A Safe Control Architecture Based on a Model Predictive Control Supervisor for Autonomous Driving

TL;DR

This paper introduces a safe control architecture for autonomous driving that uses a Model Predictive Controller as a supervisor to ensure safety by intervening in potentially hazardous control inputs.

Contribution

The novel safe control architecture integrates MPC as a supervisor to enhance safety in autonomous systems, addressing model mismatch issues.

Findings

Effective safety supervision in autonomous driving scenarios

Comparison of safety interventions in obstacle avoidance

Strategies to mitigate model mismatch effects

Abstract

This paper presents a novel, safe control architecture (SCA) for controlling an important class of systems: safety-critical systems. Ensuring the safety of control decisions has always been a challenge in automatic control. The proposed SCA aims to address this challenge by using a Model Predictive Controller (MPC) that acts as a supervisor for the operating controller, in the sense that the MPC constantly checks the safety of the control inputs generated by the operating controller and intervenes if the control input is predicted to lead to a hazardous situation in the foreseeable future invariably. Then an appropriate backup scheme can be activated, e.g., a degraded control mechanism, the transfer of the system to a safe state, or a warning signal issued to a human supervisor. For a proof of concept, the proposed SCA is applied to an autonomous driving scenario, where it is illustrated and compared in different obstacle avoidance scenarios. A major challenge of the SCA lies in the mismatch between the MPC prediction model and the real system, for which possible remedies are explored.