# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 2 Autonomous Vehicle Technology
The development of AV technology extends beyond sensor systems and includes advanced control and decision-making algorithms that allow these vehicles to navigate complex environments safely and efficiently. These algorithms reduce accidents caused by human error and improve traffic flow and travel times [16; 17]. Given the high-dimensional state space and sparse rewards in urban environments, decision-making for AVs can be particularly challenging. Techniques like Coordinated Convolution Multi-Reward Proximal Policy Optimization (CCMR-PPO) have been developed to address these challenges, optimizing state space and designing multi-objective reward mechanisms to enhance performance in simulations [18]. Specific scenarios, such as navigating roundabouts, involve formulating decision-making as a Partially Observable Markov Decision Process (POMDP) and using algorithms like Partially Observable Monte-Carlo Planning (POMCP) to improve policy predictions and state transitions [19]. AVs must also handle unconventional scenarios, including adverse weather conditions and unsignalized intersections, where traditional planning methods and emerging machine-learning approaches are critical for ensuring safety and reliability. Deep reinforcement learning (DRL) techniques have shown promise in generating decision-making policies that account for interactions with pedestrians, enabling AVs to exhibit natural driving behaviors [20]. Methods like Deep Deterministic Policy Gradient (DDPG) ensure that AVs maintain safe distances and stay centered on the road during path-following tasks [21]. Communication systems are equally essential for AV technology, ensuring safety, reliability, and efficiency in transportation. The Internet of Autonomous Vehicles (IoAV) and the Internet of Vehicles (IoV) are crucial frameworks that enable AVs to interact with each other and their environment through Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X) communications [22; 23; 24]. The integration of 5G networks supports these communications due to their high throughput and low latency, meeting the complex dynamics and real-time requirements of AVs [25]. Furthermore, the use of big data technologies enhances the functionality and safety of AV systems by facilitating the storage and processing of vast amounts of data generated by AVs [22]. Security remains a critical aspect of AV communication, with tamper-resistant broadcasting schemes developed to protect data from unauthorized access and tampering, ensuring secure and efficient information dissemination [26]. The deployment of AV technology also presents opportunities to address social equity by improving transportation access in underserved communities, thereby reshaping urban landscapes and enhancing public transportation systems. Looking ahead, the evolution toward 6G communication promises even greater bandwidth, lower latency, and higher data rates, further enhancing AV capabilities and opening new research and development horizons in vehicular communication [24]. Overall, the convergence of IoT, big data, and advanced communication technologies like 5G and 6G is driving the advancement of AV technology, making it a cornerstone of modern intelligent transportation systems (ITSs) and smart cities.