AVOID: The Adverse Visual Conditions Dataset with Obstacles for Driving Scene Understanding
This dataset addresses the need for reliable obstacle detection in self-driving cars under adverse visual conditions, though it is incremental as it builds on existing simulation-based approaches.
The authors tackled the problem of detecting small road hazards in adverse conditions by introducing the AVOID dataset, which includes obstacles under various weather and time scenarios, and benchmarked it with real-time networks, achieving results that support multiple visual perception tasks.
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.