. 200-203, doi: 10.1109/ISCID.2019.10129.
  * [34] J. E. Meseguer, C. T. Calafate, J. C. Cano, and P. Manzoni, “DrivingStyles: A smartphone application to assess driver behavior,” in Proceedings - International Symposium on Computers and Communications, 2013, pp. 535-540, doi: 10.1109/ISCC.2013.6755001.
  * [35] A. Rakotonirainy, “Design of context-aware systems for vehicles using complex system paradigms,” in CEUR Workshop Proceedings, 2005, vol. 158, Accessed: May 25, 2021. [Online]. Available: http://eprints.qut.edu.au.
  * [36] I. Khan and S. Khusro, “Towards the Design of Context-Aware Adaptive User Interfaces to Minimize Drivers’ Distractions,” Mob. Inf. Syst., vol. 2020, 2020, doi: 10.1155/2020/8858886.
  * [37] X. Jianfeng, G. Hongyu, T. Jian, L. Liu, and L. Haizhu, “A classification and recognition model for the severity of road traffic accident,” Eco-Mobility Futur. Cities-Research Artic. Adv. Mech. Eng., vol. 11, no. 5, pp. 1-8, May 2019, doi: 10.1177/1687814019851893.
  * [38] F. Malin, I. Norros, and S. Innamaa, “Accident risk of road and weather conditions on different road types,” Accid. Anal. Prev., vol. 122, pp. 181-188, Jan. 2019, doi: 10.1016/j.aap.2018.10.014.
  * [39] L. A. Sherretz and B. C. Farhar, “An Analysis of the Relationship Between Rainfall and the Occurrence Of Traffic Accidents,” J. Appl. Meteorol., vol. 17, no. 5, pp. 711-715, 1978, doi: 10.1175/1520-0450(1978)017¡0711:aaotrb¿2.0.co;2.
  * [40] R. Bergel-Hayat, M. Debbarh, C. Antoniou, and G. Yannis, “Explaining the road accident risk: Weather effects,” Accid. Anal. Prev., vol. 60, pp. 456-465, 2013, doi: 10.1016/j.aap.2013.03.006.
  * [41] H. Brodsky and A. S. Hakkert, “Risk of a road accident in rainy weather,” Accid. Anal. Prev., vol. 20, no. 3, pp. 161-176, 1988, doi: 10.1016/0001-4575(88)90001-2.
  * [42] J. Andrey and B. Mills, Collisions, casualties and costs: weathering the elements on Canadian roads, no. 33. 2003.
  * [43] “2-Second Rule for Distracted Driving Can Mean Life or Death - The New York Times.” https://www.nytimes.com/2018/09/27/business/distracted-driving-auto-industry.html (accessed May 25, 2021).
  * [44] C. Wu, D. Yu, A. Doherty, T. Zhang, L. Kust, and G. Luo, “An investigation of perceived vehicle speed from a driver’s perspective,” PLoS One, vol. 12, no. 10, Oct. 2017, doi: 10.1371/journal.pone.0185347.
  * [45] E. de Bellis, M. Schulte-Mecklenbeck, W. Brucks, A. Herrmann, and R. Hertwig, “Blind haste: As light decreases, speeding increases,” PLoS One, vol. 13, no. 1, Jan. 2018, doi: 10.1371/journal.pone.0188951.
  * [46] A. Bener, T. Özkan, and T. Lajunen, “The Driver Behaviour Questionnaire in Arab Gulf countries: Qatar and United Arab Emirates,” Accid. Anal. Prev., vol. 40, no. 4, pp. 1411-1417, 2008, doi: 10.1016/j.aap.2008.03.003.
  * [47] A. Mishra and P. Bajaj, “Driver’s Behaviour Monitoring on Urban Roads of a Tier 2 City in India,” in International Conference on Emerging Trends in Engineering and Technology, ICETET, Mar. 2016, vol. 2016-March, pp. 134-140, doi: 10.1109/ICETET.2015.13.
