# Contextual Intelligent Decisions: Expert Moderation of Machine Outputs for
Fair Assessment of Commercial Driving

Jimiama Mafeni Mase 1, Direnc Pekaslan1, Utkarsh Agrawal2, Mohammad
Mesgarpour,3
Peter Chapman 4, Mercedes Torres Torres1, Grazziela P. Figueredo1
1School of Computer Science, The University of Nottingham
2School of Medicine, University of St Andrews
4School of Psychology, The University of Nottingham
3Microlise, Farrington Way, Eastwood, Nottingham

###### Abstract

Commercial driving is a complex multifaceted task influenced by personal
traits and external contextual factors, such as weather, traffic, road
conditions, etc. Previous intelligent commercial driver-assessment systems do
not consider these factors when analysing the impact of driving behaviours on
road safety, potentially producing biased, inaccurate, and unfair assessments.
In this paper, we introduce a methodology (Expert-centered Driver Assessment)
towards a fairer automatic road safety assessment of drivers’ behaviours,
taking into consideration behaviours as a response to contextual factors. The
contextual moderation embedded within the intelligent decision-making process
is underpinned by expert input, comprising of a range of associated
stakeholders in the industry. Guided by the literature and expert input, we
identify critical factors affecting driving and develop an interval-valued
response-format questionnaire to capture the uncertainty of the influence of
factors and variance amongst experts’ views. Questionnaire data are modelled
and analysed using fuzzy sets, as they provide a suitable computational
approach to be incorporated into decision-making systems with uncertainty. The
methodology has allowed us to identify the factors that need to be considered
when moderating driver sensor data, and to effectively capture experts’
opinions about the effects of the factors. An example of our methodology using
Heavy Goods Vehicles professionals input is provided to demonstrate how the
expert-centred moderation can be embedded in intelligent driver assessment
systems.

###### Index Terms:

Contextual Factors, Driver Behaviour, Expert Systems, Fairness, Heavy Goods
Vehicle, Intelligent Driver Assessment, Uncertainty

##  I Introduction

Most intelligent driver-assessment and profiling systems for road safety use
individual sources of data, such as telematics [1, 2, 3, 4, 5], video footage
[6, 7, 8], phone usage [9, 10] and physiological signals [11, 12]. Multiple
sources of driver data used simultaneously have also been employed for more
robust assessments of driver behaviours [13, 14]. Those studies however do not
consider contextual characteristics of driving, such as drivers’ physical and
mental states, in-vehicle actions (e.g. operation of in-vehicle technologies),
weather conditions, traffic conditions, road geometry, road types, work
schedules, drivers’ reactions to events, other vehicles, and road users. These
factors impact drivers’ responses and are mostly not captured in driver data
[15, 16].

The influence of those contextual factors on driving should be utilised to
moderate data-driven, road safety assessments of driver behaviours, thereby,
producing fairer, explainable, and reliable intelligent driver behaviour
assessment systems [17]. For example, in Figueredo et al [18], the authors use
telematic incident data to identify top performing Heavy Goods Vehicle (HGV)
drivers in the United Kingdom (UK) in terms of safe and economic driving;
’Microlise Driver of the Year Awards’. However, they do not consider the
inevitable negative external factors (e.g. time pressure [19] and poor weather
conditions [20]) that could affect drivers during their journeys. This example
illustrates the danger of unfairness and the need for human moderation of the
endpoint data collected by assessing the circumstances affecting driving
behaviours that may lead to road incidents. Such intelligent systems that
consider context during processing and inference are crucial in ensuring
trust, acceptance, and successful adoption among stakeholders [21, 22].
Furthermore, in the era of big data streams, the human moderation should be
embedded into intelligent systems for automatic assessment as it is infeasible
to regulate the data manually [23].

Historically, questionnaires and surveys have been the main tools to capture
the influence of contextual factors on driving behaviours [24, 11, 25, 26,
27]. However, the approaches used in previous studies have the following
limitations:

  1. 1.

They do not include the input of stakeholders during the identification of
factors and the design of data collection tools. This is essential to ensure
that the factors identified are accurate and realistic to driving, and the
questionnaires are understandable and easy to complete [28];

  2. 2.

There is a bias towards the sole opinion of drivers, therefore neglecting
other stakeholders in the industry who may possess important qualitative
insights about the factors. In order to rely on subjective responses to
improve decision making, we need an inclusive system that considers the
viewpoints of all major stakeholders in the industry who are directed or
indirectly affected by the system’s outcomes [17];

  3. 3.

The complexities of driving, different interpretations of questions, different
levels of indecision, experience and expertise of participants increases the
levels of uncertainty and variability regarding expert input. An adequate
moderation system needs to account for this.

  4. 4.

They do not illustrate how the perceived effects of contextual factors could
be embedded into intelligent driver systems.

In this study, we present a methodology called Expert-centered Driver
Assessment (EDA) to identify, understand, and model the impact of contextual
factors on the safety of commercial driving. Guided by the literature and with
the help of stakeholders, we identify important factors that affect driving
both positively and negatively. Workshops with domain experts validate the
factors and the questionnaires designed. The questionnaires are later
distributed to gather expert views on how those factors affect driving. Fuzzy
Sets (FSs) based Interval Agreement Approach (IAA) [29] are used to model the
variance between expert views [30, 31]. The outcomes can then be incorporated
into intelligent decision systems [2]. We demonstrate our methodology by
investigating a case study for HGV driving in the UK.

