Kinematic real-time trajectory planning with state and input constraints for the example of highly automated driving
Steffen Joos, Roktim Bruder, Thomas Specker, Matthias Bitzer, Knut Graichen · 2019
The real-time generation of kinematic trajectories w.r.t. state and input constraints is discussed for the stabilization of the lateral, longitudinal and vertical motion of objects such as (automated) vehicles in the Frenet frame. The goal is to exploit the property of differential flatness in order to achieve optimal trajectories as well as a flexible and selectable prioritization-based constraints satisfaction. This aim is subject to the absence of an online optimization and a low complexity of the planner's architecture. With regard to practical application, a discrete-time implementation of the planner is presented and benchmarked with two modern MPC approaches in terms of optimality and CPU-time consumption by means of simulation studies.