| Paper FR-LA-T44.3
Furuhashi, Fumihito (The University of Tokyo)
DG-PINN: Differential Game Based Physics-Informed Neural Network for Vehicle Trajectory Prediction
Scheduled for presentation during the Regular Session " S 4 4 c - H u m a n F a c t o r s a n d H u m a n M a c h i n e I n t e r a c t i o n i n A u t o m a t e d D r i v i n g" (FR-LA-T44), Friday, November 21, 2025,
16:40−17:00, Currumbin
2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia
This information is tentative and subject to change. Compiled on October 18, 2025
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| Keywords Data Analytics and Real-time Decision Making for Autonomous Traffic Management, User-Centric HMI Design for Autonomous Vehicle Control Systems
Abstract
This paper introduces the Differential-Game based Physics-Informed Neural Network (DG-PINN), a trajectory-prediction model that embeds a multi-agent differential-game formulation into a physics-informed neural network. By treating each vehicle as a strategic agent, DG-PINN simultaneously captures inter-vehicle interactions and individual utilities, thereby unifying longitudinal car-following and lateral lane-changing behaviors within a single, interpretable framework. Physical laws derived from the Hamilton-Jacobi-Bellman equations are incorporated as loss regularizers, enabling the model to retain the data-efficiency and interpretability of physics-based methods while leveraging the representational power of deep learning. We evaluate DG-PINN on the NGSIM I-80 highway dataset under varying training-sample regimes (20-100 sequences) and compare it with constant-velocity/acceleration baselines, a Physics Uninformed Neural Network (PUNN), and an IDM-based PINN. DG-PINN achieves the lowest root-mean-square error, average displacement error, and final displacement error across all sample sizes, outperforming IDM-PINN and PUNN by up to 19 % in low-data settings while exhibiting markedly lower variance. Moreover, the learned utility parameters allow quantitative profiling of driver traits—such as safety-mindedness and aggressiveness—and visualize the rationality of observed versus hypothetical maneuvers. The results demonstrate that integrating differential-game theory with PINNs not only improves prediction accuracy and robustness but also yields actionable insights for explainable decision-making in autonomous-vehicle systems.
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