Abstract
Understanding road scenes for visual perception remains crucial for intelligent self-driving cars. In particular, it is desirable to detect unexpected small road hazards reliably in real-time, especially under varying adverse conditions (e.g., weather and daylight). However, existing road driving datasets provide large-scale images acquired in either normal or adverse scenarios only, and often do not contain the road obstacles captured in the same visual domain as for the other classes. To address this, we introduce a new dataset called AVOID, the Adverse Visual Conditions Dataset, for real-time obstacle detection collected in a simulated environment. AVOID consists of a large set of unexpected road obstacles located along each path captured under various weather and time conditions. Each image is coupled with the corresponding semantic and depth maps, raw and semantic LiDAR data, and waypoints, thereby supporting most visual perception tasks. We benchmark the results on high-performing real-time networks for the obstacle detection task, and also propose and conduct ablation studies using a comprehensive multi-task network for semantic segmentation, depth and waypoint prediction tasks.
Abstract (translated)
理解道路场景对于视觉感知来说，仍然是智能自动驾驶汽车的关键。特别是，在各种不利条件下（例如天气和光照）实时可靠地检测意外的小型路面障碍物非常必要。然而，现有的道路驾驶数据集仅提供了正常或恶劣条件下的大规模图像，并且通常不包含与其它类别的同一视觉域内的道路障碍物。为解决这一问题，我们引入了一个新的数据集——AVOID（不利视觉条件数据集），该数据集是在模拟环境中收集的实时障碍物检测数据。AVOID 包含在各种天气和时间条件下捕捉到的大规模意外道路障碍物图像集合。每张图片都配有相应的语义图、深度图、原始和语义激光雷达数据及路径点，从而支持大多数视觉感知任务。我们针对障碍物检测任务对高性能的实时网络进行了基准测试，并提出并开展了使用综合多任务网络进行语义分割、深度预测和路径点预测任务的消融研究。
URL
https://arxiv.org/abs/2512.23215
