2022
DOI: 10.48550/arxiv.2203.06963
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Speeding up deep neural network-based planning of local car maneuvers via efficient B-spline path construction

Abstract: This paper demonstrates how an efficient representation of the planned path using B-splines, and a construction procedure that takes advantage of the neural network's inductive bias, speed up both the inference and training of a DNN-based motion planner. We build upon our recent work on learning local car maneuvers from past experience using a DNN architecture, introducing a novel B-spline path construction method, making it possible to generate local maneuvers in almost constant time of about 11 ms, respectin… Show more

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