Real-Time Trajectory Optimization under Motion Uncertainty using a GPU

This paper presents a sampling-based planning method considering motion uncertainty to generate more human-like driving paths for automated vehicles. Given information in the form of a small set of rules and driving heuristics the planning system optimizes trajectories in a seven dimensional state space. In a post-processing step a set of candidates is evaluated considering the uncertainty of the vehicles motion executing the given trajectory using a Linear-Quadratic Gaussian (LQG). This addresses the problem of indecisive planning behavior in case the optimal solution is unlikely to be followed precisely. The results of our experiments show that the mobile graphics processing unit (GPU) technology can be used as an enabler for real-time applications of computationally expensive planning approaches.