论文标题
使用端到端模仿学习的自动驾驶汽车的强大行为克隆
Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation Learning
论文作者
论文摘要
在这项工作中，我们提出了使用端到端模仿学习对人类驾驶员进行稳健行为克隆的轻巧管道。拟议的管道被用来训练和部署三种不同的驾驶行为模型到模拟车辆上。训练阶段包括数据收集，平衡，增强，预处理和训练神经网络，然后将训练有素的模型部署到自我车辆上，以根据板载摄像头的提要来预测转向命令。制定了一种新型的耦合控制定律，以根据预测的转向角度和其他参数（例如自我车辆的实际速度）以及速度和转向的规定约束来生成纵向控制命令。我们通过部署阶段的详尽实验分析了管道的计算效率，并评估了训练有素的模型的鲁棒性。我们还将我们的方法与最先进的实施方法进行了比较，以便对其有效性发表评论。
In this work, we present a lightweight pipeline for robust behavioral cloning of a human driver using end-to-end imitation learning. The proposed pipeline was employed to train and deploy three distinct driving behavior models onto a simulated vehicle. The training phase comprised of data collection, balancing, augmentation, preprocessing and training a neural network, following which, the trained model was deployed onto the ego vehicle to predict steering commands based on the feed from an onboard camera. A novel coupled control law was formulated to generate longitudinal control commands on-the-go based on the predicted steering angle and other parameters such as actual speed of the ego vehicle and the prescribed constraints for speed and steering. We analyzed computational efficiency of the pipeline and evaluated robustness of the trained models through exhaustive experimentation during the deployment phase. We also compared our approach against state-of-the-art implementation in order to comment on its validity.
