Orientational information matters in trajectory prediction

Zhenyu Tong; Yue Zhou

doi:10.1117/12.2667812

28 March 2023 Orientational information matters in trajectory prediction

Zhenyu Tong, Yue Zhou

Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 125660T (2023) https://doi.org/10.1117/12.2667812
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China

Abstract

Trajectory prediction is crucial for collision avoidance and path planning in the autonomous driving task stacks. Numerous models have been proposed to learn the complicated spatiotemporal and social interactions among traffic agents. However, we find that some models perform badly in some corner scenarios, and it is related to insufficient supervision using single mean squared error (MSE) loss. Therefore, we propose the orientational MSE (OMSE) loss for better trajectory prediction. We introduce the global proxy matching strategy in OMSE loss to enable model to optimize with supervision from both Euclidean distance and orientation information. We evaluate OMSE loss on two public trajectory datasets for pedestrians and vehicles. Both quantitative and qualitative results illustrate the improvement on forecasting performance on cases of interest. The proposed method works in a learnable manner and surpasses traditional post-processing methods with more flexibility.

Citation Download Citation

Zhenyu Tong and Yue Zhou "Orientational information matters in trajectory prediction", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 125660T (28 March 2023); https://doi.org/10.1117/12.2667812

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