DRCC-LPVMPC: Robust Data-Driven Control for Autonomous Driving and Obstacle Avoidance

TL;DR

This paper introduces a robust control framework for autonomous driving that explicitly manages model uncertainties using distributionally robust chance constraints, improving safety and reliability in obstacle avoidance.

Contribution

It proposes a novel DRCC-LPVMPC method that accounts for model mismatches with Wasserstein ambiguity sets, ensuring real-time solvability and recursive feasibility.

Findings

Enhanced obstacle clearance safety.

More reliable tracking under uncertainties.

Real-time implementation demonstrated.

Abstract

Safety in obstacle avoidance is critical for autonomous driving. While model predictive control (MPC) is widely used, simplified prediction models such as linearized or single-track vehicle models introduce discrepancies between predicted and actual behavior that can compromise safety. This paper proposes a distributionally robust chance-constrained linear parameter-varying MPC (DRCC-LPVMPC) framework that explicitly accounts for such discrepancies. The single-track vehicle dynamics are represented in a quasi-linear parameter-varying (quasi-LPV) form, with model mismatches treated as additive uncertainties of unknown distribution. By constructing chance constraints from finite sampled data and employing a Wasserstein ambiguity set, the proposed method avoids restrictive assumptions on boundedness or Gaussian distributions. The resulting DRCC problem is reformulated as tractable convex constraints and solved in real time using a quadratic programming solver. Recursive feasibility of the approach is formally established. Simulation and real-world experiments demonstrate that DRCC-LPVMPC maintains safer obstacle clearance and more reliable tracking than conventional nonlinear MPC and LPVMPC controllers under significant uncertainties.