Abstract: Precisely perceiving the future actions of surrounding traffic agents is critical for ensuring the safety of autonomous vehicle. This paper mainly focuses on the complicated problem of long-term multi-agent trajectory prediction. Existing trajectory prediction methods can be categorized into joint prediction and marginal prediction. Although joint prediction methods achieve better scene consistency in multi-agent scenarios, both of them fail to reach satisfactory results in long-term prediction tasks due to their inability to capture changes in driver behavior over time. In this paper, we introduce a novel joint prediction method called Trajectory Prediction through Post-processing (TPP). The core of this method is the trajectory post-processing module, which utilizes attention mechanisms to model interactions among different vehicles. By representing a single vehicle with multiple embeddings, the module also simulates the changes in behavior during driving. With the help of post-processing module, our method is able to generate consistent multi-agent trajectories. We evaluate TPP on a short-term prediction dataset and a long-term prediction dataset separately, comparing it with mainstream trajectory prediction methods. The results indicate that TPP achieves better performance.

Key words: vehicle trajectory prediction, long-term joint prediction, trajectory post-processing, deep learning, attention-mechanism