Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation Learning
Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation Learning
复制标题
使用端到端模仿学习的自动驾驶汽车的鲁棒行为克隆
DOI：
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发表时间：
2020
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通讯作者：
Sivanathan Kandhasamy
中科院分区：
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作者：
Tanmay Vilas Samak;Chinmay Vilas Samak;Sivanathan Kandhasamy
在这项工作中，我们提出了一个强大的管道克隆驾驶行为的人使用端到端的模仿学习。所提出的管道被用来训练和部署三个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，然后将训练好的模型部署到自我车辆上，根据车载摄像头的反馈预测转向命令。提出了一种新的耦合控制律，根据预测的转向角和其他参数（如本车实际速度和速度及转向的规定约束），在行驶过程中产生纵向控制指令。我们分析了流水线的计算效率，并通过详尽的实验评估了训练模型的鲁棒性。即使是相对较浅的卷积神经网络模型也能够从稀疏标记的数据集中学习关键驾驶行为，并且在部署所述驾驶行为期间能够容忍环境变化。
In this work, we present a robust pipeline for cloning driving behavior of a human 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. Even a relatively shallow convolutional neural network model was able to learn key driving behaviors from sparsely labelled datasets and was tolerant to environmental variations during deployment of the said driving behaviors.