Maneuver Identification for Interaction-Aware Highway Lane Change Behavior Planning based on Polygon Clipping and Convex Optimization

This contribution presents A-D-PolyC
^QP
(Automated Driving using Polygon Clipping and Quadratic Programming), a framework for lane change behavior planning of automated vehicles on highways. It solves the tactical decision problem that arises through the presence of more than one lane change maneuver variant in traffic scenes. A-D-PolyC
^QP
derives all variants deterministically using polygon clipping in spatiotemporal domain and is able to deal with lane changes of surrounding traffic participants. All variants are encoded in a directed graph. The nodes in the graph correspond to spatiotemporal free space polygons and allow the derivation of constraints for trajectory optimization in a Frenet-Serret coordinate frame. Novel, linear time-variant Time-To-Collision and Time Gap constraints based on geometric boundaries are introduced. Interaction-awareness is incorporated by forward simulation of the optimized trajectories. Finally each maneuver is assessed and features are calculated that allow the decision for one lane change maneuver by a higher-level function.