Enabling Safe Autonomous Driving in Uncertain Environments
This thesis proposes a Model Predictive Control (MPC) approach to ensure safe autonomous driving in uncertain environments. While MPC has been widely used in motion planning and control for autonomous driving applications, the standard literature cannot be directly applied to ensure safety (recursive feasibility) in the presence of other road users, i.e., pedestrians, cyclists, and other vehicles. To that end, this thesis shows how recursive feasibility can still be obtained through a slight modification of the MPC controller design.
The results of this thesis build upon the assumption that the behavior of the surrounding environment can be predicted to some extent, i.e., a future motion trajectory with some uncertainty bound can be propagated. Then, by postulating the existence of a safe set for the autonomous driving problem, and requiring that the motion prediction models have a consistent structure, safety guarantees can be derived for an MPC controller.
Finally, this thesis shows that the proposed MPC framework does not only hold in theory and simulations, but that it can also be deployed on a real vehicle test platform and operate in real-time, while still ensuring that the conditions needed for the derived safety guarantees hold.
model predictive control
robust constraint satisfaction
Chalmers, Elektroteknik, System- och reglerteknik
Real-Time Constrained Trajectory Planning and Vehicle Control for Proactive Autonomous Driving with Road Users
18th European Control Conference (ECC),; (2019)p. 256-262
Paper i proceeding
A Robust Scenario MPC Approach for Uncertain Multi-Modal Obstacles
IEEE CONTROL SYSTEMS LETTERS,; Vol. 5(2021)p. 947-952
Artikel i vetenskaplig tidskrift
A Computationally Efficient Model for Pedestrian Motion Prediction
; (2018)p. 374-375
Paper i proceeding
Batkovic, I, Ali, M, Falcone, P, and Zanon, M. Safe Trajectory Tracking in Uncertain Environments
Batkovic, I, Ali, M, Falcone, P, and Zanon, M. Model Predictive Control with Infeasible Reference Trajectories
Batkovic, I, Gupta, A, Falcone, P, and Zanon, M. Experimental Validation of Safe MPC for Autonomous Driving in Uncertain Environments
The results of this thesis show that an MPC-based decision logic can be a suitable choice for controlling the actions of self-driving vehicles. The results also show, that by considering the behavior of the surrounding environment, the MPC-based framework can adapt to the different traffic situations, and plan future decisions in time which will always remain safe. Finally, the thesis shows through real vehicle experiments that the derived theory is not only applicable to simulations, but also in practice.
COPPLAR CampusShuttle cooperative perception & planning platform
VINNOVA (2015-04849), 2016-01-01 -- 2018-12-31.
ReVeRe (Research Vehicle Resource)
Elektroteknik och elektronik
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5089
Opponent: Prof. Nathan van de Wouw, Dynamics and Control group, Eindhoven University of Technology, Netherlands