# Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison
## 3 Overview of Available Datasets
### Simulators  
Table 3 presents various simulators designed to train deep neural models for autonomous driving. The most widely cited simulator is CARLA (1128 citations), offering versatile urban driving simulations with detailed annotations, including 12 semantic classes, bounding boxes, and vehicle measurements. AirSim, developed on Unreal Engine, supports simulation-in-the-loop (SIL) and hardware-in-the-loop (HIL) for drones and cars, providing a valuable tool for multiplatform simulations. The TORCS simulator is notable for simulating detailed vehicular dynamics, facilitating advanced driving behavior analyses [127]. V2X-SIM focuses on synthetic collaborative perception, capturing data both from the roadside and vehicles, emphasizing vehicle-to-everything (V2X) communications. SUMMIT is a versatile simulator supporting applications across perception, control, planning, and end-to-end learning. Other simulators include CVRPTW, which addresses the Capacitated Vehicle Routing Problem with Time Windows, and CARL, which enhances AI training with physics context features. The 3D VTSim provides accurate 3D bounding box annotations through simulation, supporting precise object detection tasks. The MUAD simulator generates realistic synthetic images under various weather conditions, annotated for multiple tasks. SDN serves as a benchmark for navigation tasks, evaluating the integration of dialogue and physical navigation actions. MULTIROTOR-GYM is dedicated to learning the control policies for UAVs within a gym environment. Emerging simulators such as DEAP CITY, which includes city pollution data, and EviLOG, with real-world LiDAR point cloud data, extend the utility of simulators to environmental analytics and precise sensor data simulations. These simulators collectively offer comprehensive tools essential for advancing autonomous driving technologies, focusing on aspects varying from control policies to environmental context and collaborative perception. This augmented dataset overview brings into focus an interesting array of simulation and synthetic data sources aimed at enhancing autonomy in driving and other vehicular contexts. Leading the way is CARLA, a widely cited urban driving simulator with robust semantic annotations, making it highly valuable for both academic and industry research. Similarly, AirSim offers a versatile simulation platform for both aerial and ground vehicles, supported by its moderate citation count, indicating its cross-domain applicability. Comparatively, TORCS provides a specialized focus on vehicular dynamics simulations with fewer citations, suggesting a niche but steady usage. V2X-SIM stands out in the emerging field of collaborative perception despite its relatively low citation count, indicating ongoing exploration in synthetic data from both roadside and vehicular perspectives. Multi-disciplinary simulators like SUMMIT and CARL, although less cited, feature support for comprehensive applications ranging from perception to end-to-end learning, providing potential growth areas for research in autonomous systems. The specific problem-oriented dataset CVRPTW emphasizes complex logistic problems and demonstrates modest citations, emphasizing its specialized use case in routing and planning scenarios. Datasets like 3D VTSim and MUAD, focusing on accurate 3D bounding boxes and diverse weather condition simulations, respectively, are in the nascent stage of adoption but show promise for detailed perception tasks. Benchmarks like SDN and MULTIROTOR-GYM offer integrated environments for evaluating navigation and control policies, particularly for UAVs, though their citations remain low, suggesting potential for traffic in specific research contexts. Less cited datasets such as DEAP CITY and EviLOG, with a focus on environmental impact and real-world LiDAR point clouds, respectively, seek to bridge the gap between simulations and practical applications, yet are still gaining traction.