Deep Reinforcement-Learning-Guided Model Predictive Control for Preventing Overtakes in Autonomous Racing

Abstract:This paper addresses defensive blocking in autonomous racing, where a vehicle must prevent a faster opponent from overtaking while operating near its dynamic limits. Different from lap-time minimization, we formulate defense as a spatial occupancy regulation problem via a hierarchical reinforcement-learning guided model predictive control framework. A Soft Actor-Critic strategic layer operates in the Frenet domain to generate geometry-aware defensive references, which are embedded into the nonlinear model predictive control formulation as spatial regularization under friction constraints. Evaluated on the Thunderhill West circuit in simulation, the framework increases average overtake time from 8.8 s to 14.6 s while significantly reducing opponent progress. Meanwhile, it allows the vehicle to utilize 83.4% of available tire force. The framework achieves a 33.3 ms mean solve time (13.9 ms std), supporting real-time high-speed adversarial interaction.

Accepted to the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026). 8 pages, 7 figures

Robotics (cs.RO)

arXiv:2607.20973 [cs.RO]

(or arXiv:2607.20973v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2607.20973 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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