Expectations and Understanding of Advanced Driver Assistance Systems Among Drivers, Pedestrians, Bicyclists, and Public Transit Riders

Horrey, William J; Benson, Aaron; Guo, Zhaomiao; Afifah, Fatima; Hamann, Cara J.; Santiago, Kelvin R. · 2021 · ROSA P / Safety Research Using Simulation (SAFER-SIM) University Transportation Center

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Summary

This study investigates the perceptions, understanding, and expectations of Advanced Driver Assistance Systems (ADAS) among various road users, including drivers, pedestrians, bicyclists, and public transit riders. Motivated by the rapid advancement of vehicle automation and a noted research gap regarding non-driver road users, the authors sought to determine if these groups differ in their mental models of SAE Levels 1 and 2 technologies, their trust in specific use cases, and their outlook on future automated vehicles. The research highlights that while drivers are central to ADAS usage, other road users must safely interact with these vehicles, yet their understanding of system capabilities remains largely unexamined. The researchers conducted an online survey with 1,531 participants, classified by their primary mode of transportation. The instrument assessed knowledge of Adaptive Cruise Control (ACC) and Lane Keeping Assist (LKA), followed by responses to two specific scenarios: a bicyclist on a narrow shoulder and a pedestrian at a mid-block crosswalk. Participants rated their expectations of system behavior, trust, perceived risk, and intended actions both when ADAS was active and when the vehicle was under manual control. Statistical analyses, primarily non-parametric Kruskal-Wallis tests, compared responses across the four main road-user groups. Results indicated that understanding of ADAS was modest across all groups, ranging from 50% to 60% accuracy, with bicyclists demonstrating the highest comprehension. In the bicyclist scenario, bicyclists were more accurate in believing the vehicle would not automatically slow down, whereas pedestrians falsely believed sensors would detect them and that the vehicle would slow down in the pedestrian scenario. Non-drivers consistently reported lower trust in vehicle technology than drivers but did not differ in their appraisal of crash risk. However, when technology was removed, all groups trusted manual drivers more than automated systems, with drivers showing the largest gap in trust and perceived risk reduction. Regarding future outlooks, drivers were more optimistic about when technology would match human detection capabilities and were more likely to assign financial responsibility to manufacturers rather than drivers in crash scenarios. The findings underscore significant differences in how road users perceive and interact with ADAS. Non-drivers often held false expectations about system capabilities or intended to interact with automated vehicles as they would with conventional ones, potentially increasing risk. The study concludes that there is a critical need to understand the mental models of all road users to develop targeted educational approaches. Improving the accuracy of these mental models is essential for ensuring safe interactions as automated vehicles become more prevalent in the transportation network.

Key finding

Non-driving road users exhibit distinct misunderstandings of advanced driver assistance systems, with pedestrians falsely expecting sensor detection and all non-drivers showing lower trust in technology compared to drivers despite similar risk perceptions.

Methodology

survey

Sample size: 1531

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• acceptance adoption
• ehmi external hmi
• automation surprise
• situational awareness
• trust calibration
• automation

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: observational prevalence, self report data