Driver Acceptance of Connected, Automation-Assisted Cruise Control : Experiment 1
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Summary
This study investigates human-factors issues related to Level-1 vehicle automation, specifically examining driver acceptance of Adaptive Cruise Control (ACC) versus Cooperative Adaptive Cruise Control (CACC). The research was motivated by known limitations of radar-based ACC systems, which frequently lose track of lead vehicles on curves or hills, causing abrupt acceleration and braking that erode driver trust. The authors hypothesized that supplementing ACC with vehicle-to-vehicle (V2V) communications (CACC) would improve system performance and driver trust, and that providing drivers with information about what the system is tracking would further enhance acceptance. The experiment was conducted using a high-fidelity driving simulator featuring a winding, hilly four-lane road designed to challenge radar tracking. Participants followed a lead vehicle traveling at 50 mph while using cruise control set to 55 mph. The study employed a factorial design testing two cruise-control types (ACC and CACC) and three display types: a basic on/off indicator, a tracking-status display showing whether a vehicle was being tracked, and a video-based display highlighting the tracked vehicle. CACC was modeled to accelerate and decelerate less aggressively than ACC when radar contact was lost, simulating the stability provided by V2V data. Dependent measures included total time with cruise control engaged, frequency of disengagement and re-engagement requests, trust ratings, workload (NASA-TLX), and eye-glance behavior. Results indicated that drivers using the CACC system reported significantly higher trust in the cruise control than those using the standard ACC system. This suggests that the smoother behavior of CACC, which maintained tracking through curves and hills via simulated V2V communication, mitigated the negative user experiences associated with radar limitations. Regarding display types, drivers looked at the tracking-status and video displays approximately 2% of the time, with no glances exceeding 0.73 seconds. The study found no evidence that providing tracking information distracted drivers or increased workload. However, the video display offered limited additional value in this specific experimental setup, as there was only one lead vehicle present. The findings conclude that supplementing ACC with V2V communications can increase driver trust and potentially improve system use, thereby enhancing roadway safety. The provision of tracking information on the instrument cluster did not appear to distract drivers, supporting its inclusion in future Level-1 automation interfaces. The authors recommend further testing in more complex driving environments to validate these results. This research provides critical insights for developers of automated systems and safety professionals aiming to optimize human-machine interaction in connected vehicles.
Key finding
Drivers rated their trust in the CACC-equipped vehicle higher than in the ACC-equipped vehicle.
Methodology
simulator
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 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 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.
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Information type
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- Empirical Findings: behavioral performance data
- Methodological Resource: tool software
- Theoretical Contribution: computational model