基于图神经网络与数值优化的非结构化场景自动驾驶轨迹规划
Trajectory Planning for Autonomous Driving in Unstructured Scenarios Based on Graph Neural Network and Numerical Optimization
AI总结：
针对非结构化场景中传统多阶段轨迹规划参数设计繁琐、端到端方法难以满足动力学与避障约束的问题，本文提出一种结合图神经网络初始轨迹预测与数值优化轨迹修正的两阶段方法，仿真实验表明其简化了规划流程并显著提升了规划效率。
摘要：
在非结构化环境中，障碍物种类多样且缺乏车道标线，使得智能车辆的轨迹规划成为一项具有挑战性的任务。传统的轨迹规划方法通常包含多个阶段，包括路径规划、速度规划和轨迹优化。这些方法需要为每个阶段人工设计大量参数，导致工作量和计算负担显著增加。端到端轨迹规划方法虽然简单高效，但在非结构化场景中往往难以保证轨迹满足车辆动力学和避障约束。为此，本文提出了一种基于图神经网络（GNN）和数值优化的新型轨迹规划方法。该方法由两个阶段组成：（1）利用图神经网络进行初始轨迹预测，（2）利用数值优化进行轨迹优化。首先，图神经网络处理环境信息并预测粗略轨迹，取代传统的路径规划和速度规划。该预测轨迹作为数值优化阶段的初始解，数值优化阶段对轨迹进行优化，以确保满足车辆动力学和避障约束。我们进行了仿真实验以验证所提算法的可行性，并将其与其他主流规划算法进行了对比。结果表明，所提方法简化了轨迹规划流程，并显著提高了规划效率。
英文摘要：
In unstructured environments, obstacles are diverse and lack lane markings, making trajectory planning for intelligent vehicles a challenging task. Traditional trajectory planning methods typically involve multiple stages, including path planning, speed planning, and trajectory optimization. These methods require the manual design of numerous parameters for each stage, resulting in significant workload and computational burden. While end-to-end trajectory planning methods are simple and efficient, they often fail to ensure that the trajectory meets vehicle dynamics and obstacle avoidance constraints in unstructured scenarios. Therefore, this paper proposes a novel trajectory planning method based on Graph Neural Networks (GNN) and numerical optimization. The proposed method consists of two stages: (1) initial trajectory prediction using the GNN, (2) trajectory optimization using numerical optimization. First, the graph neural network processes the environment information and predicts a rough trajectory, replacing traditional path and speed planning. This predicted trajectory serves as the initial solution for the numerical optimization stage, which optimizes the trajectory to ensure compliance with vehicle dynamics and obstacle avoidance constraints. We conducted simulation experiments to validate the feasibility of the proposed algorithm and compared it with other mainstream planning algorithms. The results demonstrate that the proposed method simplifies the trajectory planning process and significantly improves planning efficiency.
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。
