U-DiffPlan: Uncertainty-Aware Trajectory Prediction and Planning via Diffusion for Strategic Autonomous Racing

Autonomous racing demands not only high-speed trajectory planning but also strategic reasoning under uncertainty. Unlike urban driving, autonomous racing involves more complex and adversarial strategic interactions. In this work, we introduce U-DiffPlan, an uncertainty-aware prediction and planning framework designed for competitive racing scenarios. U-DiffPlan consists of two modules. A predictor module, the first, jointly estimates the future trajectories and the strategies of the target vehicle using a conditional diffusion model and an auxiliary encoder. A planner module, the second, determines the ego vehicle's optimal strategy using a mixed-strategy game-theoretic formulation and plans its trajectory via chance-constrained time-optimal model predictive control, which incorporates prediction uncertainty through chance constraints. Extensive experiments were conducted across diverse racing scenarios demonstrating that U-DiffPlan outperforms baseline approaches in trajectory prediction and race competitiveness. The proposed model achieves more effective overtaking and blocking strategies in the presence of probabilistic opponent behavior, while remaining computationally efficient for real-time inference.