LAUSR

This letter presents automated driving using polygon clipping (A-D-PolyC), a novel framework for lane change behavior planning of automated vehicles on highways. It assumes that a mission planning layer generates lane change requests. In crowded traffic scenes, various variants for the lane change execution arise. The developed algorithm identifies the maneuver variants deterministically and in bounded runtime using polygon clipping in spatiotemporal domain. The variants are represented using a graph that captures the scene topology. Afterward, it efficiently samples optimal lateral and longitudinal trajectories with regard to vehicle dynamics. The satisfaction of hard constraints is checked. Finally, a scene prediction is conducted for a subset of the sampled trajectories. In comparison with most state of the art approaches, the framework accounts for traffic interaction and collects meaningful features. A novel measure based on generalized kinetic energies for the impact of a maneuver execution on the whole traffic scene is introduced. A-D-PolyC is a hierarchical, holistic, modular, and parallelizable concept that can be coupled with different scene prediction engines and lower-level local trajectory planners. The performance of A-D-PolyC is statistically evaluated using randomized simulation runs and compared to a state of the art approach.