Autonomous lane keeping is an important safety feature for intelligent vehicles. This paper presents a lane keeping simulator that is built with image projections of recorded data in conjunction with vehicle dynamics estimation. An end-to-end learning method using convolutional neural network (CNN) takes front-view camera data as input and produces the proper steering wheel angle to keep the vehicle in lane. A novel method of data augmentation is proposed using vehicle dynamic model and vehicle trajectory tracking, which can create additional training data as if a vehicle drives off-lane in any displacement and orientation. Experimental results demonstrate that the CNN model trained with the simulator can achieve higher accuracy for autonomous lane keeping and much lower failure rate. The simulator can serve as a platform for both training and evaluation of vision based autonomous driving algorithms. The experimental dataset is made available at http://computing.wpi.edu/dataset.html.

中文翻译：

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使用真实世界数据和端到端学习构建自主车道保持模拟器

自动车道保持是智能汽车的一项重要安全功能。本文提出了一种车道保持模拟器，该模拟器由记录数据的图像投影结合车辆动力学估计构建而成。使用卷积神经网络 (CNN) 的端到端学习方法将前视摄像头数据作为输入，并产生合适的方向盘角度以保持车辆在车道上。使用车辆动力学模型和车辆轨迹跟踪提出了一种新的数据增强方法，该方法可以创建额外的训练数据，就像车辆在任何位移和方向上偏离车道一样。实验结果表明，使用模拟器训练的 CNN 模型可以实现更高的自主车道保持精度和更低的故障率。该模拟器可以作为基于视觉的自动驾驶算法的训练和评估平台。实验数据集可从 http://computing.wpi.edu/dataset.html 获得。