##  II Background

###  II-A Capturing the effects of contextual factors

Questionnaires and surveys have been the main tools to capture the influence
of contextual factors on driving behaviours [24, 11, 25, 26, 27]. The tools
consist of discrete-valued response-format questions asking participants (a)
to rate the extent to which they agree with statements relating to how
different factors affect driving behaviours [11] ; (b) how often the
participants engage in risky driving [24, 25, 26]; and/or (c) rank factors
based on their contributions to accidents [27]. Examples of such discrete-
valued response-format scales are a seven-point Likert scale (from 1,
‘strongly disagree’, to 7, ‘strongly agree’) and a six-point scale (from 0,
‘never’, to 5, ‘all the time’) where participants are asked to select a single
response. The responses captured using these tools are analysed using
statistical techniques to identify statements with the highest agreement,
identify significant predictors of incidents or accidents, causation and
correlation amongst factors, e.g. Path analysis [32], p-value of means [20],
Logistic regression [33, 20], and Pearson Correlation [33, 11].

As mentioned in the Introduction Section, the above approaches are limited in:
1) their questionnaire design; 2) recruiting only drivers as participants; 3)
they do not consider the uncertainties of the effects of contextual factors
produced by complex relationships; 4) their analytical techniques do not
provide a clear representation and separation of experts’ opinions as
knowledge varies among experts; and 5) lack of incorporation of contextual
factors into intelligent driver systems. In this work, we address these gaps
by presenting a methodology that: 1) Includes domain experts during the
identification of important factors and the design of data collection tools to
ensure that the factors are up-to-date, accurate and realistic of driving, and
the questionnaire is understandable and easy to complete, 2) uses a wider
cohort of stakeholders for a comprehensive provision of the influence of
factors, 3) uses a recently proposed interval-valued response-format
questionnaire [34] to capture the uncertainties in the effects of factors, and
4) uses fuzzy sets to model and aggregate experts’ responses within the same
profession and across different professions. These fuzzy sets provide a clear
representation of the variability in the viewpoints of experts and can be
integrated into intelligent systems to regulate driver-assessment.

###  II-B Variability and uncertainty in knowledge

Information obtained from experts will likely contain uncertainty due to the
complex relationships between contextual factors and driving performance. For
example, the precise effect of driver-facing cameras on a driver’s performance
may be difficult to determine as the relationship between driver-facing
cameras and driving performance is not clear-cut due to interactive effects of
other factors such as, the type of intervention after monitoring. In addition,
knowledge varies even among experts within the same profession due to
different interpretations of questions, ambiguity of questions, different
levels of indecision, and different experiences [34]. Thus, it is intuitively
expected that different experts —even though they have similar roles— may
provide different answers to questions. When experts focus is changed, the
differences in the viewpoints of different professions may also arise, due to
their distinct responsibilities, roles and expectations [35]. For example a
group of researchers may focus on the aspect of scientific knowledge of road
safety by using experiments, whereas fleet managers may focus on optimising
the delivery of goods and services in their companies. Thus, different levels
of uncertainty exist in knowledge captured from a wide cohort of stakeholders,
which must be effectively modelled to provide a comprehensive, reliable and
clear representation of knowledge for decision making [30, 31].

Recently, a fuzzy computational technique called Interval Agreement Approach
(IAA) [29] is proposed to model and provide a clear representation of these
uncertainties in experts’ opinions by following a least commitment principle
[36] to prevent any loss of information.

###  II-C Interval Agreement Approach

IAA aims to accurately model information captured through the collection of
interval-valued data (e.g., through surveys). Generally, in IAA, decision
makers (experts) are asked to provide their views on factors influencing a
certain phenomena by using decision intervals, rather than a single-score
answer [29, 36]. An interval-valued question consists of participants
selecting a range (minimum and maximum values) to represent how certain they
are about their responses. The width of a given interval denotes experts’
certainty in their answer. For example, for a question assessing the impact of
a driver’s happiness to their performance (Fig. 1) we could have three
scenarios of expert responses. Figure 1 (a) shows a response where an expert
is certain about the strong positive impact of a driver feeling happy,
therefore giving a single score of $9$. Fig. 1 (b) shows a low uncertainty
response where an expert is little uncertain about the impact of a driver
feeling happy, therefore providing an answer with a narrow interval of values
between $7$ and $9$. Fig. 1 (c) represents an overall positive response but
with high uncertainty interval of the level of impact (between $5$ and $9$).

Figure 1: (a) No uncertainty interval-valued response , (b) Low uncertainty interval-valued response, and (c) Higher uncertainty interval-valued response. TABLE I: A sample of collected intervals from three experts (1-3) with the profession A | Expert 1 | Expert 2 | Expert 3
---|---|---|---
Profession A | [1,2] | [1,3] | [2,4]

IAA utilises collected intervals and generates non-parametric Fuzzy Sets (FSs)
to capture all different levels of uncertainty in individual opinions and also
between multiple individuals/groups opinions. During IAA FSs model creation,
first, the collected intervals are formed in Type-1 FSs (T1 FSs) which
minimises the loss of information in experts’ opinion. Secondly, these
generated T1 FSs are aggregated to z-slice Type-2 General FS (zGT2 FS) which
allows to model different individual opinions from different groups of
professions all together.

For example, as it is illustrated in Table I, Experts 1-3 -who are working in
the same profession A\- are asked a question and they provide intervals which
allow to capture uncertainty in their opinions. In IAA, these collected
intervals from each individual are aggregated into a single T1 FS. Thus, the
generated T1 FS is able to capture different opinions from experts (Expert
1-3) and model them in a single representation that shows the aggregated
opinions of experts from the same profession A, as shown in Fig. 2 (a). The
y-axis ($\mu(x)$) represents the level of agreement among the responses e.g.
the experts show greatest agreement in their responses at ‘2’.

Another group of experts (Expert 4-6) -who are working profession B\- are
asked the same questions and they provide different opinions with different
levels of uncertainty, as illustrated in Table II. These opinions can be
aggregated into another single T1 FSs which is shown in Fig 2 (b). As can be
seen in the comparison of Table I and II, the experts 4-6 tend to be more
uncertain about their opinions which leads to a wider T1 FS in Fig. 2b.

