Learning from Shared-Control Overrides: Context-Driven Acceleration Profile Prediction for Personalized Overtaking

Overtaking on two-lane roads is a safety-critical decision-making problem for autonomous vehicles, since oncoming traffic may force the ego vehicle to abort and merge back to its original lane. Deep reinforcement learning (DRL) is promising for such continuous-control tasks, but it requires substantial interaction data and may produce unsafe exploratory actions during training. Model-based controllers provide structured behavior, but depend on model fidelity and hand-designed logic. This paper proposes a fading expert-guidance framework that uses a model-based controller to guide DRL training for autonomous overtaking. The expert combines a constrained iterative LQR (CiLQR) planner with PID-based auxiliary controllers, activated when the optimized plan becomes infeasible. The expert action enters the actor objective through a fading term, interpreted as a time-varying soft trust region around the expert policy. This term first biases the policy toward the expert and then vanishes, allowing optimization according to the reinforcement-learning objective. The method is evaluated with PPO, TD3, and SAC. Simulations with bidirectional traffic show improved sample efficiency and final task performance. The safety effects are algorithm-dependent: guidance reduces vehicle collisions for SAC, eliminates boundary collisions for TD3, and leaves the PPO collision profile unchanged. Overall, the results support fading expert guidance as a practical mechanism for transferring model-based driving knowledge to learning-based controllers without restricting the final policy to imitation in an abortable overtaking task with bidirectional traffic.

This paper presents Adaptive Differentiable Model Predictive Contouring Control (AD-MPCC), a framework for autonomous racing that integrates differentiable MPCC with online parameter estimation to handle varying road-surface conditions. For online parameter estimation, we leverage a parameterized Pacejka Magic Formula together with a regularized moving-horizon estimation scheme with exponentially decaying weights to capture road interactions and update parameters in real time. Furthermore, we propose a differentiable MPCC (Diff-MPCC) framework that enables optimal adjustment of objective weights based on predefined long-horizon performance costs. To implement Diff-MPCC for online objective weight adaptation, we propose a Pacejka-informed machine learning model that is trained in a supervised manner using data generated by Diff-MPCC to tune the objective weights. Simulation results demonstrate that AD-MPCC reliably ensures safety and achieves faster lap times compared to baseline controllers in both single-surface and multiple-surface scenarios.

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.