提出分段轨迹优化方法，让自动驾驶泊车更高效安全。
Segmented Trajectory Optimization for Autonomous Parking in Unstructured Environments
- 分段优化初始路径，用迭代SQP保持策略并允许弯道突变。
- 仿真显示在斜向与垂直泊车中效率提升，全程无碰撞。
- 适合需高安全性和快速决策的自动驾驶泊车场景。
本文提出一种分段轨迹优化（STO）方法用于自动驾驶泊车，通过基于序列二次规划（SQP）的迭代方法将初始轨迹优化为动态可行且无碰撞的路径。STO保留高层全局规划器的驾驶策略，同时允许在切换点处存在曲率不连续以提升操作效率。为保障安全，采用GJK加速的椭圆收缩与扩展构建凸走廊，作为每轮迭代中的安全约束。在垂直与斜向泊车场景的数值仿真表明，STO在确保安全性的同时显著提升操作效率。此外，计算性能验证了其在真实应用中的可行性。
原文摘要 · Abstract (English)
This paper presents a Segmented Trajectory Optimization (STO) method for autonomous parking, which refines an initial trajectory into a dynamically feasible and collision-free one using an iterative SQP-based approach. STO maintains the maneuver strategy of the high-level global planner while allowing curvature discontinuities at switching points to improve maneuver efficiency. To ensure safety, a convex corridor is constructed via GJK-accelerated ellipse shrinking and expansion, serving as safety constraints in each iteration. Numerical simulations in perpendicular and reverse-angled parking scenarios demonstrate that STO enhances maneuver efficiency while ensuring safety. Moreover, computational performance confirms its practicality for real-world applications.
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