Driver Acceptance and Experience with Vulnerable Road User Collision Avoidance Systems while Operating Heavy Duty Vehicles


Caroll P. Lau received her MSc. Degree in Experimental Psychology and Neuroscience at the University of Guelph in 2016. Following her degree, she joined Transport Canada’s Multi-model and Road Safety Program division as a Human Factors Specialist. The research contributes to the development of international and national standards and guidelines.

Title of Abstract

Driver Acceptance and Experience with Vulnerable Road User Collision Avoidance Systems while Operating Heavy Duty Vehicles

Slide-deck and Full Paper

Acceptance and Experience of a Vulnerable Road User Detection System among Heavy Vehicle Operators - FEB 2020 FINAL (Full Paper)

CL_VRU_CARSP_Webinar2020_FINAL (Slidedeck)


Heavy-duty vehicles are often involved in fatal collisions with vulnerable road users (VRUs) due to the blind spots created by the design of these vehicles. The reduced visibility of VRUs surrounding heavy-duty vehicles put them at a high risk. As a countermeasure, Transport Canada conducted track tests to identify the best VRU collision warning system and equipped them on heavy-duty vehicles for field operational testing (FOT). The system aids the driver by monitoring blind spots and warning them when VRUs are at risk, which may allow drivers to detect VRUs in a timely manner and take appropriate actions to safely avoid collisions. Although these systems are designed to aid the driver, there are known challenges when it comes to system performance in real world conditions and driver acceptance of new technology.


The goal of this study was to measure system performance on extended use under real operating conditions in Canada and to evaluate driver acceptance of the system.


A survey was given to vehicle operators (n=49) several times during the year to measure their impressions overtime.


Overall descriptive statistics and counts were applied to the survey data. Based on self-report measures, the overall impression of the system was generally neutral where reports show no positive or negative impact of the system on driver workload and road safety. Furthermore, results show mixed responses on driver trust where the majority of the drivers reported having little to no trust in the system, despite reports showing that the system was extremely effective at capturing their attention (e.g., timing, light and audio). At the end of the FOT, operators were asked to participate in focus-groups to discuss their experience. Although the self-report responses were generally neutral, the responses obtained from the focus-group expressed a unanimous negativity towards the VRU collision avoidance system, which revealed that the system would consistently present false positive warnings. The abundance of false alerts increased workload in the environment, making the system more of a distraction than an aid for drivers.


There are known challenges when it comes to drivers’ acceptance of new technology designed to improve road safety. As evident from this study, we found that drivers were hesitant to trust the VRU collision avoidance system, despite added benefits of the system to monitoring blind spots and detecting VRUs. Furthermore, the system was reported to have no impact on drivers’ workload and road safety. Contrarily to the questionnaire surveys, the focus-group showed a unanimously negative experience towards the VRU collision avoidance system. This can mainly be attributed to frequent false positive warnings. The false alerts made the system a source of distraction rather than a reliable technology that supplements the driving task. Drivers however, responded positively towards the idea of other mitigation technologies. In order for advanced driver-assisted systems to be accepted and used by drivers, system performance must be reliable and accurate. In some cases, it must be customizable to the task at hand.


It is important to collect field data on system performance. It is also recommended to allow drivers to provide in-depth feedback in focus-group discussions.