# Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison
## 2 AI in Autonomous Vehicles  
Research on image analysis for autonomous vehicles is an expansive and swiftly evolving domain intersecting with various interdisciplinary areas such as vision, machine learning, sensor fusion, federated learning, and more. Key advancements in recent years include the application of cutting-edge methodologies such as Convolutional Neural Networks (CNNs), transformer models, Generative Adversarial Networks (GANs), and Vision Language Models (VLMs). The existing literature and publications cover a wide range of topics, ranging from algorithm development to practical applications and system validation. Object detection and classification are fundamental tasks for autonomous vehicles, enabling the identification of pedestrians, vehicles, traffic signs, and other road users [36]. Notable research includes YOLO (You Only Look Once), proposed by [PERSON] et al., known for its efficiency and accuracy in real-time object detection [37; 38]. In the latest YOLO model versions, there have been significant improvements in detecting small objects in images. Advanced image processing techniques and optimized neural network architectures have enabled these models to achieve a greater precision and effectiveness in identifying small elements, marking a substantial advancement in the field of computer vision [39; 40; 41]. Additionally, R-CNN (Region-Based Convolutional Neural Network) [42], developed by [PERSON] and colleagues, integrates region proposal networks with deep learning, significantly improving detection performance. Semantic segmentation involves classifying each pixel in an image to understand the full scene context. Exemplary works include DeepLab developed at Google Research, which uses deep convolutional networks and atrous convolutions for detailed spatial hierarchies [43], and SegNet [44] proposed by [PERSON] et al., designed for pixel-wise semantic segmentation while conserving memory and computational resources, making it practical for real-time applications. The continuous development of better image analysis algorithms and architectures is paramount. Notable advancements include EfficientNet by the Google Brain team [45], which uses a compound scaling method for state-of-the-art results regarding efficiency and accuracy, and Vision Transformers (ViTs), which demonstrate promise in scaling and capturing long-range dependencies [46]. Depth estimation and stereo vision are crucial for understanding spatial relationships. Notable advances include traditional stereo matching techniques like Semi-GlobalMatching (SGM) [47] by [PERSON], used in stereo vision systems for calculating depth maps, and monocular depth estimation using deep learning by [PERSON] et al. [48], which significantly advances the capability for depth estimation from single images. Sensor fusion combines data from multiple sensor types such as cameras, LiDAR, radar, and Vision Language Models (VLMs) to enhance perception system robustness and contextual understanding [18]. VLMs, which integrate visual data with textual information, are emerging as crucial tools for enhancing the contextual understanding of scenes in autonomous driving. By combining data streams from diverse sensor types--including cameras and LiDAR--VLMs facilitate improved perception system robustness, as they can leverage descriptive labels to enhance image analysis. Recent advancements, such as the DriveGenVLM framework developed by researchers at Columbia University, demonstrate the potential of VLMs in generating and analyzing real-world video sequences using diffusion models [49]. Additionally, a comprehensive review from 2024 highlights the applications of VLMs in various aspects of autonomous driving, including perception, navigation, planning, decision making, and control, emphasizing their role in improving safety and efficiency [50].