Shared Control for Vehicle Lane-Changing with Uncertain Driver Behaviors

Wu, Jiamin; Zhao, Chenguang; Yu, Huan · 2025 · arXiv

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Summary

This paper addresses the challenge of executing safe and stable lane-changing maneuvers in the presence of uncertain human driver behaviors. Lane changes are critical yet risky traffic maneuvers where human variability in throttle and brake inputs can degrade traffic string stability and cause disturbance propagation. While fully automated control can suppress these fluctuations, it often suffers from low user trust and acceptance. To bridge this gap, the authors propose a human–automation shared control framework for longitudinal dynamics that preserves driver authority while providing automated assistance to ensure stability. The motivation is to balance the effectiveness of automation with the adaptability of human drivers, specifically addressing the stochastic nature of human behavior during high-demand tasks. The methodology models human driving behavior as a continuous-time hidden Markov process with two modes: low and high task difficulty. These modes switch based on cognitive demands and traffic conditions, such as gap size and speed. Since the true behavior mode is not perfectly observable, the system accounts for estimation uncertainty using a joint Markov process of hidden and observed modes. The authors first design a nominal stabilizing controller using linear matrix inequalities (LMIs) to guarantee stochastic $L_2$ string stability, ensuring that upstream disturbances from the leader vehicle are attenuated in expectation. To further preserve driver authority, they develop a Minimal Intervention Controller (MIC) that augments the performance output with a penalty on automated effort. This design explicitly balances disturbance attenuation against the magnitude of automated intervention, limiting unnecessary throttle or braking actions. The proposed framework is validated through numerical simulations using lane-changing data from the Next Generation Simulation (NGSIM) dataset and the TGSIM dataset. The human behavior parameters and Markov transition rates were calibrated using real-world trajectories from Interstate 80. Results show that the nominal controller effectively reduces speed perturbations in the follower vehicle and shortens the lane-changing duration compared to human-only control. The MIC further reduces automated effort and enhances driver comfort, albeit with a moderate compromise in stability and efficiency metrics. Validations on SAE Level 2 vehicles demonstrate that the MIC enables earlier lane changes than standard Level 2 control while maintaining driver authority. The significance of this work lies in providing a tractable, theoretically grounded approach to shared control that accounts for stochastic human behavior. By modeling driver variability as a Markov jump process and incorporating a minimal intervention principle, the framework offers a practical solution for transitional automation levels. It demonstrates that shared control can successfully mitigate traffic disturbances and improve maneuver feasibility without undermining driver engagement, offering a viable path toward higher acceptance of automated driving systems in complex scenarios like lane changing.

Key finding

Across NGSIM and TGSIM lane-change cases, the nominal controller reduces speed perturbations and shortens lane-changing time relative to human-only control, while the MIC achieves earlier lane changes than SAE Level 2 automation-only control (e.g., 5.90 s vs 7.17 s in TGSIM Case 1) at the cost of a small string-stability decrement, with an automated effort ratio r_int around 0.5 indicating that driver input is preserved.

Methodology

dataset

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 (3 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
• steering pattern
• automation
• automation surprise

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
• Theoretical Contribution: computational model