用拓扑不变量控制多车交互轨迹，让相同起点生成不同互动模式。
Topology-Driven Trajectory Optimization for Modelling Controllable Interactions Within Multi-Vehicle Scenario
- 引入可微分的局部同伦不变量建模车辆交互
- 同一初始值下生成多种可控交互轨迹，优于现有方法
- 适合需要精确交互控制的自动驾驶场景
多车场景中的轨迹优化因非线性、非凸特性及对初值敏感而难以控制车辆间交互。受拓扑规划启发，本文提出一种可微分的局部同伦不变量度量来建模交互。将该拓扑度量作为约束引入多车轨迹优化框架，可在相同初始条件下生成多种交互轨迹，实现可控交互并支持用户设计的交互模式。大量实验表明，该方法在最优性和效率上均优于现有方法。代码将开源，以推动相关研究。
原文摘要 · Abstract (English)
Trajectory optimization in multi-vehicle scenarios faces challenges due to its non-linear, non-convex properties and sensitivity to initial values, making interactions between vehicles difficult to control. In this paper, inspired by topological planning, we propose a differentiable local homotopy invariant metric to model the interactions. By incorporating this topological metric as a constraint into multi-vehicle trajectory optimization, our framework is capable of generating multiple interactive trajectories from the same initial values, achieving controllable interactions as well as supporting user-designed interaction patterns. Extensive experiments demonstrate its superior optimality and efficiency over existing methods. We will release open-source code to advance relative research.
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