Driver steering assistance for collision avoidance and turning performance optimization by constrained MPC
DOI: 10.1299/mej.20-00361
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Summary
This paper addresses the development of a driver steering assistance control algorithm for vehicles equipped with an active front steering (AFS) system. The research is motivated by the need to simultaneously optimize vehicle turning performance and ensure collision avoidance, a capability not fully realized in existing driver assistance systems that typically reserve AFS solely for emergency collision prevention. By integrating AFS with real-time obstacle detection via computer vision, the authors aim to create a system that enhances stability on slippery roads while automatically correcting insufficient driver inputs to prevent collisions. The proposed method utilizes a constrained model predictive control (MPC) framework. The vehicle dynamics are modeled using a linear bicycle model, discretized for control implementation. The control algorithm is designed to track a time-varying reference yaw rate commanded by the human driver through the steering wheel. To ensure physical feasibility and safety, the MPC formulation incorporates magnitude constraints on the front wheel turn angle and lateral tire force. Additionally, a collision avoidance condition is derived as a state constraint based on the vehicle’s position relative to detected obstacles. The optimization problem is formulated as a convex quadratic programming problem, ensuring computational efficiency. The algorithm minimizes the deviation between the actual and reference yaw rates while satisfying all constraints, correcting the vehicle’s trajectory only when the driver’s input is insufficient to avoid an obstacle. The effectiveness of the proposed control algorithm was evaluated through human-in-the-loop simulations using a driving simulator with an accurate full-vehicle dynamical model. The simulations demonstrated that the system successfully tracks the driver-commanded yaw rate under normal conditions, thereby optimizing turning performance. In scenarios where the driver’s steering input was inadequate to avoid an obstacle, the controller automatically adjusted the front wheel angle to ensure collision avoidance while respecting the physical constraints on steering angle and tire forces. The results confirmed that the MPC-based approach could handle the dual objectives of performance optimization and safety without compromising recursive feasibility or stability. The significance of this work lies in its contribution to advanced driver assistance systems by demonstrating that AFS can be exploited for both performance enhancement and safety. Unlike previous methods that treat collision avoidance as a separate, overriding function, this approach integrates it into the primary control loop. This allows for smoother vehicle dynamics and improved stability, particularly in challenging road conditions. The study provides a robust theoretical foundation and practical validation for MPC-based steering assistance, highlighting the potential for more intuitive and effective human-machine cooperation in automated driving scenarios. Future work may extend the algorithm to handle variable vehicle speeds and more complex obstacle dynamics.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | DOAJ | — | — | 1 | 2026-06-25 |
| archive | success | unpaywall | — | — | 1 | 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