Promoting pedestrian safety in Bangladesh: Identifying factors for drivers’ yielding behavior at designated crossings using behavior change theories

Objective In Bangladesh, drivers’ failure to yield to pedestrians at designated crossings poses a significant safety risk and discourages their use of such crossings. The use of behavior change theories could be more appropriate in such complex situations where the interdependent behaviors of drivers and pedestrians interact. While many studies have identified factors that affect drivers’ yielding behavior in the literature, fewer efforts have been made to apply behavior change theories in exploring and validating these factors, and to reach a consensus among competing road users. This study is among the first to utilize behavior change theories in Bangladesh to identify pedestrians’ safety factors that could promote drivers’ yielding behavior, upon which a consensus between drivers and pedestrians could be established. Methods A self-reported attitudinal survey was administered to 202 drivers on two highways in Bangladesh with a questionnaire using the capability, opportunity, motivation, and behavior (COM-B) model for the comprehensive coverage of behavior change theories. The focus group interviews were also conducted with 40 pedestrians and 19 drivers who have experience with four crossing sites on the selected highways. The collected data were analyzed using a regression model to identify significant factors influencing the drivers’ yielding behavior. These factors were then justified using a deductive thematic coding framework based on behavior change theories. Results The regression model explained the variance in drivers’ yielding by 45.1% with eight factors. The model found seven positive significant contributory factors in the drivers’ yielding that could promote pedestrian safety. Of them, the motivation factors were avoiding random crossing by pedestrians, vulnerable groups, assertiveness, and facial fear expressions; and the opportunity factors were traffic signs or advanced yield lines, crossing in groups at specific times, and enforcement. Conclusions The study’s findings have practical implications for policymakers, highway designers, and other stakeholders involved in promoting pedestrian safety by acknowledging their stake in making any decision that might impact them. Highway designers can use the thematic coding framework to recommend any contributory factors involved, where competing drivers’ unwillingness to yield is the primary threat to pedestrians’ safety

Motivations of pedestrians for safe use of highway crossing: an application of the behaviour change model COM-B in Bangladesh

In Low- and Middle-Income Countries (LMICs), approximately 93% of global road fatalities occur. As the population of students and workers in these countries continues to grow, walking becomes a prevalent mode of transportation for their daily commutes to schools and workplaces. Bangladesh faces the challenge of pedestrian fatalities, particularly among students and workers, while they cross medium-to-high speed roads during their daily journeys. This research aims to enhance highway crossing design and promote safe crossing behaviour in Bangladesh. The study utilises the COM-B (Capability, Opportunity, and Motivation-Behaviour) model to collect self-reported attitudinal responses from 302 pedestrians who regularly encounter different crossings, including zebra crossings, footbridges, underpasses, and non-priority crossings. These data collection sites are situated along two major highways in Bangladesh. The developed conceptual model in this study focuses on understanding the interplay between Capability, Opportunity, and Motivation, explaining 42% of the variance in the Target Behaviour of safe crossing use and 34.5% in Motivation. The analysis underscores the crucial role of Opportunity in predicting safe crossing use, followed by Motivation and Capability. Furthermore, the study examines the influence of COM-B factors on three essential components of the Target Behaviour: avoiding violations in using nearby crossings, aggressions, and lapses. The findings indicate that physical opportunity plays a vital role in avoiding violations in using nearby crossings, while social opportunity plays a vital role in avoiding aggressions and lapses. Motivation is a key mediator between Capability and Opportunity when predicting safe crossing use. To promote safe crossing practices, designers should focus on Motivation factors such as satisfaction, benefits realisation, and habit formation to maximise the benefits. The study emphasises the necessity for comprehensive interventions, which involve designing pedestrian-friendly infrastructure through various measures. These measures include improving visibility, reducing crossing times, ensuring accessibility, strategically placing traffic signs and fencing, and incorporating refuge areas. Additionally, the study highlights the significant role of social opportunities in safe crossing use by considering appropriate strategies to leverage social elements to motivate pedestrians by involving influential individuals, collaborating with families and institutions, facilitating group crossings, and implementing safety alert reminders. Moreover, social elements impact pedestrians' physical and psychological capabilities for safe crossing practice, as revealed in the study. Overall, the study highlights the potential of the COM-B model and underscores the need for comprehensive interventions to enhance pedestrian safety in LMICs

