In this article, we study the optimal coordination of automated vehicles at intersections. The problem can be stated as an optimal control problem (OCP), which can be decomposed as a bi-level scheme composed by one nonlinear program (NLP) which schedules the access to the intersection and one OCP per vehicle which computes the appropriate vehicle commands. We discuss a practical implementation of the bi-level controller where the NLP is solved with a tailored semi-distributed sequential quadratic programming (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 beyond the accuracy admitted by the sensor system, 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 communication introduces delays in the outer control loop. 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 offers comparable performance using less computation and communication.

Experimental validation of a semi-distributed sequential quadratic programming method for optimal coordination of automated vehicles at intersections

Zanon M.;
2020

Abstract

In this article, we study the optimal coordination of automated vehicles at intersections. The problem can be stated as an optimal control problem (OCP), which can be decomposed as a bi-level scheme composed by one nonlinear program (NLP) which schedules the access to the intersection and one OCP per vehicle which computes the appropriate vehicle commands. We discuss a practical implementation of the bi-level controller where the NLP is solved with a tailored semi-distributed sequential quadratic programming (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 beyond the accuracy admitted by the sensor system, 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 communication introduces delays in the outer control loop. 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 offers comparable performance using less computation and communication.
automated vehicles
distributed model predictive control
distributed nonlinear programming
intersection coordination
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11771/15692
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