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Abstract



Distributed Model Predictive Control for Highway Maneuvers


This paper describes a cooperative control method for autonomous vehicles, in order to perform different traffic maneuvers. The problem is formulated as an distributed optimal control problem for a nonlinear system consists of multiple autonomous vehicles with an identified model and then solved using nonlinear Model Predictive Control (MPC). The distributed approach has been used in order to make the problem computationally feasible to be solved in real-time. In this method, each vehicle computes its own control inputs using estimated states of neighboring vehicles. The constraints on the control inputs ensure the comfort of passengers. The method allows us to construct cost function for each maneuver scenario, in which, safety and performing the maneuver constitute two terms of the integrated cost of the finite horizon optimization problem. To provide safety, a potential function is introduced for collision avoidance. Simulation results show that the distributed algorithm scales well with increasing number of vehicles.

Fatemeh Mohseni, Erik Frisk, Jan Åslund and Lars Nielsen

2017

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