Risk-based autonomous vehicle motion control with considering human driver’s behaviour

The selected motions of autonomous vehicles (AVs) are subject to the constraints from the surrounding traffic environment, infrastructure and the vehicle’s dynamic capabilities. Normally, the motion control of the vehicle is composed of trajectory planning and trajectory following according to the surrounding risk factors, the vehicles’ capabilities as well as tyre/road interaction situations. However, pure trajectory following with a unique path may make the motion control of the vehicle be too careful and cautious with a large amount of steering effort. To follow a planned trajectory, the AVs with the traditional path-following control algorithms will correct their states even if the vehicles have only a slight deviation from the desired path or the vehicle detects static infrastructure like roadside trees. In this case, the safety of the AVs can be guaranteed to some degree, but the comfort and sense of hazards for the drivers are ignored, and sometimes the AVs have unusual motion behaviours which may not be acceptable to other road users. To solve this problem, this study aims to develop a safety corridor-based vehicle motion control approach by investigating human-driven vehicle behaviour and the vehicle’s dynamic capabilities. The safety corridor is derived by the manoeuvring action feedback of actual drivers as collected in a driving simulator when presented with surrounding risk elements and enables the AVs to have safe trajectories within it. A corridor-based Nonlinear Model Predictive Control (NMPC) has been developed which controls the vehicle state to achieve a smooth and comfortable trajectory while applying trajectory constraints using the safety corridor. The safety corridor and motion controller are assessed using four typical scenarios to show that the vehicle has a human-like or human-oriented behaviour which is expected to be more acceptable for both drivers and other road users

Driver-centred Autonomous Vehicle Motion Control within A Blended Corridor

As a potential cornerstone of the future intelligent transport system, autonomous vehicles (AVs) attract much attention of researchers across a wide range of areas from engineering to computer science. In addition, human factors issues, with respect to transfer of control and the interaction between the AVs and other road users have been studied. Current AV control algorithm development has focused on improving the safety of the vehicle, while the comfort of the drivers are normally ignored. Therefore, motion planning must not only avoid collisions between the vehicle and other road users and the road edges, but also needs to provide a sense of security and comfort for the drivers. Moreover, strict lane following can lead to overly cautious AVs relative to other road users, and thereby lead to traffic accidents. To solve these problems, we estimated the acceptable tolerance of the lateral offset based on the measured driving performance of real drivers and their reaction to a range of risk elements. Together with the vehicle dynamic constraints, the risk-based constraints are incorporated into a nonlinear Model Predictive Control (MPC) controller using a blended corridor. The result is a vehicle trajectory that produces a smooth motion within the corridor that considers the drivers’ comfort

Human-like Decision Making and Motion Control for Smooth and Natural Car Following

Car-following is an important driving behaviour for intelligent vehicles and has a significant impact on traffic efficiency and traffic safety. Car-following models are widely developed to characterize the human-drivers car-following manoeuvre actions and adopted in traffic simulation and automated vehicle control system development. Car-following models need to be able to represent the drivers behaviour while following preceding vehicles. On the other hand, car-following controllers are an important component of intelligent vehicle systems, both for autonomous vehicles and connected vehicles. However, Adaptive Cruise Control (ACC) as well as Cooperative Adaptive Cruise Control (CACC) do not include human behaviour, which makes their car-following behaviour not human-like or natural for the on-board driver or passenger. To address this problem, in this study, the human-like Wiedemann car-following model is calibrated and verified with our driving simulator data. A human-like car-following nonlinear model predictive control (MPC) controller is developed based on the calibrated car-following model. Three different scenarios are tested to evaluate the performance of the proposed controller, with which the autonomous vehicle is able to have human-like and smooth trajectories at different phases and within different transition zones

Measuring Drivers’ Physiological Response to Different Vehicle Controllers in Highly Automated Driving (HAD): Opportunities for Establishing Real-Time Values of Driver Discomfort

