Trajectory Prediction for Automated Vehicles on Roads With Lanes Partially Covered by Ice or Snow

Abstract

Snow and ice films obscure lane lines during the winter, leading to inaccurate trajectory predictions and reducing a driver's ability to guide the vehicle. The full use of observable information to continuously infer trajectories is an urgent issue in trajectory prediction tasks. In this paper, a neighboring vehicle trajectory prediction method based on graph neural networks (GNNs) is proposed, the lane line positions and attribute information in high-definition (HD) maps are extracted, and an ice and snow mask mechanism that simulates instances in which lane lines are covered is introduced. Because graph aggregation with a GNN enables information exchange between nodes, a graph query mechanism that integrates local graph information and guides prediction results based on the local states of the nearest observable nodes is proposed. In addition, a multihead attention mechanism projects neighboring states to the graph, describing interactions between vehicles and the state of the traffic flow, and a two-layer graph attention network (GAT) enables information aggregation and describes node correlations. Experiments on the nuScenes dataset show that our prediction method outperforms state-of-the-art prediction systems, including Traj++, SG-NET, and P2T, when lane lines are occluded. Vehicle trajectories on roads covered by ice and snow can be accurately predicted based on observable information.