LTP: Lane-based Trajectory Prediction for Autonomous Driving
Jingke Wang, Tengju Ye, Ziqing Gu, Junbo Chen
Abstract
The reasonable trajectory prediction of surrounding traf-fic participants is crucial for autonomous driving. Espe-cially, 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, com-monly using intention classification followed by motion re-gression. This paper proposes a two-stage proposal-based motion forecasting method that exploits the sliced lane seg-ments as fine-grained, shareable, and interpretable propos-als. We use Graph neural network and Transformer to en-code 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. More-over, 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.