Connected and Automated Vehicle Based Intersection Maneuver Assist Systems (CAVIMAS) and Their Impact on Driver Behavior, Acceptance, and Safety
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Summary
This study addresses the critical safety issue of intersection crashes, which account for approximately 40% of all crashes in the United States, with driver-related factors such as inadequate surveillance and misjudgment of speed identified as primary causes. The research investigates whether Connected and Automated Vehicle based Intersection Maneuver Assist Systems (CAVIMAS) can mitigate these risks by leveraging vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications. The primary motivation is to evaluate not only the technical efficacy of such systems but also the human factors involved, specifically driver behavior, acceptance, trust, and workload, to ensure that automated interventions are designed with appropriate content and presentation. The researchers conducted a driving simulator study using a high-fidelity platform at the University of Michigan Transportation Research Institute. Twenty-four licensed drivers participated in a within-subject experimental design, interacting with three system concepts: System A (baseline with navigation only), System B (alert-only, providing visual and auditory warnings with recommendations to brake or accelerate), and System C (alert and automatic control, where the system autonomously executed braking or acceleration). Participants navigated virtual city environments containing scripted scenarios representing common intersection crash types, including left turns across path and straight crossing paths. Data collection included objective measures from the simulator (reaction time, vehicle kinematics) and an integrated eye-tracking system (gaze duration, fixation locations), alongside subjective surveys measuring driver workload (NASA-TLX), trust, and system acceptance. The findings highlight the significant impact of human factors on the deployment of CAVIMAS. Survey results indicated that drivers’ trust and acceptance of the systems were paramount, with specific variations observed between the alert-only and automated control conditions. Drivers assessed their confidence in maneuvering intersections and judging speed differently across the systems. The study also examined driver workload and hazard anticipation through visual gaze behaviors, noting how attention shifted toward the instrument panel and warning interfaces during conflict scenarios. The results suggest that while the systems provided real-time guidance and active controls, the drivers’ interactions were heavily influenced by their perceptions of the automation’s reliability and the clarity of the human-machine interface. The significance of this research lies in its demonstration that technological solutions for intersection safety must be coupled with rigorous human factors evaluation. The study concludes that the design and deployment of CAVIMAS cannot rely solely on technical performance; rather, they must account for driver trust, acceptance, and cognitive workload. By identifying how drivers respond to different levels of automation and alerting, the findings provide essential evidence for designing safer, more user-centric connected vehicle systems that effectively mitigate intersection crashes without compromising driver situational awareness or comfort.
Key finding
Human factors considerations regarding driver trust and acceptance are paramount for the successful design and deployment of connected vehicle intersection assistance systems.
Methodology
simulator
Sample size: 24
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_rosap on 2026-05-23 (8 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 4 | 2026-05-29 |
| 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.
- automation
- automation surprise
- acceptance adoption
- situational awareness
- ehmi external hmi
- driver vru interaction
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).
- Applied Guidance: design guidelines
- Methodological Resource: tool software
- Theoretical Contribution: computational model