# Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison
## 3 Overview of Available Datasets
### Image Collections  
Table 1 enumerates various image datasets crucial for training deep neural models in autonomous driving. The Cityscapes dataset emerges as the most frequently cited \begin{table}
\begin{tabular}{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 METEOR & \begin{tabular}{c} Consists of 1000+ one-minute \\ video clips with annotated frames and bounding boxes for \\ surrounding vehicles and traffic \\ agents. \\ \end{tabular} & Cameras, LiDAR, IMU & 15,000 & 7 & 2D and 3D & 1 & [108] \\  & \begin{tabular}{c} VIRAT dataset for surveillance \\ containing primarily people and \\ vehicles, aligned with videos \\ from the ILSVRC dataset. \\ \end{tabular} & Cameras & 12,000 & 5 & 2D & 1 & [109] \\ RoadTextVQA & \begin{tabular}{c} Video question-answering dataset \\ for in-vehicle conversations. \\ Real-world datasets based on \\ NuScenes and Waymo for \\ temporal logic. \\ \end{tabular} & Cameras & 30,000 & 4 & 2D & 1 & [110] \\  & \begin{tabular}{c} Dataset for vehicle identification \\ with high-resolution videos, \\ including make, model, color, and \\ license plates. \\ \end{tabular} & Cameras & 50,000 & 3 & 2D & 1 & [111] \\  & \
\begin{tabular}{c} Contains images, stereo disparity, \\ and vehicle labels with ground \\ truth annotations. \\ \end{tabular} & Cameras, LiDAR, GPS & 100,000 & 4 & 2D and 3D & 0 & [113] \\  &
\begin{tabular}{c} Dataset for vehicle detection with \\ video sequences captured at \\ different times and varying traffic \\ conditions. \\ \end{tabular} & Cameras & 10,000 & 4 & 2D & 0 & [114] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Description** & **Cited** & **Ref.** \\ \hline CARLA & Simulator for urban driving with 12 semantic classes, bounding boxes, and vehicle measurements. & 1128 & [54] \\ AirSim & Simulator for drones, cars, and more, built on Unreal Engine, with support for SIL and HIL. & 248 & [115] \\ TORCS & Driving simulator capable of simulating elements of vehicular dynamics. & 91 & [55] \\ V2X-SIM & Synthetic collaborative perception dataset for autonomous driving, collected from both roadside and vehicles. & 17 & [116] \\ SUMMIT & Supports a wide range of applications, including perception, control, planning, and end-to-end learning. & 13 & [117] \\ CVRPTW & Instances of the Capacitated Vehicle Routing Problem with Time Windows for various customer nodes. & 7 & [118] \\ CARL & Control suite extended with physics context features for & 6 & [119] \\
3D VTSim & Dataset collected using driving simulation for accurate 3D bounding box annotations. & 4 & [120] \\ MUAD & Dataset with realistic synthetic images under diverse weather conditions, annotated for multiple tasks. & 3 & [121] \\ SDN & Navigation benchmark with trials and control streams developed to evaluate dialogue moves and physical navigation actions. & 2 & [122] \\ MULTIRTOROR-GYM & Multirotor gym environment for learning control policies for UAVs. & 2 & [123,124] \\ DEAP CITY & City pollution data, including daily pollutant and meteorological features, alongside total vehicle mileage. & 1 & [125] \\ EviLOG & Real-world lidar point clouds from a test vehicle with the same LiDAR setup as simulated LiDAR. & 1 & [126] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Available simulators that can be used to train deep neural models for implementation in autonomous vehicles. (3411 citations), offering extensive semantic, instance, and pixel-level annotations across diverse classes. Other notable datasets include Waymo (398 citations) and KITTI-360 (181 citations), which offer comprehensive sensor data and advanced 2D/3D annotations, respectively. Datasets such as IDD and INTERACTION provide insights into road scene comprehension and interactive traffic participant behavior.