Safe Autonomous Driving via Data-Driven Robust Model Predictive Control

Abstract

Safe navigation in autonomous driving requires reliable obstacle avoidance despite modeling errors and sensing disturbances. Model predictive control (MPC) is widely used for this task, but simplified prediction models can lead to deviations between predicted trajectories and the actual vehicle motion. This paper presents a data-driven robust MPC framework for autonomous driving that improves safety under such uncertainties. Vehicle dynamics are described using a quasi-linear parameter-varying (LPV) bicycle model, where discrepancies between the model and the real system are captured as additive disturbances with unknown distributions. To handle these uncertainties, a distributionally robust chance-constrained formulation based on a Wasserstein ambiguity set is introduced, allowing safety constraints to be enforced probabilistically without assuming specific disturbance distributions. The resulting control problem is formulated as a quadratic program that can be solved efficiently for real-time implementation. Simulation studies and experiments on a scaled autonomous vehicle demonstrate that the proposed approach increases obstacle clearance and improves trajectory tracking consistency compared with nonlinear MPC and conventional LPV-MPC controllers.