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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