TABLE II: A sample of collected intervals from three experts (4-6) with the profession B | Expert 4 | Expert 5 | Expert 6
---|---|---|---
Profession B | [1,5] | [1.5,4] | [1,6]

Figure 2: T1 FSs for each profession using the IAA approach. (a) IAA-
Profession A, and (b) IAA-Profession B.

Later, the created T1 FSs are aggregated to generate zGT2 FSs where the
agreement/variation between multiple experts/groups of information is modelled
through the secondary memberships (zslices), as demonstrated in Fig. 3. The
darker area in the plot represents the region with higher agreement between
the two IAA FSs i.e. both groups agree the most in their responses between 1
and 4.

Figure 3: 2D view of the zGT2 FS produced with the IAA T1 FSs from Figure 2

For a detailed description of zGT2 FSs construction, please see the respective
study in [29]. The python libraries and functions used in this study to
develop the FSs can be found in [37].

Figure 4: Our novel methodology to capture and understand the effects of
contextual factors on commercial driving using a wide cohort of experts.

##  III Methodology

This section introduces our novel approach, EDA. EDA identifies, captures,
analysis and models the effects of contextual factors on driving performance
to be embedded into intelligent decision making systems. The methodology
consists of the following stages:

###  III-A Stage 1: Identify important contextual factors

This stage involves compiling a list of contextual factors that affect driving
performance based on literature. Subsequently, we organise consultative
workshops with domain experts who have experience in the industry and road
safety regulations. During the workshops, we present the factors obtained from
the literature and ask the experts to share their opinions on two questions:
“Are the contextual factors identified from the literature sensible and valid
in the current driving environment?” and “Is there any other contextual factor
we should consider?” These workshops are conducted to validate, update and
prioritise the factors obtained from the literature as some of the factors may
already be outdated due to advances in technologies and road safety
regulations. In addition, the experts assist in identifying the different
types of stakeholders in the industry (i.e. drivers, managers, researchers,
and road safety professionals), who will provide insights about the effects of
the factors.

###  III-B Stage 2: Designing questionnaires

After identifying and validating the factors in Stage 1, we proceed to design
interval-valued response-format questions (as described in Section II-C) to
collect the opinions of experts from the different types of stakeholders
identified in Stage 1 (also know as professions in this paper). We propose
interval-valued questions as they are powerful and natural means for
participants to communicate indecision or imprecision in their opinions [34].
The design and wordings of the questions are improved during the workshops
with the domain experts recruited in Stage 1. The experts are asked the
following questions: “Is the format and rating scale of the questionnaire easy
to comprehend and complete?”, “Are the wordings and questions
understandable?”, “Is the length of the questionnaire adequate considering the
busy schedules of professionals?”, and “Is there anything important missing in
the questionnaire?”. The final questionnaire is distributed to a wide cohort
of stakeholders to obtain their views about the impact of the contextual
factors.

###  III-C Stage 3: Understanding the factors and modelling their impact

In this stage, we gather the responses from the questionnaire and transform
each response into (minimum, maximum) interval format representing the range
of a response. For example, the responses for the questions in Fig. 1 will be
transformed into tuples to represent the effect of ‘happy’ i.e., (9,9) for the
response in (a), (7,9) for (b), and (5,9) for (c). This transformation is
important for efficient visualisation and development of fuzzy sets. We adopt
the least commitment strategy [36] by using all responses in our analysis
(i.e. no outlier removal) as outliers may contain rich information and IAA
fuzzy sets are able to efficiently handle this information.

The transformed data are visualised using line graphs and box-plots to
understand the individual as well as group opinions respectively. We are
interested in understanding: 1) the difference in opinions across the
different professions, 2) the level of certainty in the responses of experts,
3) agreement amongst experts within each profession, and 4) most importantly,
the final impact of factors by combining the opinions of all experts.

We establish IAA fuzzy sets from the interval-valued responses of each factor
in each profession. The fuzzy sets account for any variability in the
responses of experts. Therefore, if there exist $n$ contextual factors and $i$
professions, $n\times i$ IAA fuzzy sets will be generated. We then use fuzzy
set similarity measure e.g., Jaccard similarity measure [38], to
quantitatively express the level of agreement among the opinions of the
different stakeholders.

To obtain the final impact of the factors on driving performance, we aggregate
the IAA FSs into zGT2 FSs [29] by employing the agreement principle in Wagner
et al [39] and associating a higher secondary membership (zLevel) to areas
where the IAA FSs overlap. That is, if $n\times i$ IAA FSs are generated for
$n$ contextual factors and $i$ professions, each FS representing a specific
factor is aggregated with their corresponding FSs to produce $n$ zGT2 FSs. The
secondary membership captures the agreement among the different professions
and the zGT2 FSs provide a clear representation and separation of the
individual types of uncertainty present in the data (as described in Section
II-B). The resulting zGT2 FSs will be integrated into intelligent driver-
assessment systems (as shown in Section V-D below).

##  IV Case Study

In this study, we apply EDA to HGV driving in the United Kingdom (UK) due to
the importance of HGVs in delivering goods and services across the nation.
First, we identify and capture the effects of contextual factors from HGV
professionals. Later, the collected experts’ opinions are modelled and
embedded into an intelligent HGV driver-assessment system to automatically
moderate decisions and outcomes. We present the experimental design of our
study in the following sections.

###  IV-A Workshops with HGV domain experts

First, we identified the important contextual factors of HGV driving from the
literature. Next, we organised five iterative workshops with domain experts to
validate the contextual factors identified from the literature and the
questionnaire design. In each iteration, the domain experts refined their
responses to the questions presented in Sections III-A and III-B about the
influencing factors, questionnaire design, format and instructions. These
workshops were held virtually due to COVID-19 restrictions between September
2020 and November 2020. We recruited nine domain experts consisting of a
university professor in Psychology specialised in driving behaviour, three HGV
fleet managers, and five researchers specialised in intelligent driver
assessment systems. We believe nine participants are sufficient because too
many participants would not be manageable, as there would be too many opinions
and potentially too much noise. To complement the lack of HGV drivers in the
workshops, we interviewed the first participants who completed the
questionnaire i.e. two HGV drivers, two researchers and two road safety
professionals, asking them if there were any other important factors missing
from the questionnaire and whether the design of the questionnaire was
appropriate. Using their responses, we updated the questionnaire.

