Driver-Automation Cooperative Approach for Shared Steering Control Under Multiple System Constraints: Design and Experiments
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Summary
This paper addresses the challenge of shared lateral control between a human driver and a lane keeping assist system (LKAS) in intelligent vehicles. The primary motivation is to resolve conflicts arising from human-machine interaction, where the driver and automation may have differing driving objectives. Existing methods often fail to exploit real-time driver behavior data or adequately handle system constraints, leading to potential safety and comfort issues. The authors propose a cooperative control approach that dynamically allocates control authority based on the driver’s real-time activity, aiming to assist the driver during underload or overload conditions while allowing full takeover when necessary. The methodology integrates a driver-in-the-loop vehicle model using a bicycle model for lateral dynamics and a behavioral model for the driver. The driver’s torque is modeled based on compensatory and anticipatory visual behaviors. A key innovation is the introduction of a fictive driver activity parameter, $\theta_d$, which combines normalized driver torque and a driver state variable from a monitoring system. This parameter drives a weighting function, $\mu(\theta_d)$, shaped as a U-curve to modulate the assistance torque, ensuring high assistance when driver activity is low or critically high. The control design utilizes a Takagi-Sugeno (T-S) fuzzy framework to handle the time-varying nature of the driver activity parameter and vehicle speed. To address safety, the authors employ Lyapunov-based control and linear matrix inequalities (LMIs) to manage both state and control input constraints, specifically preventing torque saturation and ensuring stability within a robust invariant set. Experimental validation was conducted using an advanced interactive dynamic driving simulator with a human driver. The results demonstrate the effectiveness of the proposed method in managing shared steering control for both lane keeping and obstacle avoidance. The controller successfully adapted assistance levels according to the driver’s real-time engagement, providing continuous feedback and reducing workload without preventing the driver from executing specific maneuvers. The approach effectively handled system constraints, maintaining stability even when assistance torque approached saturation limits. The significance of this work lies in its comprehensive handling of human-machine interaction through adaptive control authority allocation. By explicitly incorporating real-time driver behavior and state into the control loop, the method improves the coordination between the driver and automation. Furthermore, the theoretical contribution of a Lyapunov-based T-S control design for constrained systems provides a rigorous framework for ensuring safety and comfort in shared control applications. This approach offers a promising solution for next-generation intelligent vehicles, enhancing safety by mitigating conflicts and adapting to varying driver capabilities and situational demands.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-17 |
| archive | success | unpaywall | — | — | 2 | 2026-06-25 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-18 |
| chunk | success | chunk | — | — | 1 | 2026-06-18 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-18 |
| promote | success | — | — | — | 1 | 2026-06-17 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-18 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Theoretical Contribution: computational model