The contribution at hand combines a sampling-based trajectory planning approach and a model predictive trajectory tracking controller to a collision avoidance system. The planner generates candidate trajectories by the suitable selection of breakpoints which are connected by a spline interpolation. A procedure is presented to systematically select sample states to perform a collision avoidance maneuver in case of an emergency situation. The vehicle is controlled to the optimal trajectory of the planner by comparison with a model predictive trajectory set. This is determined by the prediction of a detailed nonlinear vehicle model for constant input variables. A suitable objective function with a time weighting is utilized to evaluate the individual trajectories and to select the optimal input variables.
Sampling-based Trajectory Planning and Control for a Collision Avoidance System
2019-10-01
436603 byte
Conference paper
Electronic Resource
English
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