Impact of Time-to-Collision Information on Driving Behavior in Connected Vehicle Environments Using A Driving Simulator Test Bed
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This study investigates the impact of time-to-collision (TTC) warning messages on driver behavior within a connected vehicle environment, specifically examining whether such alerts improve safety for aggressive versus non-aggressive drivers. Motivated by the high prevalence of intersection collisions caused by driver error, the research aims to validate a driving simulator test bed as a viable platform for evaluating vehicle-to-vehicle (V2V) communication technologies. The authors hypothesized that aggressive drivers would benefit most from collision warnings, thereby reducing potential crashes. The experimental design utilized a high-fidelity driving simulator at Louisiana State University, configured to simulate a connected vehicle environment. Thirty participants (aged 18–58) were recruited and classified into aggressive (n=10) and non-aggressive (n=20) groups using the Larson Driver’s Stress Profile questionnaire. Each participant completed two drives: one with visual alert messages and one without. The alerts, displayed in a heads-up display format, warned drivers to "SLOW DOWN" when TTC dropped to 3 seconds and to "SLOW DOWN- POTENTIAL CRASH" at 1.5 seconds. Data collection focused on vehicle velocity, headway distance, and calculated TTC as participants approached intersection stop lines. Statistical analysis employed dependent t-tests to compare mean TTC values between the two driving conditions for each driver group. The results indicated a significant divergence in response based on driving style. For non-aggressive drivers, there was no statistically significant difference in driving behavior between the alert and non-alert conditions (p = 0.7561). In fact, these drivers exhibited slightly lower mean TTCs during the alert condition, suggesting they drove closer to the lead vehicle because they anticipated the warnings. Conversely, aggressive drivers demonstrated a significant improvement in safety metrics when alerts were present (p = 0.0297). The alert system successfully altered their driving style, increasing their TTC and reducing the likelihood of collision. Additionally, aggressive drivers triggered more alerts than non-aggressive drivers, confirming that the system effectively targeted high-risk behaviors. The study concludes that connected vehicle technology offers distinct safety benefits for aggressive drivers, who are more likely to adjust their behavior in response to collision warnings. Furthermore, the research validates the use of driving simulator test beds as effective tools for assessing V2V technologies, offering a controlled alternative to physical test beds. The authors suggest future research should explore optimal alert timing, the integration of auditory prompts, and larger sample sizes to analyze demographic effects. This work supports the deployment of connected vehicle systems as a means to mitigate driver error and enhance intersection safety.
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 | 2026-06-08 |
| archive | success | canonical_url | — | — | 7 | 2026-06-09 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-09 |
| chunk | success | chunk | — | — | 1 | 2026-06-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-09 |
| enrich | failed | — | — | — | 3 | 2026-07-02 |
| promote | success | — | — | — | 1 | 2026-06-09 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 8 | 2026-06-11 |
| verify | success | — | — | — | 1 | 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.
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