Cooperative path-planning and tracking controller evaluation using vehicle models of varying complexities

Abstract

This paper discusses cooperative path-planning and tracking controller for autonomous vehicles using a distributed model predictive control approach. Mixed-integer quadratic programming approach is used for optimal trajectory generation using a linear model predictive control for path-tracking. Cooperative behaviour is introduced by broadcasting the planned trajectories of two connected automated vehicles. The controller generates steering and torque inputs. The steering and drive motor actuator constraints are incorporated in the control law. Computational simulations are performed to evaluate the controller for vehicle models of varying complexities. A 12-degrees-of-freedom vehicle model is developed and is subsequently linearised to be used as the plant model for the linearised model predictive control-based tracking controller. The model behaviour is compared against the kinematic, bicycle and the sophisticated high-fidelity multi-body dynamics CarSim model of the vehicle. Vehicle trajectories used for tracking are longitudinal and lateral positions, velocities and yaw rate. A cooperative obstacle avoidance manoeuvre is performed at different speeds using a co-simulation between the controller model in Simulink and the high-fidelity vehicle model in CarSim. The simulation results demonstrate the effectiveness of the proposed method.