End-Point Drive and Reverse Enhanced Decoding- Based Traffic Participants Trajectory Prediction Under Bird’s Eye View

Trajectory prediction under Bird’s Eye View (BEV) refers to predicting the movement intention of agents based on the historical observation trajectories, which is of great significance for autonomous driving, driving safety and social navigation. The traffic prediction trajectory is multimodality with multiple reasonable trajectories and various prediction time, which often suffer complex agents interaction, cumulative errors and a large number of agents. To overcome these problems, we explore the BEV-based traffic participants trajectory prediction problem and propose the novel End-point Drive and Reverse Enhanced Decoding Network (EDRED-TPNet), based on the mechanisms of end-point driving and reverse enhanced decoding. Firstly, the Encoder based on Dynamic Spatio-temporal Graph and Multimodality Coding Fusion (ST-MC-Encoder) are constructed to effectively represent complex traffic scenario with changeable agents, encode social interactions with historical trajectories, social interaction, future trajectory and multimodality. Secondly, the End-Point Drive Module is proposed to predict the end point before predicting the complete trajectory, thus providing more accurate trajectory prediction; Lastly, to further improve the long-term prediction performance, the Reverse Enhanced Decoder (RE-Decoder) is proposed to fuse forward and reverse hidden state vectors to obtain diverse trajectories that conform to physical and social acceptability rules. We build the first AAV-captured Trajectory Prediction Dataset (UTP-Dataset) for traffic participants trajectory prediction. Experimental results show that the proposed methods can fulfill the multi-target trajectory prediction task in complex traffic scenarios and achieve high performance.