基于弗伦内坐标系的实时路径规划，让自动驾驶更安全平滑。
Frenet Corridor Planner: An Optimal Local Path Planning Framework for Autonomous Driving
- 在弗伦内空间中建模障碍物，动态确定避障方向。
- 优化路径光滑性与障碍物间距，降低碰撞风险。
- 适合需要实时响应的自动驾驶系统部署。
为满足高效与有效的需求，基于路径-速度解耦的轨迹规划方法被广泛应用于自动驾驶。虽然全局路径可离线预计算，但实时生成自适应局部路径仍至关重要。为此，我们提出弗伦内走廊规划器（Frenet Corridor Planner, FCP），一种基于优化的自动驾驶局部路径规划策略，确保绕障时路径平滑且安全。将车辆建模为安全增强的边界框，行人建模为弗伦内空间中的凸包，通过确定静态障碍物的合理偏离侧，定义可行驶走廊。随后，采用改进的空域自行车运动学模型，对路径进行优化，兼顾平滑性、边界间隙及动态障碍物风险最小化。优化后的路径传递给速度规划器，生成最终轨迹。通过大量仿真和真实硬件实验验证了FCP的高效性与有效性。
原文摘要 · Abstract (English)
Motivated by the requirements for effectiveness and efficiency, path-speed decomposition-based trajectory planning methods have widely been adopted for autonomous driving applications. While a global route can be pre-computed offline, real-time generation of adaptive local paths remains crucial. Therefore, we present the Frenet Corridor Planner (FCP), an optimization-based local path planning strategy for autonomous driving that ensures smooth and safe navigation around obstacles. Modeling the vehicles as safety-augmented bounding boxes and pedestrians as convex hulls in the Frenet space, our approach defines a drivable corridor by determining the appropriate deviation side for static obstacles. Thereafter, a modified space-domain bicycle kinematics model enables path optimization for smoothness, boundary clearance, and dynamic obstacle risk minimization. The optimized path is then passed to a speed planner to generate the final trajectory. We validate FCP through extensive simulations and real-world hardware experiments, demonstrating its efficiency and effectiveness.
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