Distributed Cooperative MPC for Autonomous Driving in Different Traffic Scenarios
A cooperative control approach for autonomous vehicles is developed in
order to perform different complex traffic maneuvers, e.g., double
lane-switching or intersection situations. The problem is formulated
as a distributed optimal control problem for a system of multiple
autonomous vehicles and then solved using a nonlinear Model Predictive
Control (MPC) technique, where the distributed approach is used to
make the problem computationally feasible in real-time. To provide
safety, a collision avoidance constraint is introduced, also in a
distributed way. In the proposed method, each vehicle computes its own
control inputs using estimated states of neighboring vehicles. In
addition, a compatibility constraint is defined that takes collision
avoidance into account but also ensures that each vehicle does not
deviate significantly from what is expected by neighboring
vehicles. The method allows us to construct a cost function for
several different traffic scenarios. The asymptotic convergence of the
system to the desired destination is proven, in the absence of
uncertainty and disturbances, for a sufficiently small MPC control
horizon. Simulation results show that the distributed algorithm scales
well with increasing number of vehicles.
Fatemeh Mohseni, Erik Frisk and Lars Nielsen
IEEE Transactions on Intelligent Vehicles,
2021
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Last updated: 2021-11-10
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