# Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison
## 6 Conclusions  
Environmental image analysis has emerged as a foundational component in the evolutionary trajectory of autonomous vehicle technology. This review elucidated the multifaceted role that image analysis plays across varying levels of vehicle autonomy, and highlighted the intricate interplay between advanced datasets, cutting-edge AI models, and real-world applications.  
The availability and utilization of comprehensive datasets like Cityscapes, nuScenes, and CARLA have revolutionized the training, validation, and benchmarking of autonomous vehicle perception systems. These datasets provide rich and diverse scenarios that are critical for developing robust AI models capable of handling the vast array of driving environments and conditions. The integration of multi-sensor data, including LiDAR, radar, and high-resolution cameras, in these datasets facilitates the development of sophisticated sensor fusion techniques that enhance environmental perception.  
The significant improvements in Convolutional Neural Networks (CNNs), Transformer models, Generative Adversarial Networks (GANs), and Vision Language Models (VLMs) have propelled the field of image analysis. Techniques such as EfficientNet and Vision Transformers have demonstrated a remarkable efficiency and accuracy, while advancements in GANs have considerably enriched training datasets and improved their robustness.  
The incorporation of federated learning and blockchain technology ensures privacy and security by enabling decentralized model training. This innovative approach allows for continuous learning from diverse data sources without compromising sensitive data.  
Models like YOLOv8-DSAF and RT-DETR have set new benchmarks in semantic segmentation with a superior speed and accuracy, crucial for real-time processing. In 3D object detection, models such as EA-LSS and BEVFusion stand out for their precision in identifying and localizing objects in dynamic traffic scenarios.  
The assessment of leading models on simulation platforms like CARLA has provided valuable insights into their practical capabilities. For instance, models like ReasonNet and LAV in CARLA Leaderboard 1.0 and CarLLaVA in Leaderboard 2.0 exemplify notable advancements in route completion, driving scores, and infraction minimization. Moreover, the Talk2 Car platform emphasizes the growing importance of integrating natural language processing with vision systems, as evidenced by high-performing Vision Language Models like Udder_HuBo-VLM.  
The continuous evolution of image analysis technologies is integral to advancing the autonomy of vehicles. As AI models become more sophisticated and datasets more comprehensive, the capabilities of autonomous vehicles will continue to expand, pushing the boundaries of safety, efficiency, and intelligence in transportation. Ongoing research and development, coupled with rigorous testing and validation, will drive the deployment of reliable and sophisticated autonomous systems, ultimately transforming the landscape of modern transportation.  
Conceptualization, M.K.; methodology, M.K. and S.R.; formal analysis, M.K. and S.R.; investigation, M.K. and S.R.; resources, M.K. and S.R.; writing--original draft preparation, M.K. and S.R.; writing--review and editing, M.K. and S.R.; visualization, M.K. and S.R.; supervision, S.W.; project administration, S.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.