This study investigated how driver discomfort was influenced by different types of automated vehicle (AV) controllers, compared to manual driving, and whether this response changed in different road environments, using heart-rate variability (HRV) and electrodermal activity (EDA). A total of 24 drivers were subjected to manual driving and four AV controllers: two modelled to depict “human-like” driving behaviour, one conventional lane-keeping assist controller, and a replay of their own manual drive. Each drive lasted for ~15 min and consisted of rural and urban environments, which differed in terms of average speed, road geometry and road-based furniture. Drivers showed higher skin conductance response (SCR) and lower HRV during manual driving, compared to the automated drives. There were no significant differences in discomfort between the AV controllers. SCRs and subjective discomfort ratings showed significantly higher discomfort in the faster rural environments, when compared to the urban environments. Our results suggest that SCR values are more sensitive than HRV-based measures to continuously evolving situations that induce discomfort. Further research may be warranted in investigating the value of this metric in assessing real-time driver discomfort levels, which may help improve acceptance of AV controllers

Achieving Driving Comfort of AVs by Combined Longitudinal and Lateral Motion Control

As automated vehicles (AVs) are moving closer to practical reality, one of the problems that needs to be resolved is how to achieve an acceptable and natural risk management behaviour for the on-board users. Cautious automated driving behaviour is normally demonstrated during the AV testing, by which the safety issue between the AV and other road users or other static risk elements can be guaranteed. However, excessive cautiousness of the AVs may lead to traffic congestion and strange behaviour that will not be accepted by drivers and other road users. Human-like automated driving, as an emerging technique, has been concentrated on mimicking a human driver’s behaviour in order that the behaviour of the AVs can provide an acceptable behaviour for both the drivers (and passengers) and the other road users. The human drivers’ behaviour was obtained through simulator based driving and this study developed a nonlinear model predictive control to optimise risk management behaviour of AVs by taking into account human-driven vehicles’ behaviour, in both longitudinal and lateral directions

A comparison of two methodologies for subjective evaluation of comfort in automated vehicles

This paper compared two different methodologies, used in two driving simulator studies, for real-time evaluation of comfort imposed by the driving style of different Automated Vehicle (AV) controllers. The first method provided participants with two options for assessing three different AV controllers. Participants rated each controller in terms of whether or not it was comfortable/safe/natural, when it navigated a simulated road. The evaluation was either positive (yes) or negative (no), indicated by pressing one of two buttons on a handset. In the second study, an 11-point Likert-type scale (from -5 to +5) was used to evaluate the extent to which a controller’s driving style was “comfortable” and/or “natural”, separately. Participants provided this evaluation for three different AV controllers. Here, they were instructed to utter a number from the scale, at designated points during the drive. To understand which method is better for such evaluations, we compared the data collected from the two studies, and investigated the patterns of data obtained for the two methodologies. Results showed that, despite the multiple response options provided by the 11-point scale, a similar pattern was seen to that of the binary method, with more positive responses provided for all controllers. The Likert scale is useful for identifying differences because of the multiple levels of responses. However, allowing people to present their ratings as often as they want, also makes the binary technique useful for such evaluations

The Relationship Between Sensation Seeking And Speed Choice In Road Environments With Different Levels Of Risk

This paper presents the results of a driving simulator study conducted for the UK-funded HumanDrive project, which aims to develop natural, humanlike autonomous vehicle control. As part of that effort, this paper examines whether the established relationship between different sensation seeking (SS) traits and speed choice holds true across a range of driving scenarios, with different levels of contextual risk. Risk was introduced by varying a number of factors, including the environment (rural/urban), and the road edge context (low risk, static risk, potentially dynamic risk). Correlation analysis was performed between sensation seeking and the 95th percentile of vehicle speed for roads with different levels of risk, also considering age and gender. The results indicated that, overall, SS was significantly positively correlated with the 95th percentile of vehicle speed, and particularly for drivers under 40 years. SS was also found to correlate positively with speed choice at all risk levels, however, the effect was more pronounced in road environments that were classified as less risky. These findings have design implications for the development of autonomous vehicle control models