###  IV-B Questionnaire

Our questionnaire consisted of interval-valued questions that asked
participants to provide their expert opinions or ratings about the impact of
the critical factors (elicited in the previous stages) on HGV driving
performance. Clear guidelines and instructions on how to answer the questions
and the purpose of using a nine-point interval-valued scale were provided i.e.
the possibility for participants to select two points representing the range
of certainty of their responses. The nine-point rating scale ranged from 1,
meaning ‘strong negative impact’, to 9, ‘strong positive impact’ and 5
representing ‘no impact’ as shown in Fig. 5.

Figure 5: Rating scale for questions in our study

###  IV-C Participant recruitment

The four stakeholders identified during the workshops with the domain experts
were fleet managers, road safety professionals, HGV drivers and researchers.
Fig 6 defines the roles of each stakeholder. HGV drivers are operators of the
vehicles and the main beneficiary of this study as we intend to understand the
influence of contextual factors to moderate the evaluation of their driving
behaviours. Road safety professionals enforce road safety regulations on road
users and the results of this study will assist them in developing adequate
traffic laws that take into consideration the contextual factors. Researchers
improve scientific knowledge of intelligent driver assistance systems and road
safety. The findings of this study will assist them in prioritising the
detection of high impact factors and optimising the regulation of decisions.
Fleet managers ensure their companies are compliant with road safety
regulations and manage drivers to optimise delivery of goods and services.
These managers will benefit from this study by adapting their driver-
assessment systems to include context. In addition, the managers can organise
workshops with their drivers regarding factors in which the managers and
drivers disagree in opinions.

Participants for our study were recruited by sending mass messages to
individuals on LinkedIn [40], University of Nottingham and Transport Study
Group (UTSG) whose job titles and expertise matched any of the different
stakeholders. No compensation was offered for participation.

Figure 6: Diagram showing the different types of stakeholders in the HGV
industry along with the definitions of their roles.

###  IV-D Sample

Table III provides a summary of the number of participants in the study and
the average years of experience. Ninety-three participants from the UK
completed the questionnaire. Among the participants were: 20 HGV drivers, 23
researchers, 24 fleet managers, and 26 road safety professionals. Years of
experience ranged from 3 to 46 years with average and standard deviations of
(M=22.90, SD=12.79) for HGV drivers, (M=17.33, SD=10.72) for researchers,
(M=11.04, SD=8.22) for fleet managers, and (M=20.81, SD=12.39) for road safety
professionals.

TABLE III: Distributions of participants, average years of experience, and
standard deviations of experience among the groups of stakeholders

| HGV drivers | Researchers | Fleet managers | Road Safety Prof
---|---|---|---|---
No. of Participants | 20 | 23 | 24 | 26
Avg. Experience (Yrs) | 22.90 | 17.33 | 11.04 | 20.81
Std. Experience (Yrs) | 12.79 | 10.72 | 8.22 | 12.39

###  IV-E Model selection and settings

We use IAA and zGT2 FSs [29] to model the responses and variability in
opinions among experts in the different professions. We employ Jaccard
similarity measure [38] to calculate the agreement in opinions amongst the
different professions. The Jaccard similarity measure is an efficient and
well-established method used to calculate similarity between fuzzy sets. It
calculates the cardinality of the intersection of two sets, divided by the
cardinality of the union of the two sets. The output value for the method lies
between 0 and 1, where 1 indicates total agreement and 0 indicates total
disagreement.

##  V Case Study Results

###  V-A Identification of contextual factors

Contextual factors identified from the literature [24, 11, 25, 26, 27, 41, 3]
(see Fig. 7) were presented to the experts in the workshops for validation.
Some factors were identified as irrelevant, such as vehicle characteristics,
while others were identified as outdated due to new road safety policies in
the UK, such as rest breaks. Other factors, such as time of the day and day of
the week were revised to start, mid and end of shifts, as HGV drivers start
their jobs at different times of the day and different days of the week. The
experts proposed additional positive related factors, driver affective states
and weather conditions that could affect drivers. Fig. 7 shows the updated
list of critical factors included in our study. Those eliminated during the
workshop are represented using strikethrough texts; additional factors arising
from our discussions with experts are in bold text. 17 additional factors to
those found in the literature were identified as a result of EDA stage 1
(Section III-A). A total of 31 factors were utilised to design the interval-
valued questionnaire111Survey link:
https://nottingham.onlinesurveys.ac.uk/driving-performance.

Figure 7: Contextual factors that affect HGV driving performance extracted
from the literature and validated by experts. The strikethrough factors were
eliminated by experts during a workshop while the bold texts represent
additional factors identified by the experts.

###  V-B Effects of contextual factors on HGV drivers’ performance

We used box-plots and line graphs to visualise the responses from experts. The
box-plots show the group distributions of responses while the line graphs show
the individual responses. Each line or dot in the line graphs represents the
(minimum, maximum) response of each expert and the colours represent the
different professions. If an expert’s response was very certain (i.e., they
selected a single value as their rating), a single point is plotted in the
line graph. Each line graph has 93 lines and points for all 93 experts. The
line graphs are found in the supplementary results section (Appendix). We show
the mode rather than the median in the box-plot distributions as we are
interested in the rating provided by majority of the experts in each
profession. We use the acronyms HD=HGV Driver, FM=Fleet Manager, R=Researcher,
RS= Road Safety professional, to represent the different professions on the
box-plots.

Figure 8: Box-plots showing the distribution of responses from drivers, fleet
managers, researchers and road safety professionals about the impact of
drivers’ affective states on their driving.

