Sharing the Road: How Human Drivers Interact with Autonomous Vehicles on Highways

Huang, Chunxi; Wen, Xiao; He, Dengbo; Jian, Sisi · 2022 · Proceedings of the Human Factors and Ergonomics Society Annual Meeting

DOI: 10.1177/1071181322661165

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Summary

This study investigates how human drivers adjust their behaviors when sharing highways with autonomous vehicles (AVs), addressing a critical gap in understanding mixed-traffic safety during the transition period of AV deployment. While previous research relied on simulations or low-speed controlled environments, this work utilizes real-world data to analyze driver interactions with AVs at highway speeds, specifically focusing on car-following and car-passing events. The researchers analyzed data from the Waymo Open Dataset, which contains trajectories of SAE Level 4/5 AVs and surrounding traffic captured via sensors on public roads in the U.S. They extracted 229 human-driven vehicle (HV)-following-AV events and 1,246 HV-following-HV events for car-following analysis, alongside 37 HV-pass-AV and 26 HV-pass-HV events for car-passing analysis. Statistical models assessed four safety metrics in car-following scenarios: gap distance, time gap, reciprocal time to collision (reTTC), and standard deviation of following vehicle speed (SD-FVspeed). For car-passing, lateral distance (LD) was measured. The results indicate that human drivers interact differently with AVs depending on speed. At high speeds, drivers maintained larger safety margins when interacting with AVs than with HVs. Specifically, gap distance was larger when following AVs above 103 km/h, and time gap was larger above 85 km/h. In car-passing events, drivers kept a significantly larger lateral distance (0.78 m) when passing AVs compared to HVs. However, at high speeds, drivers exhibited difficulty anticipating AV speed changes, evidenced by a smaller time to collision and higher speed volatility when following AVs compared to HVs. Conversely, at lower speeds (below 43 km/h), drivers showed less speed volatility when following AVs, and maintained closer gaps, suggesting they perceived tailgating AVs as more controllable or less risky at low speeds. These findings highlight that human drivers do not treat AVs as predictable as human drivers, particularly at highway speeds, leading to inconsistent safety margins and reduced responsiveness to speed variations. The study suggests that AVs may behave differently than humans at high speeds, causing drivers to struggle with anticipation. The authors conclude that these behavioral differences underscore the need for external human-machine interfaces (eHMI) to increase AV transparency and for AV control algorithms to account for surrounding human driver behaviors to ensure safety in mixed traffic.

Key finding

At high speeds, human drivers maintain larger safety margins but exhibit greater difficulty anticipating speed changes when interacting with autonomous vehicles compared to human-driven vehicles.

Methodology

dataset

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• following distance
• automation surprise
• traffic density

Information type

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• Empirical Findings: behavioral performance data