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.

翻译：在非结构化环境中，障碍物形态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。传统轨迹规划方法通常包含路径规划、速度规划和轨迹优化等多个阶段，这些方法需要为每个阶段手动设计大量参数，导致工作量和计算负担显著增加。端到端轨迹规划方法虽简单高效，但在非结构化场景下往往难以保证轨迹满足车辆动力学和避障约束。为此，本文提出一种基于图神经网络（GNN）与数值优化的新型轨迹规划方法。该方法包含两个阶段：（1）利用GNN进行初始轨迹预测；（2）通过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，以替代传统路径与速度规划。该预测轨迹作为数值优化阶段的初始解，通过优化确保轨迹满足车辆动力学与避障约束。我们进行了仿真实验验证所提算法的可行性，并与主流规划算法进行对比。结果表明，所提方法简化了轨迹规划流程，并显著提升了规划效率。