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.
A Multi-Task Learning Network With a Collision-Aware Graph Transformer for Traffic-Agents Trajectory Prediction
IEEE Transactions on Intelligent Transportation Systems ; 25 , 7 ; 6677-6690
2024-07-01
3991473 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Trajectory-Based Spatiotemporal Multi-Task Multi-Graph Network for Traffic State Prediction
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