构建了用于恶劣天气下障碍物检测的仿真数据集AVOID
AVOID: The Adverse Visual Conditions Dataset with Obstacles for Driving Scene Understanding
- 在模拟环境中采集多种天气光照下的道路障碍图像
- 包含语义、深度图、激光雷达及路径点等多模态标注
- 适合自动驾驶感知算法测试与多任务学习研究
视觉感知对智能自动驾驶至关重要，尤其需要在不同恶劣条件（如天气和光照）下实时可靠地检测意外小型道路障碍物。然而现有驾驶数据集通常仅覆盖正常或恶劣场景之一，且缺乏与其它类别同视觉域的障碍物数据。为此，我们提出新数据集AVOID（Adverse Visual Conditions Dataset），在仿真环境中收集大量道路障碍物图像，涵盖不同天气与时段条件。每张图像均配有语义图、深度图、原始及语义激光雷达数据、以及航路点，支持多数视觉感知任务。我们在高性能实时网络上对该数据集进行障碍物检测基准测试，并提出一个综合性多任务网络，开展语义分割、深度估计与航路点预测的消融实验。
原文摘要 · Abstract (English)
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.
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