####  V-B1 Driver affective states

The box-plots in Fig. 8, reveal a less negative effect of feeling tired
(Fig.8d) suggested by researchers. This may be due to new road safety
regulations in the UK that require drivers to take frequent rest breaks [42].
Next, we notice that the majority of researchers suggest that being confident
(Fig.8j) or insecure (Fig.8k) has no impact on drivers’ performance (i.e. mode
= 5), which contradicts what majority of experts from the other professions
think. The other experts suggest that being confident has a positive impact,
while being insecure has a negative impact on performance.

The box-plots show that experts agree on how affective states influence
driving (i.e. positively or negatively). However, we observe variations among
professions with regards to the intensity of the influence of those factors.
These variations can be attributed to the distinct roles, knowledge,
experiences and goals of stakeholders. Such variations in opinions need to be
considered in the development of intelligent driver-assessment systems to
ensure that the systems are not biased towards particular stakeholders. This
is the main objective of stages 2 and 3 of EDA, where we collect responses
from a wide cohort of stakeholders and incorporate the consensus among their
views into intelligent systems.

Although not always easy to capture, some of the affective states brought out
by experts during our workshops can be automatically detected using machine
learning. Deep learning approaches applied to driver-facing footage, for
instance, have shown promising results in automatically identifying some of
the affective states, such as, distracted or attentive driving [43, 44],
different types of human emotions [45, 46], and tired or energetic [47, 48].
Alternatively, calm or aggressive driving is accurately detected using
telematics incident data [3, 2], while more complex affective states such as
confidence and insecurity are still difficult to detect.

####  V-B2 Work-life factors

Fig. 9 depicts the distributions of responses from experts about the impact of
work-life factors. We observe variation between the opinions of drivers
compared to fleet managers and road safety professionals for start and end of
a shift (Fig.9a,c) with the mode of drivers at 5. The majority of fleet
managers and road safety professionals indicate that the start of a shift has
a positive influence on driving and the end of a shift has a negative
influence. To better understand the cause of this variability, further
investigation and interviews with the different stakeholders is required.
Furthermore, time pressure for delivery (Fig.9e) is considered to have a
strong negative impact (mode = 1) by the majority of fleet managers, who
sometimes exert pressure on drivers to deliver on time [19]. This observation
stresses the need for moderation of driver’s performance, as HGV drivers face
pressure from their companies to deliver goods on time, leading to accidents
and deaths [49, 19]. It is unfair to ignore the pressure from their employers
when assessing driving behaviours, as their jobs are on the line if they do
not comply.

The time of shift (i.e. start, mid and end of shift) can be automatically
identified using job dispatch and routing management systems, while traffic
state (i.e. high or low) can be automatically detected from road-facing camera
images using computer vision techniques [50, 51] or obtained from location
based systems e.g., Google Maps.

Figure 9: Box-plots showing the distribution of responses from drivers, fleet
managers, researchers and road safety professionals about the impact of work
related factors.

####  V-B3 In-vehicle technologies

Fig. 10 depicts the distributions of responses from experts about the impact
of in-vehicle technologies. Drivers and road safety professionals disagree
about the impact of driver-facing cameras (Fig.10a) as majority of drivers
suggest driver-facing cameras have a negative impact on driving while road
safety professionals believe driver-facing cameras have a positive impact. In
addition, road safety professionals suggest that the absence of cameras
(Fig.10d) has a negative impact on driving while majority of other
stakeholders suggest no impact. The negative impact of driver-facing cameras
suggested by drivers may be a consequence of how the videos or images are used
(e.g. used to penalise drivers) or due to privacy concerns as we clearly
observe positive ratings by drivers for road-facing cameras, which are less
intrusive and personal.

A moderation system developed with only the opinions of drivers may be
inaccurate with regards to the effects of driver-facing cameras, as work such
as Mase et al [41] shows a significant reduction in driving incidents due to
both driver-facing and road-facing cameras. Such discrepancies in opinions can
only be identified when different stakeholders are considered; the motivation
of stage 2 of EDA. The discrepancies can be resolved by organising follow-up
interviews with stakeholders to understand their viewpoints or by combining
all opinions and finding a consensus (i.e. stage 3 of EDA).

Figure 10: Box-plots showing the distribution of responses from drivers, fleet
managers, researchers and road safety professionals about the impact of
technologies.

Figure 11: Box-plots showing the distribution of responses from drivers, fleet
managers, researchers and road safety professionals about the impact of
weather conditions.

####  V-B4 Weather conditions

Fig. 11 presents the distributions of responses on the impact of weather
conditions. The majority of drivers and fleet managers suggest a sunny weather
(Fig.11a) has a positive impact (mode = 7) while majority of researchers and
road safety professional suggest it has no impact(mode = 5). Similarly, the
majority of drivers indicate a rainy weather (Fig.11b) has no impact on
driving, while the other stakeholders believe it has a negative impact. The
weather conditions identified in this study can be automatically detected
using deep learning methods on road-facing images [52, 53, 54] or obtained
from online weather data sources such as the Metropolitan Police
UK222https://www.met.police.uk/.

###  V-C Agreement amongst the different stakeholders

In order to validate our rationale of using different types of stakeholders to
provide the effects of contextual factors (stage 2 of EDA), we calculate the
similarity measures between the different professions for each contextual
factor to show the agreement among opinions. The results are presented in Fig.
12, 13, 14 and 15 with similarity values between 0 and 1, where 0 indicates
total disagreement (red) and 1 indicates total agreement (green). We observe
from the figures that drivers, researchers and road safety professionals have
similarity values above 0.5 for several factors. However, we observe low
levels of agreement (below 0.5) between drivers and researchers in 18 out of
31 factors, with the highest disagreement in the absence of camera, and 17 out
of 31 factors between drivers and road safety professionals. Similarly, we
identify 17 out of 31 factors between researchers and road safety
professionals with agreement values less than 0.5. Furthermore, fleet managers
disagree the most with other stakeholders in the following number of factors:
27 between drivers, 30 between researchers, and 27 between road safety
professionals.

