Model predictive trajectory optimization and tracking for on-road autonomous vehicles
This addresses trajectory tracking for on-road autonomous vehicles, but it appears incremental as it builds on existing model predictive control methods.
The paper tackles motion planning for autonomous vehicles by proposing a control scheme that optimizes and tracks trajectories using a feedback-feedforward controller, with stability guarantees and good performance in simulated evasive maneuvers.
Motion planning for autonomous vehicles requires spatio-temporal motion plans (i.e. state trajectories) to account for dynamic obstacles. This requires a trajectory tracking control process which faithfully tracks planned trajectories. In this paper, a control scheme is presented which first optimizes a planned trajectory and then tracks the optimized trajectory using a feedback-feedforward controller. The feedforward element is calculated in a model predictive manner with a cost function focusing on driving performance. Stability of the error dynamic is then guaranteed by the design of the feedback-feedforward controller. The tracking performance of the control system is tested in a realistic simulated scenario where the control system must track an evasive lateral maneuver. The proposed controller performs well in simulation and can be easily adapted to different dynamic vehicle models. The uniqueness of the solution to the control synthesis eliminates any nondeterminism that could arise with switching between numerical solvers for the underlying mathematical program.