# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 2 Autonomous Vehicle Technology  
Autonomous vehicle (AV) technology, commonly referred to as self-driving or driverless cars, represents a significant innovation in the transportation sector. These vehicles are designed to sense their environment and operate without human intervention, utilizing a sophisticated combination of sensors, cameras, machine learning algorithms, and real-time data processing [6; 7]. The Society of Automotive Engineers (SAE) International classifies AVs into six levels of automation, each representing an increasing degree of autonomy [8]:  
* Level 0 (No Automation)--the driver is responsible for all driving tasks. The vehicle may provide warnings and momentary assistance but does not control any aspect of driving. * Level 1 (Driver Assistance)--the vehicle can assist with either steering or acceleration/deceleration using information about the driving environment. The human driver handles all remaining aspects of driving. * Level 2 (Partial Automation)--the vehicle can control both steering and acceleration/deceleration under certain conditions. The driver must remain engaged and monitor the environment continuously. * Level 3 (Conditional Automation)--the vehicle can perform all aspects of driving in specific conditions. The driver must be ready to take control when requested by the vehicle. * Level 4 (High Automation)--the vehicle is capable of performing all driving tasks and monitoring the environment in certain conditions without human intervention. The vehicle may request human intervention in rare cases. * Level 5 (Full Automation)--the vehicle can perform all driving tasks under all conditions without any human intervention. No driver attention or intervention is required at any time. The core technologies behind AVs include computer vision, sensor fusion, localization, path planning, and driving control, which collectively enable the vehicle to operate autonomously [9]. These technologies allow AVs to communicate with other vehicles (V2V), infrastructures (V2I), and pedestrians to enhance safety and efficiency. However, despite the rapid advancements, challenges remain in ensuring safety, reliability, regulatory approval, and public acceptance. The increased connectivity also exposes AVs to cyber threats, necessitating robust security measures. Autonomous vehicles rely heavily on advanced sensor and perception systems to navigate and operate safely. These systems integrate various sensors, including cameras, LiDAR, and radars, to create a comprehensive understanding of the vehicle's surroundings. While cameras and radars are cost-effective and reliable, LiDAR provides detailed 3D mapping and object detection capabilities, though it is more susceptible to adverse weather conditions [10; 11]. The fusion of data from these sensors is crucial for enhancing the accuracy of environmental perception, which is essential for safe operational planning. Machine learning algorithms play a vital role in processing sensor data and improving the efficiency and accuracy of object detection and image segmentation. For instance, the MultiTaskV3 detection-segmentation network achieves high precision and low power consumption, particularly when implemented on specialized platforms like the AMD Xilinx Kria KV260 Vision AI [12]. However, these systems' performance can degrade under adverse weather conditions, which affect the quality of data from cameras and LiDAR [13]. To mitigate these challenges, new methods, such as monocular depth estimation using YOLOv7 and attention mechanisms, have been developed to provide low-cost, accurate depth information from RGB images [14]. Robust positioning and mapping systems are also crucial for maintaining submeter-level accuracy across various environments, enhancing AVs' reliability and safety. However, the broader acceptance and integration of AVs into public roads will depend on addressing concerns related to cost, security, privacy, and environmental impact, as highlighted by studies on the willingness of different populations to adopt AVs [15].