Available pavement condition datasets.

Ensuring the safety and comfort of autonomous driving relies heavily on accurately perceiving the quality of the road pavement surface. However, current research has primarily focused on perceiving traffic participants such as surrounding vehicles and pedestrians, with relatively limited investigation into road surface quality perception. This paper addresses this gap by proposing a high-performance semantic segmentation method that utilizes real-time road images captured by an onboard camera to monitor the category and position of road defects ahead of the ego vehicle. Our approach introduces a novel multi-scale spatial attention module to enhance the accuracy of detecting road surface damage within the traditional semantic segmentation framework. To evaluate the proposed approach, we curated and utilized a dataset comprising 2,400 annotated images for model training and validating. Experimental results demonstrate that our method achieves a superior balance between detection precision and computational efficiency, outperforming existing semantic segmentation models in terms of mean IoU while maintaining low computational cost and high inference speed. This approach holds great potential for application in vision-based autonomous driving as it can be seamlessly integrated with appropriate control strategies, thereby offering passengers a smooth and reliable driving experience.保障自动驾驶的行驶安全与乘坐舒适性，高度依赖对道路路面表面质量的精准感知。然而现有研究主要聚焦于对周边车辆、行人等交通参与者的感知，针对路面质量感知的探索相对不足。本文针对这一研究空白，提出了一种高性能语义分割（semantic segmentation）方法：该方法依托车载摄像头采集的实时道路图像，对自车（ego vehicle）前方的道路缺陷类别与位置进行监测。我们的方法在传统语义分割框架中引入了一种新颖的多尺度空间注意力模块，以提升路面破损检测的精度。为评估所提方法，我们构建并使用了一个包含2400张标注图像的数据集，用于模型的训练与验证。实验结果表明，我们的方法在检测精度与计算效率之间实现了优异的平衡：在平均交并比（mean IoU）指标上优于现有语义分割模型，同时保持了较低的计算开销与较高的推理速度。该方法可与适配的控制策略无缝集成，在基于视觉的自动驾驶场景中具备极高的应用潜力，能够为乘客带来平稳可靠的驾乘体验。