Experimental Validation of a Semi-Distributed SQP Method for Optimal Coordination of Automated Vehicles at Intersections
In this paper, we study the optimal coordination of automated vehicles at intersections. The problem can be stated as an optimal control problem, that can be decomposed into one NLP which schedules access to the intersection and one opti- mal control problem per vehicle. The decomposition enables a bi-level MPC, where an outer control loop schedules access to the intersection, and inner control loops compute the appropriate vehicle commands. We discuss a practical implementation of the bi-level controller where the NLP is solved with a tailored SQP algorithm that enables distribution of most computation to the vehicles. Results from an extensive experimental campaign are presented, where the bi-level controller and the semi-distributed SQP are implemented on a test setup consisting of three automated vehicles. In particular, we show that the vehicle-level controller can enforce the scheduled intersection access within a relevant accuracy, and that the bi-level controller can handle large perturbations and large communication delays, which makes the scheme applicable in practical scenarios. Finally, the use of wireless communi- cation introduce delays in the outer control loop and to allow faster feedback, we introduce a Real Time Iteration (RTI) like variation of the bi-level controller. Experimental and simulated results indicate that the RTI-like variation offer comparable performance using less computation and communication.
Distributed Nonlinear programming
Distributed Model Predictive Control