Abstract
One of the most difficult aspects of planning the motion of autonomous vehicles is the uncertainty regarding the future behavior of other road users. Ensuring a safe motion often requires preparing for multiple plausible outcomes of a current road situation. To achieve this, we propose an optimization-based trajectory generation method capable of planning an efficient motion while ensuring a collision-free behavior with respect to several conflicting hypotheses. The method generates multiple control trajectories that overlap for a certain time period, ensuring that the initial part of the trajectory can be continued with a safe motion regardless of which hypothesis will prove to be true. One notable application of the proposed method is an implementation of a Fail-Safe Planning concept, where the vehicle’s trajectory is planned based on a most plausible prediction of the other road users’ behaviors while simultaneously a fail-safe trajectory is generated based on a worst-case prediction, ensuring the existence of a collision-free maneuver that can be used in emergency situations.
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Turlej, W., Mitkowski, W. (2023). Multiple Hypothesis Planning for Vehicle Control. In: Pawelczyk, M., Bismor, D., Ogonowski, S., Kacprzyk, J. (eds) Advanced, Contemporary Control. PCC 2023. Lecture Notes in Networks and Systems, vol 709. Springer, Cham. https://doi.org/10.1007/978-3-031-35173-0_24
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