Experimental Validation of Safe MPC for Autonomous Driving in Uncertain Environments
Artikel i vetenskaplig tidskrift, 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
Författare
Ivo Batkovic
Chalmers, Elektroteknik, System- och reglerteknik
Ankit Gupta
Zenseact AB
Mario Zanon
IMT Alti Studi Lucca
Paolo Falcone
Chalmers, Elektroteknik, System- och reglerteknik
IEEE Transactions on Control Systems Technology
1063-6536 (ISSN) 15580865 (eISSN)
Vol. 31 5 2027-2042Ämneskategorier
Programvaruteknik
Robotteknik och automation
Reglerteknik
DOI
10.1109/TCST.2023.3291562