Reactive motion planning with probabilistic safety guarantees
- URL: http://arxiv.org/abs/2011.03590v2
- Date: Thu, 26 Nov 2020 00:07:12 GMT
- Title: Reactive motion planning with probabilistic safety guarantees
- Authors: Yuxiao Chen, Ugo Rosolia, Chuchu Fan, Aaron D. Ames, and Richard
Murray
- Abstract summary: This paper considers the problem of motion planning in environments with multiple uncontrolled agents.
A predictive model of the uncontrolled agents is trained to predict all possible trajectories within a short horizon based on the scenario.
The proposed approach is demonstrated in simulation in a scenario emulating autonomous highway driving.
- Score: 27.91467018272684
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Motion planning in environments with multiple agents is critical to many
important autonomous applications such as autonomous vehicles and assistive
robots. This paper considers the problem of motion planning, where the
controlled agent shares the environment with multiple uncontrolled agents.
First, a predictive model of the uncontrolled agents is trained to predict all
possible trajectories within a short horizon based on the scenario. The
prediction is then fed to a motion planning module based on model predictive
control. We proved generalization bound for the predictive model using three
different methods, post-bloating, support vector machine (SVM), and conformal
analysis, all capable of generating stochastic guarantees of the correctness of
the predictor. The proposed approach is demonstrated in simulation in a
scenario emulating autonomous highway driving.
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