Experimental Validation of Safe MPC for Autonomous Driving in Uncertain Environments

TL;DR

This paper presents a validated Model Predictive Control framework that guarantees safety constraints for autonomous vehicles in uncertain environments, demonstrated through simulations and real-world experiments.

Contribution

It introduces a safe MPC framework with proven safety guarantees for autonomous driving, validated via both simulations and real vehicle tests.

Findings

Framework ensures constraint satisfaction at all times

Demonstrated safe driving behavior in real-world tests

Effective in uncertain traffic environments

Abstract

The full deployment of autonomous driving systems on a worldwide scale requires that the self-driving vehicle be operated in a provably safe manner, i.e., the vehicle must be able to avoid collisions in any possible traffic situation. In this paper, we propose a framework based on Model Predictive Control (MPC) that endows the self-driving vehicle with the necessary safety guarantees. In particular, our framework ensures constraint satisfaction at all times, while tracking the reference trajectory as close as obstacles allow, resulting in a safe and comfortable driving behavior. To discuss the performance and real-time capability of our framework, we provide first an illustrative simulation example, and then we demonstrate the effectiveness of our framework in experiments with a real test vehicle.