LTP: Lane-Based Trajectory Prediction for Autonomous Driving

Abstract

The reasonable trajectory prediction of surrounding traffic participants is crucial for autonomous driving. Especially, how to predict multiple plausible trajectories is still a challenging problem because of the multiple possibilities of the future. Proposal-based prediction methods address the multi-modality issues with a two-stage approach, commonly using intention classification followed by motion regression. This paper proposes a two-stage proposal-based motion forecasting method that exploits the sliced lane segments as fine-grained, shareable, and interpretable proposals. We use Graph neural network and Transformer to encode the shape and interaction information among the map sub-graphs and the agents sub-graphs. In addition, we propose a variance-based non-maximum suppression strategy to select representative trajectories that ensure the diversity of the final output. Experiments on the Argoverse dataset show that the proposed method outperforms state-of-the-art methods, and the lane segments-based proposals as well as the variance-based non-maximum suppression strategy both contribute to the performance improvement. Moreover, we demonstrate that the proposed method can achieve reliable performance with a lower collision rate and fewer off-road scenarios in the closed-loop simulation.