Figure 12: Agreement amongst stakeholders opinions for affective factors

Figure 13: Agreement amongst stakeholders opinions for work-life factors

Figure 14: Agreement amongst stakeholders opinions for in-vehicle technologies

Figure 15: Agreement amongst stakeholders opinions for weather conditions

Figure 16: The aggregation of IAA fuzzy sets for the impact of drivers feeling
‘sad’ on HGV driving obtained from drivers, fleet managers, researchers and
road safety professionals

The perceived large number of disagreements among stakeholders, especially
between fleet managers and other stakeholders, validates our hypothesis of
considering different stakeholders. However, by considering several
stakeholders to provide the influence of contextual factors, we are faced with
the question, ‘how do we combine the effects of factors with high disagreement
among stakeholders?’. In order to answer this question, we first develop IAA
FSs for the responses of experts from each profession. For instance, Fig. 16
shows IAA FSs of ‘feeling sad’ for HGV drivers, fleet managers, researchers
and road safety professionals. The IAA FSs model and provide a clear
visualisation of the variability in opinions among experts within the same
profession. Examining the IAA FSs of ‘feeling sad’ in Fig. 16, we observe that
opinions of experts in all four professions have some level of uncertainty
about the impact of feeling sad on HGV driving e.g. drivers’ IAA FS ranges
from 1 to 5 with the highest agreement at 3 (i.e. maximum of the FS), while
managers’ IAA FS ranges from 1 to 4 with an outlier at 7. Also, the FS of
managers is more skewed compared to the FS of drivers indicating that more
managers suggest ‘feeling sad’ has a strong negative impact compared to
drivers.

To obtain the final FSs representing the overall impact of factors, we
aggregate the IAA FSs into zGT2 FSs as shown in Fig. 16. By adopting the least
commitment strategy of information fusion [36], we assume equal weights for
all stakeholders and experts to enable easy adaptation of our framework in the
future. The resulting zGT2 FSs (Fig. 17, 18, 19 and 20) represent the
agreement and variability of all experts’ opinions across all professions. The
advantage of using the IAA approach in EDA is : 1) its generalisability to any
interval-valued dataset as no information is added or removed, and assumptions
are kept to a minimum such as the membership function type e.g. Gaussian or
Triangular, and 2) it provides interpretable representations of expert
knowledge and uncertainty levels. The shaded regions in the zGT2 FSs represent
the areas where the IAA FSs overlap, effectively weighting areas with high
agreement among professions. Dark areas represent high agreement among
professions. Thus, as shown in Fig. 16, the outlier at ‘7’ is given a low
weight denoted by the bright shade. This approach of weighting the impact of
factors according to the agreement among stakeholders illustrated by the zGT2
FSs provides a logical and visual solution to combine the opinions of
different stakeholders even with high disagreements.

Figure 17: Resulting General Type-2 Fuzzy Sets of affective states showing
variability in viewpoints among experts

Figure 18: Resulting General Type-2 Fuzzy Sets of weather conditions showing
variability in viewpoints among experts

Figure 19: Resulting General Type-2 Fuzzy Sets of work-life factors showing
variability in viewpoints among experts

Figure 20: Resulting General Type-2 Fuzzy Sets of in-vehicle technologies
showing variability in viewpoints among experts

###  V-D Embedding EDA into intelligent systems

TABLE IV: An example of assessing drivers’ risk-taking behaviour from their driving incidents | Driving incidents |  |
---|---|---|---
Frequency of | Over-Speed | Excessive Throttle | Frequency of | Risk level of | Risk score(%)
Harsh Braking | duration | duration | OverRevving events | driving behaviour |
Driver A | High | High | Moderate | Low | Very High | 83.09
Driver B | High | High | Moderate | Low | Very High | 83.09
Driver C | Low | High | High | Low | High | 75.24
Driver D | Low | High | High | Low | High | 75.24
TABLE V: An example of moderating drivers’ risk-taking behaviour using the effects of contextual factors | Risk score | Contextual factors | Risk level | Risk score
---|---|---|---|---
before moderation (%) | Time Pressure | Weather condition | after moderation | after moderation (%)
Driver A | 83.09 | High | Rainy | Moderate | 63.48
Driver B | 83.09 | Low | Rainy | Very High | 90.06
|  | Multi-cameras | Emotion |  |
Driver C | 75.24 | Absence | Energetic | High | 76.97
Driver D | 75.24 | Presence | Energetic | Very High | 83.59

In this section, we show the application of the outputs of EDA (GT2 FSs) in
moderating and explaining the assessment of driving behaviours using simple
examples from the HGV industry as shown in Table IV. We replicate a driver
risk-scoring system proposed by Mase et al [2]. The driver risk-scoring system
uses driving incidents (i.e. number of Harsh Braking (HB) events; Over Speed
(OS) duration in seconds; Excessive Throttle (ET) duration in seconds, and
number of Over Rev (ORev) events) from HGVs to classify drivers according to
the risk of their driving styles on a scale of 0 to 100, where 0 is a low risk
driver and 100 high risk. In Table IV, we present the risk category and score
of four drivers (A, B, C and D) whose driving incidents and scores were
simulated using the system. The labels ‘Low’, ‘Moderate’, ‘High’ and ‘Very
High’ describe the occurrence of driving incidents and level of risk. For
simplicity, we ignore the membership values of the labels by considering only
the labels with majority membership. For example, the number of HB events
produced by driver A is fuzzified as ‘Moderate’ with a membership value of 0.2
and as ‘High’ with a membership of 0.8; we select ‘High’ due to its higher
membership. We use examples with similar risk scores to demonstrate how the
impact of external factors could be used to regulate the assessments of driver
behaviours. Drivers A and B are classified as ‘Very High’ risk drivers with a
risk-score of 83.09% due to ‘High’ HB and ‘High’ OS incidents while drivers C
and D are classified as ‘High’ risk drivers with a risk-score of 75.24% due to
‘High’ OS and ’High’ ET incidents. These assessments produced by the risk-
scoring system are based solely on the number of incidents and do not consider
contextual factors.

