动态路径规划框架提升自动驾驶安全性和实时性。
Safety-Oriented Dynamic Path Planning for Automated Vehicles
- 双层控制架构，主控用非线性模型预测，辅以同伦约束松弛提高求解效率。
- 障碍物动态投影增强路边界，支持实时路径优化与安全容错。
- 适合复杂动态场景下的自动驾驶系统开发与安全验证。
确保自动驾驶车辆的安全性需要先进的路径规划与避障能力，尤其在动态环境中。本文提出一种双层控制框架，通过融合障碍物运动的时间依赖网格投影来高效扩展道路边界，从而实现精确且自适应的路径规划。主控制回路采用非线性模型预测控制（NMPC）进行实时路径优化，引入基于同伦的约束松弛方法以改善最优控制问题（OCP）的可解性。此外，独立的备用回路并行运行，在主回路无法在关键时间内计算出最优轨迹时提供安全备选路径，显著提升安全性与实时性能。评估结果表明，该方法在多种驾驶场景中均表现出良好的实时适用性与鲁棒性。整体而言，该框架为复杂动态环境中的更安全、更可靠的自动驾驶迈出了重要一步。
原文摘要 · Abstract (English)
Ensuring safety in autonomous vehicles necessitates advanced path planning and obstacle avoidance capabilities, particularly in dynamic environments. This paper introduces a bi-level control framework that efficiently augments road boundaries by incorporating time-dependent grid projections of obstacle movements, thus enabling precise and adaptive path planning. The main control loop utilizes Nonlinear Model Predictive Control (NMPC) for real-time path optimization, wherein homotopy-based constraint relaxation is employed to improve the solvability of the optimal control problem (OCP). Furthermore, an independent backup loop runs concurrently to provide safe fallback trajectories when an optimal trajectory cannot be computed by the main loop within a critical time frame, thus enhancing safety and real-time performance. Our evaluation showcases the benefits of the proposed methods in various driving scenarios, highlighting the real-time applicability and robustness of our approach. Overall, the framework represents a significant step towards safer and more reliable autonomous driving in complex and dynamic environments.
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