Speeding up deep neural network-based planning of local car maneuvers via efficient B-spline path construction

• URL: http://arxiv.org/abs/2203.06963v1
• Date: Mon, 14 Mar 2022 09:54:24 GMT
• Title: Speeding up deep neural network-based planning of local car maneuvers via efficient B-spline path construction
• Authors: Piotr Kicki, Piotr Skrzypczy\'nski
• Abstract summary: We introduce a novel B-spline path construction method, making it possible to generate local maneuvers in almost constant time of about 11 ms. We evaluate thoroughly the new planner employing the recent Bench-MR framework to obtain quantitative results showing that our method outperforms state-of-the-art planners by a large margin.
• Score: 3.9596068699962323
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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, respecting a number of constraints imposed by the environment map and the kinematics of a car-like vehicle. We evaluate thoroughly the new planner employing the recent Bench-MR framework to obtain quantitative results showing that our method outperforms state-of-the-art planners by a large margin in the considered task.

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