LTP: Lane-Based Trajectory Prediction for Autonomous Driving

Jingke Wang, Tengju Ye, Ziqing Gu, Junbo Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 17134-17142


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.

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@InProceedings{Wang_2022_CVPR, author = {Wang, Jingke and Ye, Tengju and Gu, Ziqing and Chen, Junbo}, title = {LTP: Lane-Based Trajectory Prediction for Autonomous Driving}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {17134-17142} }