Generating Realistic Vehicle Trajectories Based on Vehicle–Vehicle and Vehicle–Map Interaction Pattern Learning

Diversified and realistic traffic scenarios are a crucial foundation for evaluating the safety of autonomous driving systems in simulations. However, a considerable number of current methods generate scenarios that lack sufficient realism. To address this issue, this paper proposes a vehicle trajectory generation method based on vehicle–vehicle and vehicle–map interaction pattern learning. By leveraging a multihead self-attention mechanism, the model efficiently captures complex dependencies among vehicles, enhancing its ability to learn realistic traffic dynamics. Moreover, the multihead cross-attention mechanism is also used to learn the interaction features between the vehicles and the map, addressing the challenge of trajectory generation’s difficulty in perceiving static environments. This proposed method enhances the model’s ability to learn natural traffic sequences, enable the generation of more realistic traffic flow, and provide strong support for the testing and optimization of autonomous driving systems. Experimental results show that compared to the Trafficgen baseline model, the proposed method achieves a 26% improvement in ADE and a 20% improvement in FDE.