基于深度残差学习的自动驾驶道路场景理解

随着道路场景理解技术的快速发展，自主驾驶领域取得了长足的进步。在相关任务中，包括道路分割、分类和车辆检测的实时性和准确性是安全性的一个关键问题。为此，提出了一个具有编/解码器网络结构的基于深度残差学习的方法。一方面，编码器网络结构使用不同层次的残差网络来提取高维中的抽象特征，这些特征在接下来的三个任务中共享使用；另一方面，解码器网络结构采用一种子任务的并行计算机制，即道路分割、车辆检测和道路分类任务同时执行。此外，全卷积神经网络用于对提取的图像特征进行上采样以解决道路分割问题。最终，实验结果表明在保证高精度的前提下处理帧率可达到15 fps以上。

Road scene understanding for autonomous driving via deep residual learning

It is making great progress in the autonomous driving field with the rapid development of road scene understanding techniques. The safety is a concerning issue with respect to the real-time and accurate performance in the related tasks which contains the road segmentation, road classification and vehicle detection. To this end, this paper proposed an approach based on deep residual learning with an encoder-decoder network structure. On the one hand, the encoder network structure used different layers of residual networks to extract the abstract features in the high dimension, which shared in the next three tasks. On the other hand, the decoder network structure adopted a mechanism of parallel computing for sub-tasks, i. e., executed the road segmentation, vehicle detection and road classification tasks simultaneously. Additionally, it used the fully convolutional networks to upsample the extracted features to specifically solve the problem of road segmentation. At last, the experimental results show that the processing rate can effectively reach more than 15 fps with the high accuracy guaranteed.