Table V shows regulated driver risk-taking scores when Time Pressure, Multi-
cameras, Driver Affective State and Weather conditions are considered. We are
assuming Driver A was driving under ‘High’ time pressure while Driver B was
under ’Low’ time pressure from their managers, and both drivers were driving
under the same weather condition. Similarly, Driver C was driving in the
’Absence’ of cameras while Driver D in the ’Presence’ of cameras, and both
drivers were energetic. We use the GT2 FSs obtained for time pressure (see
Fig. 19), in-vehicle technologies (see Fig. 20), weather conditions (see
Fig.18), and affective state (see Fig.17) to understand and regulate the
effects of contextual factors on the drivers’ risk-taking behaviours as
follows:

  1. 1.

Defuzzify GT2 FSs: We defuzzify the FSs for high time pressure, low time
pressure, absence of cameras, presence of cameras, rainy and energetic using
centroid defuzzification [55] to produce crisp impact scores for the factors,
which represent the consensus of the experts’ opinions. These crisp scores
show how each factor affects a driver’s performance from 1 (strong negative
impact) to 9 (strong positive impact), and will be utilised to moderate their
driving risk scores. The defuzzified impact scores are: high time
pressure=2.45, low time pressure=7.55, absence of cameras=4.36, presence of
cameras=5.76, rainy=3.78, and energetic=6.02.

  2. 2.

Merge the impact scores: To merge the impact scores of the different
contextual factors, we employ the commonly used average or mean ensemble
method [56]. We assume joint effects of contextual factors. Other ensemble
methods that could be employed are minimum (which prioritise more negative
impact factors), weighted average voting (where each factor is assigned a
weight), fuzzy rules (where experts determine the combined effects of factors)
or Bayes ensemble (where prior information about certain factors are
considered) [56]. The joint effect of the contextual factors for Driver A =
mean(2.45, 3.78) = 3.11, Driver B = mean(7.55, 3.78) = 5.67, Driver C =
mean(4.36, 6.02) = 5.19, and Driver D = mean(5.76, 6.02) = 5.89.

  3. 3.

Moderate the assessment of driving behaviours: For simplicity, we could for
instance employ a product operator to moderate the assessment of driving
behaviours. First, we perform [x, y] normalisation of the joint effect, [x, y]
represents a sensible moderation domain. After several simulations of x and y,
we choose [0.5, 1.5] normalisation for this example as it seems most
appropriate to reduce or increase the risk score by half for strong negative
or positive influence respectively. It is important to note that [x, y] is
selected by the decision maker and it controls the moderation magnitude of the
decision or outcome. This results to Driver A’s contextual effect =
$norm(3.11)_{[0.5,1.5]}$ = 0.764, Driver B = $norm(5.67)_{[0.5,1.5]}$ = 1.084,
Driver C = $norm(5.19)_{[0.5,1.5]}$ = 1.023, and Driver D =
$norm(5.89)_{[0.5,1.5]}$ = 1.111. Lastly, we use the product operator to
regulate drivers’ risk-taking behaviours as follows:

$\displaystyle\text{A's moderated risk score}=83.09\%\times 0.764=63.48\%$
$\displaystyle\text{B's moderated risk score}=83.09\%\times 1.084=90.06\%$
$\displaystyle\text{C's moderated risk score}=75.24\%\times 1.023=76.97\%$
$\displaystyle\text{D's moderated risk score}=75.24\%\times 1.111=83.59\%$

The above examples illustrate how the assessment of HGV driving behaviours
could be fairly moderated to take into account the driving conditions and
external factors. Drivers A and B had the same driving risk score of 83.09%
(very high risk) due to their aggressive driving styles i.e. high HB and OS
incidents. Both drivers were driving in the same weather condition (rainy) but
under different time pressure to deliver their goods. We observe a significant
reduction in the risk-score of driver A after considering the inevitable high
time pressure. The results imply that driver A would have been of ‘Moderate’
risk if the driver was rather on ‘low’ time pressure compared to driver B who
was under ‘low’ time pressure and still obtained a ‘Very High’ risk score.
Similarly, drivers C and D had the same driving risk score of 75.24% (high
risk) due to their speeding driving styles i.e. high OS and ET incidents. Both
drivers were driving with the same affective state (energetic) but under
different in-vehicle technologies. The moderated scores imply that driver D
would have been more risky (risk score of 83.59%) without the presence of
multi-cameras. Therefore, with the impact of contextual factors (GT2 FSs)
embedded in intelligent systems, decision makers can clearly understand how
different factors may have influenced drivers’ behaviours to obtain fairer and
more reliable assessments of the drivers.

##  VI Conclusion

This study presents an Expert-centered Driver Assessment (EDA) methodology for
capturing the effects of contextual factors on commercial driving, and
embedding the opinions of experts into intelligent systems for automatic
moderation of driving behaviour assessment. EDA consists of: 1) the
identification of contemporary factors, 2) the effective design of data
collection tools, 3) the comprehensive collection, modelling and aggregation
of expert knowledge about the contemporary factors, and 4) the integration of
expert knowledge into intelligent systems. The application of EDA to HGV
driving in the UK produce the following results and insights:

  1. 1.

A significant number of additional contextual factors are identified by domain
experts.

  2. 2.

There exist variability in the opinions of experts within the same profession
indicating the need to recruiting a good number of experts for more
comprehensive decisions.

  3. 3.

There exist variability in the opinions of experts across different
professions indicating the need to consider and aggregate knowledge from a
wide variety of stakeholders for more reliable and fair decisions.

