Experimental Validation of Safe MPC for Autonomous Driving in Uncertain Environments
Journal article, 2023
The full deployment of autonomous driving systems on a worldwide scale requires that the self-driving vehicle can be operated in a provably safe manner, i.e., the vehicle must be able to avoid collisions in any possible traffic situation. In this article, 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.
Roads
Trajectory
Safety
Autonomous vehicles
uncertain constraints
Predictive models
Behavioral sciences
recursive feasibility
nonlinear predictive control
safety
Autonomous driving
Planning
Author
Ivo Batkovic
Chalmers, Electrical Engineering, Systems and control
Ankit Gupta
Zenseact AB
Mario Zanon
IMT School for Advanced Studies
Paolo Falcone
Chalmers, Electrical Engineering, Systems and control
IEEE Transactions on Control Systems Technology
1063-6536 (ISSN) 15580865 (eISSN)
Vol. 31 5 2027-2042Subject Categories
Software Engineering
Robotics
Control Engineering
DOI
10.1109/TCST.2023.3291562