End-to-end control of autonomous vehicles based on deep learning with visual attention

Zhenze Liu, Kuilin Wang, Jinliang Yu, Jingquan He
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Abstract

In this paper, we propose an end-to-end controller for self-driving vehicles based on visual attention. Attention strategy is used to weight the high-dimensional feature information extracted by convolutional neural networks (CNNs), and then the vehicle's velocity and steering wheel angle are predicted by different recurrent neural networks (RNNs). The end-to-end controller is trained on Comma.ai dataset and can effectively reduce the mean absolute error (MAE). The result shows that compared with other models, the end-to-end control model based on visual attention can achieve better control effects of vehicle's speed and steering wheel angle.
基于视觉注意深度学习的自动驾驶车辆端到端控制
在本文中,我们提出了一种基于视觉注意的自动驾驶车辆端到端控制器。采用注意策略对卷积神经网络(cnn)提取的高维特征信息进行加权,然后利用不同的递归神经网络(rnn)预测车辆的速度和方向盘角度。端到端控制器在逗号上进行训练。并且可以有效地降低平均绝对误差(MAE)。结果表明,与其他模型相比,基于视觉注意的端到端控制模型可以获得更好的车辆速度和方向盘角度控制效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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