A Data-Driven Predictive Control Driver for Racing Car Simulation

The capability to accurately simulate the behavior of a racing car is paramount in modern-day racing competitions to quickly find a good base setup to kick-start the work on track. Typically, a professional driver is employed to drive the simulated race car and provide feedback. However, this operation is expensive and time-consuming, as capable human drivers quickly become a bottleneck. In conjunction with highly accurate simulations of the physical car's behavior, a capable virtual driver could thus accelerate the car setup and development to a great extent. In this paper, we propose to apply a data-driven predictive control approach called Data-enabled Predictive Control to model a racing driver by tracking a pre-defined trajectory. We compare our proposed approach with an industrial first-choice Proportional-Integral-Derivative controller and state-of-the-art Model Predictive Control controller, finding that the approach is feasible, and it can provide significant improvements over the state-of-the-art, especially for trajectories whose feasibility is at the edge of the car's capabilities.