  * [48] A. Kumar and R. Patra, “Driver drowsiness monitoring system using visual behaviour and machine learning,” in ISCAIE 2018 - 2018 IEEE Symposium on Computer Applications and Industrial Electronics, 2018, pp. 339-344, doi: 10.1109/ISCAIE.2018.8405495.
  * [49] E. S. Lee and D. Kum, “Feature-based lateral position estimation of surrounding vehicles using stereo vision,” in IEEE Intelligent Vehicles Symposium, Proceedings, Jul. 2017, pp. 779-784, doi: 10.1109/IVS.2017.7995811.
  * [50] D. Xu, H. Zhao, F. Guillemard, S. Geronimi, and F. Aioun, “Scene-Aware driver state understanding in car-following behaviors,” in IEEE Intelligent Vehicles Symposium, Proceedings, Jul. 2017, pp. 1490-1496, doi: 10.1109/IVS.2017.7995920.
  * [51] U. Mittal, R. Potnuru, and P. Chawla, “Vehicle Detection and Classification using Improved Faster Region Based Convolution Neural Network,” in ICRITO 2020 - IEEE 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), 2020, pp. 511-514, doi: 10.1109/ICRITO48877.2020.9197805.
  * [52] J. Gong, J. Zhao, F. Li, and H. Zhang, “Vehicle detection in thermal images with an improved yolov3-tiny,” in Proceedings of 2020 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2020, 2020, pp. 253-256, doi: 10.1109/ICPICS50287.2020.9201995.
  * [53] R. A. Kharjul, V. K. Tungar, Y. P. Kulkarni, S. K. Upadhyay, and R. Shirsath, “Real-Time pedestrian detection using SVM and AdaBoost,” in International Conference on Energy Systems and Applications, ICESA 2015, 2016, pp. 740-743, doi: 10.1109/ICESA.2015.7503447.
  * [54] K. Tateiwa and K. Yamada, “Estimating driver awareness of pedestrians in crosswalk in the path of right or left turns at an intersection from vehicle behavior,” in IEEE Intelligent Vehicles Symposium, Proceedings, 2015, vol. 2015-Augus, pp. 952-957, doi: 10.1109/IVS.2015.7225807.
  * [55] K. Tateiwa, A. Nakamura, and K. Yamada, “Study on estimating driver awareness of pedestrians while turning right at intersection based on vehicle behavior utilizing driving simulator,” in IEEE Intelligent Vehicles Symposium, Proceedings, 2016, vol. 2016-Augus, pp. 388-393, doi: 10.1109/IVS.2016.7535415.
  * [56] A. Rangesh, O. B. Eshed, K. Yuen, and M. M. Trivedi, “Pedestrians and their phones - Detecting phone-based activities of pedestrians for autonomous vehicles,” in IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2016, pp. 1882-1887, doi: 10.1109/ITSC.2016.7795861.
  * [57] P. Polack, F. Altche, B. DAndrea-Novel, and A. De La Fortelle, “The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles?,” in IEEE Intelligent Vehicles Symposium, Proceedings, 2017, pp. 812-818, doi: 10.1109/IVS.2017.7995816.
  * [58] D. D. Clarke, P. Ward, C. Bartle, and W. Truman, “Young driver accidents in the UK: The influence of age, experience, and time of day,” Accid. Anal. Prev., vol. 38, no. 5, pp. 871-878, Sep. 2006, doi: 10.1016/j.aap.2006.02.013.
  * [59] A. F. Williams, “Teenage drivers: Patterns of risk,” in Journal of Safety Research, Jan. 2003, vol. 34, no. 1, pp. 5-15, doi: 10.1016/S0022-4375(02)00075-0.
  * [60] D. Sommer and M. Golz, “Evaluation of PERCLOS based current fatigue monitoring technologies,” in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC’10, 2010, pp. 4456-4459, doi: 10.1109/IEMBS.2010.5625960.
