# Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison
## 1 Introduction  
Environmental image analysis is pivotal in autonomous vehicle development and operation, serving as the cornerstone of their perception and decision-making capabilities. Accurate and real-time image analysis allows these vehicles to interpret their surroundings effectively, facilitating safe and efficient navigation. A wide range of autonomous vehicles, including cars [1, 2, 3], ships [4, 5], underwater vehicles [6, 7, 8], unmanned aerial vehicles (UAVs) [9, 10, 11], and robots [12, 13], employ an array of sensors, including cameras, LiDAR, and radar, to capture detailed images and environmental information [4, 10, 11, 14, 15]. These images undergo sophisticated processing through algorithms and machine learning techniques [16, 17] to detect, classify, and track objects such as pedestrians, cyclists, other vehicles, traffic signs, and lane markings. Recently, Vision Language Models (VLMs) [18, 19, 20] have shown promise by integrating visual and textual information, enhancing the contextual understanding of environments, which is critical for real-time processing and decision making in autonomous systems. Additionally, federated learning allows for decentralized model training, enhancing privacy while leveraging data from multiple sources, thus refining image analysis algorithms. Federated learning is also possible through the use of blockchain technology, i.e., Internet Computer [21]. Due to the decentralized nature of blockchain technology, data can be stored and processed in a distributed manner, eliminating the need for a central server that could become a target for attacks or privacy violations [22]. Internet Computer enables the creation of smart contracts that can manage the model training process in a secure andtransparent manner [23]. Additionally, this technology can be integrated with federated learning, where models are trained locally on user devices and only updated weights are sent to the blockchain network. This allows users' data to remain on their devices, minimizing the risk of data leakage. Autonomous vehicles are categorized into different levels of automation, as defined by the Society of Automotive Engineers (SAE) (Figure 1), ranging from level 0 (no automation) to level 5 (full automation) [24]. Higher levels indicate increased system independence and decreased human intervention, primarily driven by advancements in image analysis technology. At level 0, image analysis is not used and the driver is responsible for all aspects of driving. At levels 1 (Driver Assistance) and 2 (Partial Automation), image analysis becomes more critical, managing tasks such as adaptive cruise control, lane-keeping assistance, and semi-automated features like auto-steering and traffic-aware cruise control, respectively. Level 3 (Conditional Automation) marks a significant shift, where vehicles can take full control under certain conditions, heavily relying on high-definition cameras and advanced algorithms for monitoring the environment and detecting objects. At level 4 (High Automation), vehicles operate without human intervention in most scenarios, necessitating robust image analysis systems that integrate visual data with inputs from other sensors such as LiDAR and radar. Finally, level 5 (Full Automation) epitomizes vehicle autonomy, with the vehicle handling all driving aspects in any condition without human oversight, requiring image analysis capabilities surpassing human abilities in interpreting visual information and making instantaneous decisions  
The significance of image analysis spans all autonomy levels, enhancing situational awareness, safety and redundancy, complex decision making, adaptability, and precise mapping and localization, ultimately supporting the regulatory and social acceptance of autonomous systems. Technologies like federated learning facilitate collective model training from distributed vehicle fleets, improving the robustness and generalizability of image analysis algorithms without compromising data privacy.