An Ad-hoc Sampling-based Planner for On-road Automated Driving
Christian Lienke, Martin Keller, Karl-Heinz Glander, Torsten Bertram · 2018
The paper at hand proposes a real-time capable approach to trajectory planning. An online sampling strategy is chosen, exploiting the structure of the surrounding environment. Lateral states are sampled from state space, whereas longitudinal states are generated via sampling from the action space. The combination yields breakpoints, which are then used to generate a candidate trajectory via spline interpolation. A bi-level candidate evaluation strategy is presented assessing comfort and human-like driving as well as a post-check of collision avoidance with accurate geometric modeling. The result is a reactive feedback motion planner, which shows promising results with respect to on-road automated driving.