  * [61] H. Ginting, G. Näring, and E. S. Becker, “Attentional bias and anxiety in individuals with coronary heart disease,” Psychol. Heal., vol. 28, no. 11, pp. 1306-1322, Nov. 2013, doi: 10.1080/08870446.2013.803554.
  * [62] O. Lopez-Fernandez, M. Freixa-Blanxart, and M. L. Honrubia-Serrano, “The problematic internet entertainment use scale for adolescents: Prevalence of problem internet use in Spanish high school students,” Cyberpsychology, Behav. Soc. Netw., vol. 16, no. 2, pp. 108-118, Feb. 2013, doi: 10.1089/cyber.2012.0250.
  * [63] G. B. Casal, J. Carlos, S. Normas, P. La, and D. E. C. Cl, “International Journal of Clinical and Health Psychology,” International Journal of Clinical and Health Psychology, vol. 2, no. 1. pp. 525-532, 2002, Accessed: May 25, 2021. [Online].
  * [64] T. C. CHIVERS, W. J. ROGERS, and M. E. WILLIAMS, “A TECHNIQUE FOR THE MEASUREMENT OF GAS-LEAKAGE.,” psycnet.apa.org. 1974, Accessed: May 25, 2021. [Online]. Available: https://psycnet.apa.org/record/1933-01885-001.
  * [65] C. León-Mantero, J. C. Casas-Rosal, C. Pedrosa-Jesús, and A. Maz-Machado, “Measuring attitude towards mathematics using Likert scale surveys: The weighted average,” PLoS One, vol. 15, no. 10 October, Oct. 2020, doi: 10.1371/journal.pone.0239626.
  * [66] J. F. Liu, Y. F. Su, M. K. Ko, and P. N. Yu, “Development of a vision-based driver assistance system with lane departure warning and forward collision warning functions,” in Proceedings - Digital Image Computing: Techniques and Applications, DICTA 2008, 2008, pp. 480-485, doi: 10.1109/DICTA.2008.78.
  * [67] “TeleFOT - UK DFOT3 - FOT-Net WIKI.” https://wiki.fot-net.eu/index.php/TeleFOT - UK DFOT3 (accessed May 25, 2021).
  * [68] M. M. Kunt, I. Aghayan, and N. Noii, “Prediction for traffic accident severity: Comparing the artificial neural network, genetic algorithm, combined genetic algorithm and pattern search methods,” Transport, vol. 26, no. 4, pp. 353-366, Dec. 2011, doi: 10.3846/16484142.2011.635465.
  * [69] J. Morton, T. A. Wheeler, and M. J. Kochenderfer, “Analysis of Recurrent Neural Networks for Probabilistic Modeling of Driver Behavior,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 5, pp. 1289-1298, 2017, doi: 10.1109/TITS.2016.2603007.
  * [70] M. Zhu, Y. Li, and Y. Wang, “Design and experiment verification of a novel analysis framework for recognition of driver injury patterns: From a multi-class classification perspective,” Accid. Anal. Prev., vol. 120, pp. 152-164, Nov. 2018, doi: 10.1016/j.aap.2018.08.011.
  * [71] Y. Ma, G. Gu, B. Yin, S. Qi, K. Chen, and C. Chan, “Support vector machines for the identification of real-time driving distraction using in-vehicle information systems,” J. Transp. Saf. Secur., 2020, doi: 10.1080/19439962.2020.1774019.
  * [72] T. Liu, Y. Yang, G. Bin Huang, Y. K. Yeo, and Z. Lin, “Driver Distraction Detection Using Semi-Supervised Machine Learning,” IEEE Trans. Intell. Transp. Syst., vol. 17, no. 4, pp. 1108-1120, Apr. 2016, doi: 10.1109/TITS.2015.2496157.
  * [73] Carsten, O. M. J., & Brookhuis, K. (2005). Issues arising from the HASTE experiments. Transportation Research Part F: Traffic Psychology and Behaviour, 8(2), 191-196.