DRCC-LPVMPC: Robust Data-Driven Control for Autonomous Driving and Obstacle Avoidance
The DRCC-LPVMPC framework addresses safety in autonomous driving by handling model errors and disturbances using a data-driven, distributionally robust chance-constrained approach. It reformulates hard constraints into probabilistic ones based on sampled data and a Wasserstein ambiguity set, allowing for real-time obstacle avoidance under uncertainty.