Learning to Predict Ego-Vehicle Poses for Sampling-Based Nonholonomic Motion Planning

3 Dec 2018Holger BanzhafPaul SanzenbacherUlrich BaumannJ. Marius Zöllner

Sampling-based motion planning is an effective tool to compute safe trajectories for automated vehicles in complex environments. However, a fast convergence to the optimal solution can only be ensured with the use of problem-specific sampling distributions... (read more)

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