Driver's Interactions with Advanced Vehicles in Various Traffic Mixes and Flows (Connected and Autonomous Vehicles (CAVs), Electric Vehicles (EVs), V2X, Trucks, Bicycles and Pedestrians) - Phase I: Driver Behavior Study and Parameters Estimation
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Summary
This study addresses the critical gap in understanding how human drivers react to Connected and Autonomous Vehicle (CAV) safety applications. While CAV technology is rapidly deploying, existing research often relies on microscopic simulations that assume driver behavior rather than measuring actual human responses. The authors argue that evaluating driver interactions with automated systems is essential for ensuring the safety and effectiveness of these technologies, particularly regarding braking, steering, throttle control, and speed adjustments in mixed traffic environments. To investigate these behaviors, the researchers conducted a Phase I study using a medium-fidelity, full-scale driving simulator at Morgan State University. The experimental design involved 93 participants from diverse socio-economic backgrounds who completed 186 driving experiments. The study tested five specific CAV scenarios: Pedestrian Collision Warning (PCW), Red-Light Violation Warning (RLVW), Forward Collision Warning (FCW), Curve Speed Warning (CSW), and Level 3 Autonomous Mode. Data collection included pre- and post-simulation surveys to assess familiarity and trust in the technology, alongside behavioral analysis using Hazard-based Duration Models, Random Forest Models, and Take Over Reaction Time (TORt) analysis. The results revealed distinct behavioral impacts for different warning systems. PCW and RLVW significantly influenced braking behavior, causing participants to engage in initial aggressive braking when these warnings were active. FCW had a positive influence on speed changes, helping drivers adjust their velocity appropriately. In contrast, CSW had no statistically significant impact on driver speed. Regarding the transition from autonomous to manual control, the average Take Over Reaction Time (TORt) for steering was 2.47 seconds, and for throttle, it was 2.98 seconds. These reaction times were significantly influenced by demographic and experiential factors, specifically the participant's age, annual miles driven, and prior familiarity with CAV technology. The significance of this research lies in the identification of specific, quantifiable driver parameters—namely TORt, Deceleration Rate, and Change in Speed—that can be integrated into traffic simulators. By incorporating these empirically derived human behavior metrics, future traffic flow models can more realistically simulate mixed traffic environments involving both human drivers and automated vehicles. This provides policymakers and engineers with better tools to analyze the impacts of novel navigation systems and plan for the safe integration of CAVs into existing transportation networks.
Key finding
Pedestrian Collision Warning and Red-Light Violation Warning caused initial aggressive braking, whereas Forward Collision Warning positively influenced speed changes and Curve Speed Warning had no impact on speed.
Methodology
simulator
Sample size: 93
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.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- situational awareness
- traffic density
- automation surprise
- acceptance adoption
- braking response
- exposure measurement
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: behavioral performance data
- Methodological Resource: tool software
- Theoretical Contribution: computational model