A Safe Control Architecture Based on Robust Model Predictive Control for Autonomous Driving

TL;DR

This paper introduces a Robust Safe Control Architecture for autonomous vehicles that ensures safety through a dual MPC system, providing safety guarantees even under disturbances and unsafe conditions.

Contribution

It presents a novel RSCA framework combining Supervisor and Controller TMPCs with a robust terminal set computation, ensuring safety and recursive feasibility.

Findings

The RSCA guarantees safe vehicle operation under bounded disturbances.

Simulation results confirm the effectiveness of the proposed safety architecture.

The method ensures recursive feasibility and safety certificates for autonomous driving.

Abstract

This paper proposes a Robust Safe Control Architecture (RSCA) for safe-decision making. The system to be controlled is a vehicle in the presence of bounded disturbances. The RSCA consists of two parts: a Supervisor MPC and a Controller MPC. Both the Supervisor and the Controller are tube MPCs (TMPCs). The Supervisor MPC provides a safety certificate for an operating controller and a backup control input in every step. After an unsafe action by the operating controller is predicted, the Controller MPC takes over the system. In this paper, a method for the computation of a terminal set is proposed, which is robust against changes in road curvature and forces the vehicle to reach a safe reference. Moreover, two important proofs are provided in this paper. First, it is shown that the backup control input is safe to be applied to the system to lead the vehicle to a safe state. Next, the recursive feasibility of the RSCA is proven. By simulating some obstacle avoidance scenarios, the effectiveness of the proposed RSCA is confirmed.