Analysis of Truck Driver Behavior to Design Different Lane Change Styles in Automated Driving

Wang, Zheng; Guan, Muhua; Lan, Jin; Yang, Bo; Kaizuka, Tsutomu; Taki, Junichi; Nakano, Kimihiko · 2020 · arXiv

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Summary

This study addresses the challenge of improving driver acceptance of automated lane change systems in commercial trucks. While automation offers safety and workload benefits, a one-size-fits-all system may fail to meet diverse driver preferences. The authors propose designing an adaptable automated system with distinct driving styles—aggressive, medium, and conservative—based on empirical modeling of professional truck driver behavior. The research aims to determine if these styles are distinguishable to drivers and if they maintain acceptable safety and reliability perceptions. The methodology involved two truck driving simulator experiments with 12 professional truck drivers. In the first experiment, participants performed discretionary lane changes in response to a decelerating lead vehicle, allowing researchers to identify model parameters such as gap acceptance thresholds and acceleration profiles. These parameters were classified into three styles using the 25th, 50th, and 75th percentiles of the collected data. In the second experiment, the same participants evaluated the automated system executing these three styles. Evaluations were conducted from two perspectives: Scenario A, where participants rode in the automated ego vehicle, and Scenario B, where participants drove a surrounding vehicle observing the automated truck’s maneuvers. Subjective ratings on safety, distance perception, and system reliability were collected and analyzed using Friedman and Wilcoxon signed-rank tests. The results demonstrated that the three driving styles were successfully distinguished by drivers from the perspective of the automated vehicle. Statistical analysis revealed significant differences in perceived safety and distance to the vehicle ahead in the target lane, with the conservative style yielding longer distances and higher safety ratings compared to the aggressive style. Although differences in distance to surrounding vehicles were not statistically significant in all cases, a clear trend emerged where conservative styles maintained greater gaps. From the perspective of surrounding vehicles, no significant differences were found among the styles regarding distance or safety perceptions. However, all three styles were rated as reliable, with mean scores indicating that the automated maneuvers were acceptable and aligned with driver expectations for safe distances. The study concludes that designing automated lane change systems with multiple, data-driven driving styles can enhance user acceptance by allowing drivers to adapt the system to their preferences and situational needs. The findings validate that using percentile-based parameter classification creates distinguishable styles without compromising perceived safety or reliability. This approach supports the development of human-centered automation for commercial trucks, potentially reducing driver workload and improving cooperation between human drivers and automated systems. The results suggest that future automated truck systems should incorporate adaptable driving styles to better serve professional drivers.

Key finding

A driver-model-based automated lane-change controller produced three distinguishable, human-acceptable lane-change styles (aggressive/medium/conservative) for commercial trucks.

Methodology

simulator

Sample size: 12 professional truck drivers (mean age 42.9, SD 8.2; 11 male, 1 female; mean driving experience 14.6 years, SD 8.2)

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. Discovered via discover_arxiv on 2026-05-04 (4 acquisition events logged).

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.

• lane changing
• lane positioning
• following distance
• automation surprise
• work zones
• traffic density

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