A Multi-Task Learning Network With a Collision-Aware Graph Transformer for Traffic-Agents Trajectory Prediction

Abstract

It is critical for autonomous vehicles to accurately forecast the future trajectories of surrounding agents to avoid collisions. However, capturing the complex interactions between agents in complex urban scenes is challenging. As a result, complex interactions may impair trajectory prediction accuracy. A trajectory prediction network with an enhanced Graph Transformer (TP-EGT) is proposed to forecast the future trajectories of traffic-agents. A collision-aware Graph Transformer is introduced to capture the complex social interactions between traffic-agents. Following that, an additional interaction prediction task that could predict the interaction probabilities between agents is proposed to mitigate the over-smoothing issue of the Graph Transformer via a multi-task learning strategy. Afterward, the trajectory prediction performance is improved with additional interaction probabilities, which are beneficial for the decision-making and planning modules of autonomous vehicles. Quantitative and qualitative evaluations of TP-EGT on the ETH/UCY and ApolloScape databases demonstrate that the trajectory prediction accuracy of TP-EGT is comparable to the state-of-the-art baseline methods, and the predicted interaction probabilities can help autonomous vehicles comprehend the complex traffic scenes.