Zhao Tong, Wei Yintao
State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China.
Data Brief. 2022 Jul 23;43:108483. doi: 10.1016/j.dib.2022.108483. eCollection 2022 Aug.
The preview of the road surface states is essential for improving the safety and the ride comfort of autonomous vehicles. The created dataset in this data article consists of 370151 road surface images captured under a wide range of road and weather conditions in China. The original pictures are acquired with a vehicle-mounted camera and then the patches containing only the road surface area are cropped. The friction level, material, and unevenness properties of each road image are annotated in detail. This large-scale dataset is useful for developing vision-based road sensing modules to improve the performance of the driving assistance systems. Also, deep-learning experts can regard this dataset as a comparing benchmark for their algorithms. The dataset is available at [1].
路面状态的预对于提高自动驾驶车辆的安全性和乘坐舒适性至关重要。本数据文章中创建的数据集包含在中国广泛的道路和天气条件下拍摄的370151张路面图像。原始图片由车载摄像头获取，然后裁剪出仅包含路面区域的图像块。对每个道路图像的摩擦系数、材料和不平度特性进行了详细标注。这个大规模数据集对于开发基于视觉的道路传感模块以提高驾驶辅助系统的性能很有用。此外，深度学习专家可以将此数据集作为其算法的比较基准。该数据集可在[1]获取。
