Learning Graph Neural Architectures for Heterogeneous Multi-Agent Trajectory Prediction via Automated Search

Most existing deep learning-based trajectory prediction algorithms heavily rely on human expertise, involving iterative manual tuning of their architectures and parameters to tailor prediction models for specific tasks or scenarios. This approach is not only complex to implement and inefficient, but also struggles to balance inference speed with prediction accuracy. To address this challenge, this paper innovatively proposes an improved heterogeneous multi-agent trajectory prediction algorithm utilizing graph neural architecture search. This method automatically conducts an end-to-end graph architecture search to obtain an optimal trajectory prediction model. To enhance model interpretability and its heterogeneous awareness of diverse scenarios, we design a physics- and risk-interaction-based guidance mechanism to steer the architecture search process. Furthermore, we construct a novel neural architecture search loss function, SocialMI-Loss, which comprehensively considers multiple factors such as prediction accuracy, driving region semantic constraints, and model complexity. This function is intended to guide the learning of the trajectory predictor, achieving a harmonious balance between accuracy and computational complexity. A comprehensive series of comparative experiments conducted on three large-scale autonomous driving datasets (nuScenes, Argoverse, and ApolloScape) consistently demonstrates the superior performance of our proposed method. Experimental results indicate that our framework achieves performance comparable to current state-of-the-art methods, while its automatically searched architecture remains remarkably lightweight. Our code is available at:https://github.com/Tu5tra/TrajGNAS.