  4. 4.

The IAA FSs effectively model interval-valued responses without loss of
information and provide clear representations of the variability in experts’
opinions within the same professions.

  5. 5.

The GT2 FSs aggregate experts’ opinions from different professions and provide
clear visualisations of the degree of agreement among the different
professions.

  6. 6.

The resulting GT2 FSs can be efficiently embedded into intelligent systems to
moderate their outputs and provide interpretable decisions.

The study shows the importance of collective intelligence of stakeholders in
the driving community to identify, understand and embed the influence of
personal traits and external contextual factors when evaluating driving
behaviours. The all-inclusive approach assists in explaining the decisions of
intelligent systems and enables fairness and accountability in the assessment
of driving behaviours that lead to incidents or accidents. It is important to
note that our sample sizes (the number of participants recruited for the
workshops and completing the questionnaires) should be carefully considered in
making any statistical conclusions.

For future work, we plan to include additional contextual factors identified
during follow-up interviews with experts, such as, road geometry and foggy
weather. We also intend to recruit more experts to complete our questionnaire
to ensure more robust, comprehensive and statistical results. Lastly, we plan
to incorporate the fuzzy sets obtained from this study in the automatic
moderation of driver data for fair and explainable assessment of driving
behaviours.

## Acknowledgement

This work was supported by the Horizon Centre for Doctoral Training at the
University of Nottingham and Microlise (UKRI Grant No. EP/L015463/1), and the
participants who dedicated some time to complete our questionnaire. We also
acknowledge -Lisa Keogh and Neil Selby from Microlise and Shazmin Majid from
University of Nottingham- who helped during the study.

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|  Jimiama Mafeni Mase is a PhD candidate in Computer Science at the
University of Nottingham. His PhD explores the use of domain experts to fuse
and moderate intelligent systems’ decisions for a fair and comprehensive
assessment of commercial driving behaviours. His expertise includes
optimisation, data modelling, information fusion, computer vision, data stream
mining, anomaly detection, uncertainty and explainable AI. He is currently a
member of Computer Vision Lab (CVL) and the Lab for Uncertainty in Data and
Decision Making (LUCID) in the University of Nottingham.
---|---
|  Direnc Pekaslan received his MSc degree -with distinction- in the Advanced
Computer Science programme from the University of Sheffield, UK., in 2015. He
has awarded the PhD degree with the thesis title of ’Towards Better
Performance in the Face of Input Uncertainty while Maintaining
Interpretability in AI’ at the School of Computer Science, University of
Nottingham, UK., in 2021. He is currently a research fellow in the project
’Leveraging the Multi-Stakeholder Nature of Cyber Security’ on human centred
cybersecurity studies that explores novel approaches of capturing and
modelling uncertain data on the vulnerability of computer systems from a
variety of sources. He is currently a member of the Lab for Uncertainty in
Data and Decision Making (LUCID) and the Intelligent Modelling and Analysis
(IMA) research group. His main research interest includes uncertainty handling
with interpretable AI systems, particularly, focuses on providing adaptive
behaviours in non-singleton fuzzy models that can handle unexpected
circumstances in real-world applications.
---|---
|  Dr Utkarsh Agrawal received his PhD in Computer Science from the University
of Nottingham. He is currently a post-doctoral research fellow at University
of St Andrews. His research interests include the application of data
analytics and machine learning in real-world scenarios and his current focus
is on vaccine effectiveness and multimorbidity.
---|---
|  Dr Mohammad Mesgarpour received the PhD degree in operational research from
the University of Southampton in 2012. He is now head of Data Science and
Operational Research in Microlise. Prior to that, he was a KTP Research
Associate with The University of Nottingham for two years. His main areas of
research is in the fields of transport management, predictive modelling, data
analytics, and combinatorial optimisation.
---|---
|  Dr Peter Chapman is an Associate Professor, researching applied cognitive
psychology within the School of Psychology at the University of Nottingham.
His main areas of research are the psychology of driving, the understanding of
crashes and their causes (memory, attention, distraction, human error etc.),
visual search in novice and experienced drivers, ways of training newly
qualified drivers to use more effective visual search strategies, and eye
movements in dangerous driving situations.
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|  Dr Mercedes Torres Torres is an Assistant Professor of Computer Science.
She is a member of the Computer Vision Lab (CVL) and the Horizon Research
Institute. Her area of expertise is in machine learning and computer vision,
with particular emphasis in the development of novel deep learning techniques
for small and skewed datasets. Her research portfolio includes
multidisciplinary collaborations in areas like Healthcare (QMC), GIS (Dstl),
Cybersecurity (Netacea), and Children Protection Services (Nottingham City
Council), with funding from EPSRC (Horizon Research Institute, TAS Network,
EnLightenUs as Co- I), Innovate UK (KTP, PI), and Huawei (HIRP, PI). She has
published in high-impact conferences and journals, such as LREC, AIME, ESWA,
FG and IMAVIS.
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|  Dr Grazziela Figueredo is Assistant Professor in the School of Computer
Science and the Research and Innovation Lead at the Digital Research Service
at The University of Nottingham. The focus of her research is the development
and application of techniques for systems simulation and intelligent data
analysis for urban mobility and transportation. Grazziela has served as
Associate Editor of the IEEE Intelligent Transportation Systems conference and
has published over 20 papers in AI for transport research (in journals such as
IEEE Transactions on Intelligent Transportation Systems and Accident Analysis
and Prevention).
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Figure 21: line graphs showing the individual responses from experts about the
impact of HGV drivers’ affective states on their driving performance.

Figure 22: line graphs showing the individual responses from experts about the
impact of work related factors on HGV driving performance.

Figure 23: line graphs showing the individual responses from experts about the
impact of technologies on HGV driving performance.

Figure 24: line graphs showing the individual responses from experts about the
impact of weather conditions on HGV driving performance.