论文标题
通过有效的B-Spline Path构造加速当地汽车操纵的深度神经网络计划
Speeding up deep neural network-based planning of local car maneuvers via efficient B-spline path construction
论文作者
论文摘要
本文展示了如何使用B型平面图对计划路径的有效表示，以及利用神经网络的电感偏差的施工程序，加快了基于DNN的运动计划者的推理和培训。我们基于我们最近使用DNN架构从过去的经验中学习本地汽车操作的工作，引入了一种新颖的B型路径构建方法，这使得在几乎恒定的时间内生成本地操纵，这几乎持续约11毫秒，尊重环境图和汽车型汽车的动力学施加的许多约束。我们彻底评估了采用最近的台式MR框架的新计划者，以获得定量结果，以表明我们的方法在经过的任务中的大幅度优于最先进的计划者。
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.
