Shared lateral control with on-line adaptation of the automation degree for driver steering assist system: A weighting design approach
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Summary
This paper addresses the challenge of shared lateral control in driver steering assist systems (DSAS), specifically focusing on lane-keeping and obstacle avoidance tasks. The primary motivation is to resolve conflicts between the human driver and the automated system (E-copilot) by dynamically adapting the level of automation based on driver behavior and state. Previous approaches often failed to fully exploit driver behavior information or handle actuator saturation effectively. This work proposes an "intelligent" cooperative control strategy that modulates assistance torque according to a weighting factor representing driver activity, thereby ensuring continuous and appropriate support during both low-load and high-load driving situations. The methodology employs a Takagi-Sugeno (T-S) fuzzy model-based control design combined with Lyapunov stability tools. The authors introduce a fictive nonlinear term into the vehicle model to represent driver activity, quantified by a normalized variable $\theta_d$ derived from measured driver torque and driver state monitoring. The assistance torque is modulated using a Bell-shaped weighting function $\mu(\theta_d)$ that mimics the U-shaped relationship between driver load and required assistance. The control design accounts for input saturation, system state constraints, and bounded disturbances (such as lateral wind forces). All design conditions are formulated as linear matrix inequalities (LMIs), allowing for effective numerical solution. The system handles variations in longitudinal speed and ensures stability despite actuator limitations. Simulation results validate the proposed approach under various driving scenarios. In tests involving different assistance levels, the system successfully adjusted the automation degree: providing maximal assistance ($\mu=1$) when the driver was disengaged, minimal assistance ($\mu=0.28$) during normal lane tracking, and high assistance again when the driver was highly involved in difficult maneuvers. The controller maintained stability and performance within defined state constraints, even when the fictive torque approached saturation limits. The results demonstrate that integrating driver behavior into the control loop significantly improves conflict management and closed-loop performance compared to static cooperation strategies. The significance of this work lies in its contribution to human-machine coordination in intelligent vehicles. By explicitly modeling driver activity and adapting the automation degree online, the proposed method enhances safety and comfort by preventing conflicts and ensuring the DSAS provides appropriate support. The use of T-S models with LMI-based design offers a robust framework for handling nonlinearities and constraints in shared control systems. This approach advances the field by providing a systematic way to balance driver authority and automation assistance, crucial for the development of next-generation driving assistance systems.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-25 |
| archive | success | unpaywall | — | — | 2 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-25 |
| chunk | success | chunk | — | — | 1 | 2026-06-25 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-25 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-25 |
| 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