S-CVAE: Stacked CVAE for Trajectory Prediction With Incremental Greedy Region

Yuzhen Zhang, Junning Su, Hang Guo, Chaochao Li, Pei Lv, Mingliang Xu

Abstract: Predicting accurate future trajectories of agents is essential for autonomous navigation in complex scenarios. Although numerous work has made great progress on this goal, it is still challenging due to the uncertainty and continuity of behavioral intentions of agents, where uncertainty means the instantaneous multimodality of motion behavior, while the continuity refers to the consistency and stability of behavioral intention of an agent over a period of time constrained by its final destination. These factors easily affect the improvement of prediction accuracy. In this paper, we present a novel trajectory prediction method, Stacked Conditional VAE (S-CVAE) with Incremental Greedy Region (IGR). Specifically, the IGR is designed to enlarge the coverage of candidate waypoints/endpoints by reformulating the waypoints/endpoints prediction problem as candidate region generation, which can further encourage and model multimodality of behavioral intentions. Meanwhile, to exploit the inherent continuity between adjacent behavioral intentions of an agent, the S-CVAE architecture is constructed to transmit the behavioral intentions of one agent by inserting intermediate waypoints with IGR into the potential trajectories from the observed path to the final endpoint, and also enhances the reliability of the generated waypoints/endpoints in the next moment, further improve the accuracy of trajectory prediction. Our method is evaluated on several public datasets, including nuScenes, Apolloscape, SDD, INTERSECTION, Waymo, and VTPTL. The comprehensive experimental results demonstrate that our method achieves significant performance on these datasets. Especially in nuScenes and VTPTL, the accuracy is increased by at least 11.11% on average ADE and 2.40% on average FDE compared with state-of-the-arts.