Single-Shot and Long-Term Collision Risk for Safer Driving

Navigation in congested environments is a challenge for autonomous vehicles. In order to drive in a safer way, the potential collision risk with other obstacles should be considered in the planning algorithm. We focus on assessing realistic collision risk with other traffic participants, to finally enable a more human-style and safer driving behavior. In the demonstration videos, the ego vehicle always try to drive to the speed limit and follow the centerline of the road, but should also pay attention to the static and dynamic obstacles that result in certain collision risk to the ego vehicle. In order to reduce the collision risk, the ego vehicle reduces the speed or move laterally slightly to avoid passing the obstacles with low distance and high relative speed. After that, it should retrieve its origin lateral position and velocity. Future work will be done in learning how human drivers balance risk, utility and comfort from the real data. By applying machine learning techniques on recorded human trajectories, a realistic risk balanced driving behavior will be achieved.

Contact: M.Sc. Marlon Steiner