一种基于交互式时空走廊的多车协调运动规划分层方法
A Hierarchical Multi-Vehicle Coordinated Motion Planning Method based on Interactive Spatio-Temporal Corridors
AI总结：
提出基于交互式时空走廊（ISTCs）的多车协调运动规划分层方法，通过混合整数二次规划构建走廊解耦高维冲突，再用非线性规划生成满足动力学的轨迹，有效降低计算压力并提升真实场景适用性。
摘要：
多车协调运动规划一直面临在非完整动态约束下安全高效解决冲突的挑战。为多车构建时空走廊可以解耦高维冲突，并进一步降低获取可行轨迹的难度。因此，本文提出了一种基于交互式时空走廊（ISTCs）的新型分层方法。在第一层，基于初始引导轨迹，设计混合整数二次规划来构建ISTCs，能够解决一般多车场景中的冲突。然后在第二层，采用非线性规划生成满足车辆动力学的走廊内轨迹。通过引入ISTCs，多车协调运动规划问题能够被解耦为单车轨迹优化问题，这极大地分散了计算压力，在现实应用中具有巨大潜力。此外，所提方法在三维$(x,y,t)$配置空间中搜索可行解，比传统的速度-路径解耦方法保留了更多可能性。已在无信号交叉口和具有挑战性的密集场景中进行了仿真实验，验证了所提框架的可行性和适应性。
英文摘要：
Multi-vehicle coordinated motion planning has always been challenged to safely and efficiently resolve conflicts under non-holonomic dynamic constraints. Constructing spatial-temporal corridors for multi-vehicle can decouple the high-dimensional conflicts and further reduce the difficulty of obtaining feasible trajectories. Therefore, this paper proposes a novel hierarchical method based on interactive spatio-temporal corridors (ISTCs). In the first layer, based on the initial guidance trajectories, Mixed Integer Quadratic Programming is designed to construct ISTCs capable of resolving conflicts in generic multi-vehicle scenarios. And then in the second layer, Non-Linear Programming is settled to generate in-corridor trajectories that satisfy the vehicle dynamics. By introducing ISTCs, the multi-vehicle coordinated motion planning problem is able to be decoupled into single-vehicle trajectory optimization problems, which greatly decentralizes the computational pressure and has great potential for real-world applications. Besides, the proposed method searches for feasible solutions in the 3-D $(x,y,t)$ configuration space, preserving more possibilities than the traditional velocity-path decoupling method. Simulated experiments in unsignalized intersection and challenging dense scenarios have been conduced to verify the feasibility and adaptability of the proposed framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
