Driving simulator study for the effects of autonomous vehicles on drivers behaviour under car-following conditions
DOI: 10.54941/ahfe1002434
archive: archived pipeline: cataloged verified
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Summary
This study investigates the impact of autonomous vehicles (AVs) on the driving behavior of human drivers in mixed traffic flows, specifically under car-following conditions. While AVs are recognized for improving safety and efficiency, limited research has examined how their presence affects conventional vehicle drivers during the transitional period of mixed traffic. The authors hypothesized that the cautious, rule-compliant behavior of AVs might influence following drivers differently than conventional vehicles, potentially altering safety outcomes. The primary objective was to determine how human drivers adjust their following distance and reaction times when trailing recognizable AVs, unrecognizable AVs, or conventional vehicles. The research employed a driving simulator study involving 40 participants (final sample of 38 after excluding outliers). Participants drove a simulated Toyota Auris in a motorway scenario featuring three car-following configurations: a marked autonomous vehicle (AVM), an unmarked autonomous vehicle (AV), and a conventional vehicle (CV). Each configuration included two ordinary conditions (constant speeds of 90 km/h and 50 km/h) and one braking condition involving heavy deceleration. Data collection focused on safety indicators, including Time Headway (TH) and Time-To-Collision (TTC). The researchers calculated Risk Indicators (IRs) based on the distance traveled below specific risk thresholds for TH and TTC. Statistical analyses, including one-way ANOVA, were used to compare driving performance across the three configurations. The results revealed distinct behavioral differences depending on the leading vehicle type and driving condition. Under ordinary conditions, drivers exhibited poorer safety performance when following conventional vehicles, with significantly higher risk indicators for both TH and TTC compared to AV and AVM configurations. This suggests that the predictable, cautious behavior of AVs encourages safer following behaviors in human drivers. Conversely, under braking conditions, drivers maintained larger safety margins (higher TH and TTC) when following conventional vehicles, indicating more conservative behavior. However, when following AVs, drivers reacted with emergency braking more frequently (45–58% of cases) compared to only 8% for CVs. Notably, drivers following marked AVs (AVM) demonstrated significantly shorter reaction times than those following unmarked AVs or conventional vehicles, suggesting that visual identification of an AV enhances driver responsiveness. The study concludes that the introduction of AVs influences human driver behavior in mixed traffic, with safety implications varying by context. AVs promote safer following distances during steady-state driving but may lead to closer following and more abrupt reactions during braking events, possibly due to driver reliance on AV predictability. The findings highlight the benefit of marking AVs to improve reaction times. These insights are significant for understanding human-machine interaction in transitional traffic environments and suggest that AV visibility and behavior design are critical for optimizing road safety during the widespread adoption of autonomous technology.
Key finding
Drivers exhibited safer car-following behavior under ordinary conditions when trailing autonomous vehicles, but maintained larger safety gaps behind conventional vehicles during braking, with marked autonomous vehicles eliciting faster driver reaction times.
Methodology
simulator
Sample size: 38
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.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-05 |
| archive | success | canonical_url | — | — | 7 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 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 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 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.
- following distance
- traffic density
- automation surprise
- braking response
- behavioral adaptation risk compensation
- situational awareness
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