Zhu Wenjie, Li Hongwei, Cheng Xianglong, Jiang Yirui
School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China.
School of Geo-Science & Technology, Zhengzhou University, Zhengzhou 450052, China.
Sensors (Basel). 2023 Sep 30;23(19):8182. doi: 10.3390/s23198182.
To cope with the challenges of autonomous driving in complex road environments, the need for collaborative multi-tasking has been proposed. This research direction explores new solutions at the application level and has become a hot topic of great interest. In the field of natural language processing and recommendation algorithms, the use of multi-task learning networks has been proven to reduce time, computing power, and storage usage in various task coupling cases. Due to the characteristics of the multi-task learning network, it has also been applied to visual road feature extraction in recent years. This article proposes a multi-task road feature extraction network that combines group convolution with transformer and squeeze excitation attention mechanisms. The network can simultaneously perform drivable area segmentation, lane line segmentation, and traffic object detection tasks. The experimental results of the BDD-100K dataset show that the proposed method performs well for different tasks and has a higher accuracy than similar algorithms. The proposed method provides new ideas and methods for the autonomous road perception of vehicles and the generation of highly accurate maps in visual-based autonomous driving processes.
为应对复杂道路环境下自动驾驶的挑战，人们提出了协作多任务处理的需求。这一研究方向在应用层面探索新的解决方案，已成为备受关注的热门话题。在自然语言处理和推荐算法领域，多任务学习网络的使用已被证明在各种任务耦合情况下能够减少时间、计算能力和存储使用。由于多任务学习网络的特性，近年来它也被应用于视觉道路特征提取。本文提出了一种将分组卷积与Transformer和挤压激励注意力机制相结合的多任务道路特征提取网络。该网络可以同时执行可行驶区域分割、车道线分割和交通目标检测任务。BDD - 100K数据集的实验结果表明，所提方法在不同任务上表现良好，且比类似算法具有更高的准确率。所提方法为基于视觉的自动驾驶过程中车辆的自主道路感知和高精度地图的生成提供了新的思路和方法。
