Expectations and Understanding of Advanced Driver Assistance Systems among Drivers, Pedestrians, Bicyclists, and Public Transit Riders
archive: archived pipeline: cataloged verified
Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)
Summary
This study addresses the critical gap in understanding how non-drivers perceive and interact with Advanced Driver Assistance Systems (ADAS). While previous research has focused on drivers’ often poor understanding of vehicle automation, this project, a collaboration between the AAA Foundation for Traffic Safety and the SAFER-SIM University Transportation Center, examined the perceptions, trust, and expectations of bicyclists, pedestrians, and public transit riders regarding technologies like Adaptive Cruise Control (ACC) and Lane Keeping Assist (LKA). The research aimed to determine if these groups differ from drivers in their understanding of ADAS, their trust in specific use cases, and their outlook on the future of automated vehicles. The methodology involved an online survey administered to 1,531 participants aged 18 to 91. Respondents were classified into four primary road-user groups—drivers, bicyclists, pedestrians, and public transit riders—based on their primary mode of transportation. The survey assessed general knowledge of ACC and LKA, followed by two specific scenarios involving vehicle-bicycle and vehicle-pedestrian interactions. Participants answered questions regarding system behavior expectations, trust, perceived safety, crash responsibility, and intended behavioral responses. The study also queried respondents about their projections for the future progression of automated vehicle capabilities. Key findings revealed significant disparities in understanding and behavior across groups. Overall accuracy in understanding ADAS was modest (50–60%), though bicyclists demonstrated a stronger understanding of ACC and LKA than drivers. Despite this superior knowledge, bicyclists were less likely to adjust their behavior to enhance safety. Pedestrians frequently held false beliefs that vehicles would accurately detect them and adjust accordingly. Non-drivers exhibited lower trust in vehicle technology than drivers, yet all groups assessed risk similarly. Drivers trusted their own skills more than the technology and were more optimistic about future automation capabilities. Notably, the presence of vehicle technology was perceived to increase crash risk, particularly by drivers, likely due to overconfidence in their manual driving skills. All groups trusted manual drivers more than automated systems. The significance of these findings lies in the identified mismatch between user expectations and actual behaviors, which poses safety risks. Non-drivers may face greater danger due to false expectations (pedestrians) or the persistence of normal behaviors despite knowing system capabilities (bicyclists). The study underscores the necessity of expanding research beyond drivers to include all road users. It suggests that targeted, individualized approaches are needed to address the disconnect between trust, risk perception, and compensatory behaviors, highlighting the need to understand how various information sources shape public understanding of emerging vehicle technologies.
Key finding
Bicyclists demonstrated superior understanding of ADAS capabilities but were less likely to modify their behavior for safety, while pedestrians held false beliefs about vehicle detection capabilities.
Methodology
survey
Sample size: 1531
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_aaa_foundation on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | aaa_foundation | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
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
- automation surprise
- trust calibration
- ehmi external hmi
- situational awareness
- 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: self report data, observational prevalence