Analysis of Driver Behavior in Mixed Autonomous and Non-autonomous Traffic Flows
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Summary
This study investigates driver behavior in mixed traffic environments containing both autonomous vehicles (AVs) and human-driven vehicles, specifically focusing on interactions at unsignalized intersections. As AV market penetration increases, a transitional period of mixed traffic is inevitable. While AVs can improve safety and efficiency through precise trajectory following and vehicle-to-vehicle communication, human drivers exhibit significant behavioral variability that can negate these advantages. The research aims to identify factors influencing human driver deviations from planned trajectories to inform the design of driver-assistance guidance systems that minimize crash risks in mixed flows. The researchers conducted a driving simulator study using a fixed-base National Advanced Driving Simulator (NADS) miniSim. Thirty-two participants from the Seattle area, screened for valid licenses and recent driving experience, followed a virtual lead AV through five consecutive unsignalized intersections. The experimental design employed a 2x3 within-subject factorial structure manipulating three independent variables: traffic density (high with competing AVs vs. low with only the lead vehicle), lead vehicle speed (30 mph vs. 40 mph), and the order of turning operations (left-turn first vs. right-turn first). Participants were instructed to maintain a safe following distance without stopping. The primary dependent variable was trajectory deviation, calculated as the area between the participant’s path and the lead vehicle’s path during left and right turns. Data were analyzed using Analysis of Variance (ANOVA) and linear regression models after applying Box-Cox transformations to satisfy normality assumptions. The results indicated that drivers performed well when traveling straight but exhibited significant deviations during turning maneuvers. For left turns, trajectory deviation was significantly influenced by both the speed of the lead vehicle and the order of operations. Higher lead vehicle speeds (40 mph) resulted in larger deviations, while performing the left turn as the first turning operation yielded smaller deviations compared to performing it second. Traffic density did not significantly affect left-turn deviations. For right turns, only the order of operations was significant; performing a left turn prior to a right turn increased the deviation in the subsequent right turn. Neither speed nor traffic density significantly impacted right-turn deviations. Visual analysis revealed that drivers tended to make narrower left turns and wider right turns than the lead vehicle’s trajectory. These findings suggest that driver-assistance systems in mixed traffic must dynamically adapt to human behavior, particularly regarding speed and maneuver sequence. The lack of significant impact from traffic density implies that human drivers can maintain performance even in dense AV environments, though speed remains a critical factor for left-turn safety. The study highlights the necessity for guidance systems to account for larger trajectory deviations at higher speeds and to adjust expectations based on the order of maneuvers. Future research should explore augmented reality displays to enhance guidance adherence and address limitations related to participant demographics and pandemic-era recruitment constraints.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-05 |
| archive | success | canonical_url | — | — | 7 | 2026-06-09 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-07 |
| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| promote | success | — | — | — | 1 | 2026-06-05 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Empirical Findings: behavioral performance data
- Methodological Resource: tool software