# Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison
## 3 Overview of Available Datasets

Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Name** & **Description** & **Sensors Used** & \
\begin{tabular}{c} **Samples** \\ **Number** \\ \end{tabular} &
\begin{tabular}{c} **Classes** \\ **Number** \\ \end{tabular} & **2D/3D** & **Cited** & **Ref.** \\ \hline \multirow{4}{*}{nuScenes} & Full autonomous vehicle data & 6 cameras, & & & & & \\  & suite: 32-beam LiDAR, 6 cameras, & 1 LIDAR, & & & & & \\  & and radars with complete 360\({}^{\circ}\) & 5 RADAR, GPS, & 1.4 M & 32 & 2D and 3D & 1695 & [90] \\  & & coverage. & & & & & \\  & Photo-realistic synthetic video & 4 cameras, & & & & & \\ Virtual KITTI & dataset for several video & LIDAR, RADAR, & GPS, IMU & 21,200 & 7 & 2D and 3D & 124 & [91] \\  & understanding tasks. & Cross-disciplinary dataset for & & & & & \\  & & grounding natural language into & Cameras, LiDAR & 80,000 & 5 & 2D and 3D & 105 & [92] \\  & visual space. & & & & & \\  & Lane detection dataset collected & & & & & & \\  & by cameras mounted on six & Cameras & 133,000 & 6 & 2D & 77 & [93] \\  & different vehicles in Beijing, & & & & & \\  & Large dataset with over 140,000 & & & & & \\ ApolloScape & video frames from various & Cameras, LiDAR & 140,000 & 18 & 2D and 3D & 68 & [94] \\  & locations in China. & & & & & \\  & Tests an autonomous vehicle’s & & & & & \\ ROAD & ability to detect road events using & & & & & \\  & annotated videos. & & & & & \\  & Data collected by two vehicles & & & & & \\ V2V4 Real & equipped with multi-modal & & & & & \\  & sensors driving together through & & & & & \\  & diverse scenarios. & & & & & \\  & 700 labeled video-clips with & & & & & \\ TITAN & odometry captured from a & & & & & \\  & moving vehicle in Tokyo. & & & & & \\  & Vehicles annotated with precise & & & & & \\ BrnoCompSpeed & speed measurements from LiDAR & & & & & \\  & and GPS tracks. & & & & & \\  & Real traffic accident videos & & & & & \\ CCD & captured by dashcams with & & & & & \\  & diverse annotations. & & & & & \\  & Large-scale 5D semantics dataset & & & & & \\ BLVD & collected in China’s Intelligent & & & & & \\  & Vehicle Proving Center. & & & & & \\  & Dataset includes video clips of & & & & & \\ HEV-I & real human driving in different & & & & & \\  & intersections in the San Francisco & & & & & \\  & Bay Area. & & & & & \\  & Dataset collected by an & & & & & \\ Ford CVAL & autonomous ground vehicle & & & & & \\  & testbed equipped with multiple & & & & & \\  & sensors, collected in Michigan. & & & & & \\  & Over 1000 scenarios captured by & & & & & \\  & autonomous vehicles, each log & & & & & \\ TbV Dataset & represents a continuous & & & & & \\  & observation of a scene around a & & & & \\  & self-driving vehicle. & & & & & \\ Argoverse 2 Map & & & & & & \\ Change & map changes within 30 m of an & & & & \\  & autonomous vehicle. & & & & & \\  & Large-scale collection of dashcam & & & & & \\ D2 CITY & videos collected by vehicles on & & & & \\  & DiD’s platform. & & & & & \\  & Sequences acquired by agents & & & & \\ DADE & (ego vehicles) within a 5 h time & & & & & \\  & frame, totaling 990k frames. & & & & & \\  & Test set contains images and & & & & & \\ DMPD & pedestrian labels captured from a & & & & \\  & vehicle during a 27 min drive. & & & & & \\  & Dataset consisting of 104 videos & & & & & \\ INDRA & with annotated road crossing & & & & & \\  & safety labels and vehicle & & & & \\  & bounding boxes. & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Available video datasets that can be used to train deep neural models for implementation in autonomous vehicles.