Trajectory Planning for Autonomous Driving in Unstructured Scenarios Based on Graph Neural Network and Numerical Optimization

TL;DR

This work addresses trajectory planning for autonomous driving in unstructured environments by combining a Graph Neural Network (GNN) with numerical optimization. The method uses offline data to train a GNN to predict an initial trajectory, which serves as the starting point for an optimal-control problem solved with IPOPT to guarantee vehicle dynamics and obstacle avoidance constraints. Empirically, the GNN-OCP framework streamlines planning, achieving lower total planning times and high-quality locally optimal trajectories compared with traditional Hybrid A*-OCP and RRT*-OCP baselines. The approach leverages environment-aware graph representations to deliver fast, feasible trajectories and highlights the potential of integrating end-to-end-like prediction with principled optimization for robust autonomous driving in complex, unstructured scenarios.

Abstract

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.

Figures (11)

• Figure 1: Overall framework
• Figure 2: Network architecture schematic diagram
• Figure 3: Vectorization
• Figure 4: Geometric parameters related to vehicle kinematics.
• Figure 5: Triangle area method