## 3 Architecture of AV Components and AV Categorization  
Figure 8 provides a summary of the key content in this section. Figure 9 clarifies the different levels of autonomous driving, clarifying specifically the detail of intervention required with automotive vehicles. These levels are described as follows: Autonomous vehicles are categorized into six distinct levels ranging from 0 to 5, each representing different levels of self-driving technology they possess, as illustrated below.  
As presented in [1,71] by [PERSON] et al., autonomous vehicles usually contain five functional components as shown in Figure 9.  
* Perception;
* Localization;
* Planning;
* Control;
* System management.  
Perception serves to sense the vehicle's surroundings, identifying significant objects such as obstacles. Meanwhile, localization creates a map of the surroundings and accurately determines the position of the vehicle. Additionally, planning utilizes the information from perception and localization to chart the vehicle's overarching actions, encompassing route selection, lane changes and target speed settings. In addition, the control module supervises the execution of particular tasks, such as steering, acceleration and braking. Lastly, system management monitors the functioning of all components and offers a user interface for interaction between humans and machines.  
The intricate operation of autonomous vehicles consists of interconnected components, as illustrated in Figure 10.  
The control system serves as the vehicle's perspective brain. This central intelligence works in tandem with a network of sensors, including cameras, radar and LiDAR, which collectively gather real-time information about the vehicle's surroundings. These sensory data are then processed by perception algorithms, enabling the vehicle to decipher and comprehend its environment. Following perception, the vehicle's onboard artificial intelligence embarks on the critical task of planning and charting a course of action based on the assimilated data. This planning process involves decision-making algorithms that analyze factors such as traffic conditions, road obstacles and pedestrian movements. The ultimate goal is to generate a precise sequence of actions that will enable the vehicle to navigate its surroundings with safety and efficiency.  
Figure 8: Summary of Section 3’s content.  
Figure 9: Levels of automation in AVs—SAE levels.  
Operating in blended environments demands that autonomous vehicles flawlessly negotiate a diverse range of driving conditions, from bustling city streets to high-speed highways and unpredictable scenarios. To master this adaptability, a fusion of perception and planning is essential, equipping the vehicle to traverse varied landscapes with agility and accuracy.  
Vehicle-to-vehicle (V2V) communication is an integral component of the autonomous vehicle revolution, enabling real-time information exchange among vehicles. This interconnectedness fosters enhanced road safety and efficiency. V2V communication facilitates collaborative decision-making, empowering vehicles to anticipate and respond to the actions of their counterparts on the road. For example, if a vehicle detects an obstacle or abrupt braking, it can instantly transmit this information to surrounding vehicles, allowing them to adapt their trajectories accordingly. The seamless interplay between control systems, sensors, perception algorithms, planning strategies, blended environment adaptability and V2V communication lies at the heart of successful autonomous vehicle implementation. This integration enables autonomous vehicles to adapt to dynamically changing surroundings and make informed real-time decisions.