论文标题
一种自制的学习方法，用于在城市环境中操纵类似汽车的车辆的快速路径计划
A Self-Supervised Learning Approach to Rapid Path Planning for Car-Like Vehicles Maneuvering in Urban Environment
论文作者
论文摘要
一个有效的自动驾驶汽车式车辆的路径策划者应处理强大的运动学限制，尤其是在城市交通进行操作时通常遇到的狭窄空间中，并且由于城市交通状况高度动态而进行快速计划。最先进的计划算法以高计算成本处理了此类困难案例，通常会产生非确定性的结果。但是，可以快速生成可行的本地路径，利用在相同或相似环境中获得的过去的计划经验。尽管通过监督培训学习对于实际交通情况是有问题的，但我们在本文中介绍了一种基于神经网络的新方法，用于路径计划，该方法采用了一种基于梯度的自我监督学习算法来预测可行的路径。这种方法强烈利用了过去获得的经验，并迅速产生了可行的操纵计划，该计划的类似汽车的车辆具有有限的转向角度。这种方法的有效性已通过计算实验证实。
An efficient path planner for autonomous car-like vehicles should handle the strong kinematic constraints, particularly in confined spaces commonly encountered while maneuvering in city traffic, and should enable rapid planning, as the city traffic scenarios are highly dynamic. State-of-the-art planning algorithms handle such difficult cases at high computational cost, often yielding non-deterministic results. However, feasible local paths can be quickly generated leveraging the past planning experience gained in the same or similar environment. While learning through supervised training is problematic for real traffic scenarios, we introduce in this paper a novel neural network-based method for path planning, which employs a gradient-based self-supervised learning algorithm to predict feasible paths. This approach strongly exploits the experience gained in the past and rapidly yields feasible maneuver plans for car-like vehicles with limited steering-angle. The effectiveness of such an approach has been confirmed by computational experiments.
