# Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison
## 3 Overview of Available Datasets
### Video Stocks  
Table 2 lists various video datasets used for training deep neural models in autonomous driving, highlighting the key datasets and their applications. The nuScenes dataset stands out as the most widely cited (1695 citations) with its comprehensive suite of 32-beam LiDAR, six cameras, and radars providing full 360\({}^{\circ}\) coverage, crucial for holistic vehicle perception. The Talk2 Car dataset is based on the nuScenes dataset. It is the first dataset designed for object referencing with natural language commands for autonomous vehicles. Talk2 Car enables the study of how autonomous cars can understand and execute passenger commands in urban settings. Other significant datasets include Virtual KITTI, with its photo-realistic synthetic videos, and CULane, which focuses on lane detection through camera footage collected in Beijing. The ApolloScape dataset, notable for its extensive collection of over 140,000 video frames, captures diverse locations across China. For specific driving scenarios, ROAD evaluates the ability of autonomous vehicles to recognize road events through annotated videos. Datasets like V2V4 Real encompass multi-modal sensor data from two vehicles driving together. TITAN and BrnoCompSpeed include labeled video clips with odometry and speed measurements, respectively. Additionally, CCD features real traffic accident videos from dashcams, and BLVD offers a large-scale semantic dataset from China's Intelligent Vehicle Proving Center. The HEV-I dataset includes real human driving clips at San Francisco intersections, while Ford CVaL provides multi-sensor data from Michigan. More niche datasets, such as the TbV Dataset, with over 1000 autonomous vehicle scenarios, and Argoverse 2 Map Change, indicating temporal map changes, serve specialized purposes. Datasets like D2 CITY, DADE, and DMPD each have minimal citations, but contribute with extensive dashcam videos and pedestrian labels. Emerging and less-cited datasets include INDRA for road-crossing safety, METEOR with annotated traffic clips, METU-VIREF for surveillance, and RoadTextVQA for in-vehicle conversation question answering. TLV, Vehicle-Rear, and IMO offer enriched vehicle identification and traffic data, albeit with very few citations. Overall, these video datasets collectively support the development of robust autonomous driving models, catering to various aspects such as object detection, lane marking, event recognition, and real-world driving behavior. The expanded dataset table highlights the varying strengths and weaknesses pertinent to autonomous driving research. Notably, nuScenes excels with its comprehensive 360\({}^{\circ}\) sensor suite, featuring multiple sensors (cameras, LiDAR, RADAR, GPS, and IMU) and a substantial volume of samples (1.4 M), making it a powerful resource for multi-modal perception studies, evidenced by its high citation count. On the contrary, datasets such as V2V4 Real and Ford CVaL, although equipped with multiple sensors, are limited by smaller sample sizes, constraining any extensive testing capabilities. Virtual KITTI provides a unique edge with its photo-realistic synthetic data, contributing to experimental control in simulation environments, albeit with a relatively moderate citation impact. Datasets like CULane and ApolloScape, with their sizable sample counts, are advantageous for focused domain tasks, such as lane detection and general driving scene understanding, respectively. However, some datasets such as TITAN and BrnoCompSpeed offer specific yet limited applications due to their restricted sample sizes and fewer annotated classes. The research utility of datasets like CCD and DADE is highlighted by their moderate sample volumes; however, their lack of citations indicates potential underutilization or emerging utility in the field. Several datasets like HEV-I and V2V4 Real are noted for their multi-modal sensor data collected in specific geographical regions or setups, presenting strengths in context-specific model validations.