自动驾驶中通过优先级软化约束，实现在突发情况下的安全与舒适平衡。
Priority-Driven Safe Model Predictive Control Approach to Autonomous Driving Applications
- 基于优先级的约束软化机制，动态调整舒适性约束以保障安全
- 在真实驾驶场景模拟中，始终满足碰撞避免等关键安全要求
- 引入学习算法加速计算，支持实时决策，适合实际车载系统
本文展示了安全模型预测控制（SMPC）框架在自动驾驶中的适用性，重点设计自适应巡航控制（ACC）与自动变道系统。基于具有优先级驱动约束软化的SMPC方法——该方法通过选择性松弛可调约束子集，在外部扰动下仍确保硬约束满足——我们证明了算法能在突发扰动时动态放松低优先级的舒适性约束，同时维持碰撞避免、车道保持等关键安全要求。为实现实时执行，提出一种基于学习的算法近似耗时的SMPC计算。在包含未预见扰动的真实驾驶场景仿真中，该优先级软化机制持续保障严格的安全约束，验证了所提方法的有效性。
原文摘要 · Abstract (English)
This paper demonstrates the applicability of the safe model predictive control (SMPC) framework to autonomous driving scenarios, focusing on the design of adaptive cruise control (ACC) and automated lane-change systems. Building on the SMPC approach with priority-driven constraint softening -- which ensures the satisfaction of \emph{hard} constraints under external disturbances by selectively softening a predefined subset of adjustable constraints -- we show how the algorithm dynamically relaxes lower-priority, comfort-related constraints in response to unexpected disturbances while preserving critical safety requirements such as collision avoidance and lane-keeping. A learning-based algorithm approximating the time consuming SMPC is introduced to enable real-time execution. Simulations in real-world driving scenarios subject to unpredicted disturbances confirm that this prioritized softening mechanism consistently upholds stringent safety constraints, underscoring the effectiveness of the proposed method.
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