用于比例自动驾驶车辆控制的零样本策略可迁移性
Zero-Shot Policy Transferability for the Control of a Scale Autonomous Vehicle
AI总结：
本文研究比例自动驾驶车辆的零样本策略迁移，提出仅在圆形轨迹上训练的前馈神经网络控制器，并利用内部仿真平台结合人类驾驶员和MPC训练，验证了在航点轨迹跟随和变速场景下的有效性。
摘要：
我们报告了一项研究，该研究采用内部开发的仿真基础设施，为与比例自动驾驶车辆相关的控制策略实现零样本策略可迁移性。我们专注于实现无需真实世界数据即可训练的策略（零样本迁移），并且这些策略由内部开发，而非由先前工作验证。我们通过实现一个仅在圆形参考轨迹族上训练的神经网络（NN）控制器来完成这一目标。所使用的传感器为RTK-GPS和IMU，后者用于提供航向。该神经网络控制器使用人类驾驶员（通过人在环仿真）或模型预测控制（MPC）策略进行训练。我们结合两种运行场景展示了这两种方法：车辆以恒定速度跟随由航点定义的轨迹；以及车辆跟随沿其航点定义轨迹变化的速度曲线。本工作的主要贡献是展示了零样本迁移与一种新颖的前馈神经网络控制器的结合，该控制器使用一个通用的、内部开发的仿真平台进行训练。
英文摘要：
We report on a study that employs an in-house developed simulation infrastructure to accomplish zero shot policy transferability for a control policy associated with a scale autonomous vehicle. We focus on implementing policies that require no real world data to be trained (Zero-Shot Transfer), and are developed in-house as opposed to being validated by previous works. We do this by implementing a Neural Network (NN) controller that is trained only on a family of circular reference trajectories. The sensors used are RTK-GPS and IMU, the latter for providing heading. The NN controller is trained using either a human driver (via human in the loop simulation), or a Model Predictive Control (MPC) strategy. We demonstrate these two approaches in conjunction with two operation scenarios: the vehicle follows a waypoint-defined trajectory at constant speed; and the vehicle follows a speed profile that changes along the vehicle's waypoint-defined trajectory. The primary contribution of this work is the demonstration of Zero-Shot Transfer in conjunction with a novel feed-forward NN controller trained using a general purpose, in-house developed simulation platform.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
