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| Heterogeneous Agent Trajectory Prediction Method Based on Safety-Enhanced Interaction Modeling |
| LI Xixi1,2, REN Minglun1,2 |
1. School of Management and Intelligence Science, Hefei University of Technology, Hefei 230009;
2. Key Laboratory of Process Optimization and Intelligent Decision Making, Ministry of Education, Hefei University of Technology, Hefei 230009 |
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Abstract In complex scenarios such as urban unsignalized intersections, autonomous vehicles interact intensively with heterogeneous agents. Synchronous trajectory prediction of heterogeneous agents is crucial for driving safety and efficiency. Existing methods neglect dynamic risk factors during interactions, resulting in insufficient prediction safety and accuracy. A heterogeneous agent trajectory prediction method based on safety-enhanced interaction modeling(SEIM) is proposed. Interaction risk, heterogeneous interaction characteristics, road environment, and individual dynamics are jointly considered. In the risk perception layer, interaction risks are characterized via the safety potential field and interaction risk features are extracted. In the heterogeneous interaction layer, two heterogeneous graphs are constructed and a heterogeneous interaction encoder is designed to capture heterogeneous interaction features. Safety potential field-based risk values are introduced as edge attributes of the safety- enhanced directed edge-feature heterogeneous graph to enhance edge features of the graph structure. In the road environment layer, road structure features are extracted from the directed graph. In the individual dynamic layer, individual motion features are captured. The four types of features are fused and fed into trajectory decoders to predict future trajectories of heterogeneous agents. Experiments on the inD dataset demonstrate the effectiveness and accuracy of SEIM in heterogeneous agent trajectory prediction.
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Received: 15 June 2026
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| Fund:National Natural Science Foundation of China(No.72371087), Basic Science Center Program of National Natural Science Foundation of China(No.72188101) |
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Corresponding Authors:
REN Minglun, Ph.D., professor. His research interests include theories and methods of cognitive intelligence and human-machine collaborative decision-making.
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2026, Vol. 39 
