# Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison
## 3 Overview of Available Datasets
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\begin{tabular}{c c c c c c c c} \hline \hline
**Name** & **Description** & **Sensors Used** & **Samples** & **Classes** & **2D/3D** & **Cited** & **Ref.** \\ \hline SODA-D & High-quality images under driving scenarios for small object detection. Popular KITTI dataset with comprehensive semantic/instance labels in 2D and 3D. Road scene understanding in unstructured environments dataset. & Camera & 24,828 & 9 & 2D & 232 & [67] \\ \multirow{2}{*}{IDD} & High-quality images under driving scenarios for small object detection. Popular KITTI dataset with comprehensive semantic/instance labels in 2D and 3D. Road scene understanding in unstructured environments dataset. & Camera & 10,000+ & 19 & 2D\&3D & 181 & [52] \\  & High-quality images under driving scenarios for small object detection. Popular KITTI dataset with comprehensive semantic/instance labels in 2D and 3D. Road scene understanding in unstructured environments dataset. & Camera & 10,000+ & 34 & 2D\&3D & 90 & [68] \\ INTERACTION & Participants in highly interactive scenarios. Three-dimensional semantic segmentation dataset collected in Peking University. Extensive fisheye camera automotive dataset with nine tasks and 40 class annotations. Lost and rough pixel-wise annotations of obstacles and free space. DrivingStereo & Over 180k images for stereo vision, larger than KITTI Stereo dataset. Evaluates pixel-wise uncertainty estimates towards detecting anomalous objects. & Fisheye camera & 100,000+ & 40 & 2D\&3D & 49 & [71] \\ \multirow{2}{*}{Fishysscapes} & Lost-cargo image sequence dataset with pixel-wise annotations of obstacles and free space. & Stereo camera & 2104 & 14 & 2D & 47 & [72] \\  & Over 180k images for stereo vision, larger than KITTI Stereo dataset. Evaluates pixel-wise uncertainty estimates towards detecting anomalous objects. & Stereo camera & 182,000 & 10 & 3D & 42 & [73] \\ \multirow{2}{*}{ROAD Anomaly} & Contains images of unusual dangers encountered by vehicles, such as animals and traffic cones. & Camera & 30,000 & n/a & 2D & 44 & [74] \\  & Dataset captured with high-precision autonomous vehicle sensor kit. & Camera LiDAR & 8000 & 32 & 3D & 39 & [76] \\ KITTI Road & Road and lane estimation benchmark. & Stereo camera, LiDAR & 289 & 3 & 2D & 38 & [77] \\ \multirow{2}{*}{MVSEC} & Data collection designed for developing 3D perception algorithms for event-based cameras. Raw sensor data for vehicle navigation with development tools in the ROS environment. & Event-based camera and LiDAR & 4 & n/a & 3D & 26 & [78] \\  & & & & & & \\ Cityscapes 3D & Extends the original Cityscapes dataset with 3D bounding box annotations for vehicles. & Stereo camera & 5000 & 25 & 2D\&3D & 19 & [79] \\ \multirow{2}{*}{RaiSem19} & Dataset for semantic rail scene understanding with images from rail vehicles. & Stereo camera & 5000 & 30 & 3D & 10 & [65] \\  & Dataset for semantic rail scene & & & & & \\ RailSem19 & Understanding with images from rail vehicles. & Camera & 850 & 19 & 2D & 8 & [80] \\ \multirow{2}{*}{RoadAnomaly21} & Contains images with at least one anomalous object such as animals or unknown vehicles. & Camera & 1000 & n/a & 2D & 8 & [81] \\  & Annotations of pedestrians, cyclists, and riders in urban traffic scenes from 31 cities in Europe. & Camera & 47,300 & 2 & 2D & 6 & [82] \\  & Dataset for testing & & & & & \\ DOLPHINS vehicle-to-everything (V2X) network in autonomous driving. & Camera, LiDAR & 42,376 & 2 & 2D\&3D & 5 & [83] \\  & Dataset capturing dynamic intent & & & & & \\ PSI changes for pedestrians crossing in front of ego vehicles. & Camera & 1000 & 3 & 2D\&3D & 5 & [84,85] \\ \multirow{2}{*}{TICaM} & Dataset for vehicle interior monitoring using a wide-angle depth camera. & Camera & 1000 & 5 & 2D & 5 & [86] \\  & Dataset collected over 2 years across 14 European countries with full sensor suite. & Camera, GNSS/IMU, LiDAR & 100,000 & 5 & 2D\&3D & 45 & [87] \\ \multirow{2}{*}{OoDIS} & Dataset for anomaly instance segmentation in autonomous driving. & Camera & 4218 & 8 & 2D & 4 & [88] \\  & Real-world scenarios for autonomous vehicles focusing on pedestrian interactions. & Camera & 16,000 & 18 & 2D & 3 & [89] \\ \hline \hline \end{tabular}
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