The impact of the following vehicles behaviors on the car following behaviors of the ego-vehicle

Liu, Yang; Zhang, Jiahao; Yuxuan, Ouyang,; Yu, Huan; He, Dengbo · 2025 · ArXiv.org

DOI: 10.48550/arxiv.2507.00452

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Summary

This study investigates how the behavior of a following vehicle (FV) influences the car-following (CF) decisions of an ego-vehicle, addressing a gap in traditional models that primarily focus on lead vehicle dynamics. Motivated by the prevalence of rear-end collisions and the known human tendency to react to surrounding traffic, the authors quantify the impact of "peer pressure" from tailgating vehicles on driver behavior. The researchers utilized the highD naturalistic driving dataset, extracting 1,024 "tailgated" events (time headway ≤ 1 second) and 465 "gapped" events (time headway ≥ 3 seconds). To ensure fair comparison, Dynamic Time Warping (DTW) was employed to pair segments with similar lead vehicle speed profiles. The study analyzed speed fluctuation metrics (standard deviation, mean absolute deviation, coefficient of variation, and stochastic volatility) and safety metrics (time headway and Deceleration Needed to Avoid Crash, DRAC). Additionally, Adversarial Inverse Reinforcement Learning (AIRL) was applied to recover the reward functions governing driver decisions in both scenarios. Statistical analysis revealed that speed fluctuation metrics were largely similar between tailgated and gapped events, indicating that ego-vehicles generally maintained traffic flow stability regardless of rear pressure. However, significant differences emerged in safety metrics. Drivers in tailgated scenarios maintained a significantly smaller mean time headway compared to those in gapped scenarios. Crucially, despite the reduced following distance, the mean DRAC was also significantly lower in tailgated events. This suggests that while drivers compressed their safety buffer when tailgated, they simultaneously adopted more cautious driving strategies to mitigate collision risk. The AIRL model successfully replicated these behaviors, confirming that drivers adjust their decision-making rewards based on FV proximity. The findings demonstrate that ego-vehicle drivers actively adapt their CF strategies in response to tailgating, balancing reduced following distances with increased caution. This challenges the assumption that drivers only react to lead vehicles, highlighting the importance of incorporating surrounding vehicle states into CF models. These insights have implications for improving traffic flow modeling, designing advanced driver assistance systems, and enhancing traffic safety by accounting for the complex social dynamics of driving.

Key finding

Drivers adjust their car-following behavior in response to tailgating by maintaining closer distances to the lead vehicle while simultaneously driving more cautiously to mitigate collision risk.

Methodology

naturalistic

Provenance

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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
• speed choice
• 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