# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 7 Applications of AV Traffic Modeling
### Safety Analysis  
The field of safety analysis in autonomous vehicle (AV) traffic modeling is extensively covered in the provided research papers, each contributing unique insights and methodologies.  
[PERSON] et al. highlighted the pivotal role of Computer Vision (CV) techniques in microscopic traffic safety analysis, particularly through the use of Surrogate Safety Measures (SSMs). These techniques bridge the gap between video processing and traffic safety modeling, providing a methodological foundation for analyzing near-miss events and other safety-critical situations [274]. This work was complemented by [PERSON] et al., who emphasized the importance of robust driver models in the certification and simulation-based testing of AVs. By ensuring that AVs meet safety standards comparable to those of competent human drivers, these models form an essential component of the broader safety analysis framework [275].  
Building on these foundational elements, [PERSON] et al. applied the System Theoretic Process Analysis (STPA) method to adaptive cruise control (ACC) systems. This approach identifies unsafe control behaviors and establishes safety requirements across various scenarios, further contributing to the systematic analysis of AV safety [276]. [PERSON] and [PERSON] expanded the scope by exploring various aspects of AV technology, including sensor systems, control algorithms, and communication technologies, and their roles in enhancing road safety. Their comprehensive analysis demonstrates how AVs can mitigate common traffic risks, reduce accident rates, and improve overall traffic efficiency, thus reinforcing the need for integrated safety systems [277].  
[PERSON] et al. introduced a data-driven framework for analyzing the safety of autonomous driving systems, leveraging model learning techniques to simulate and evaluate AV behavior in diverse traffic scenarios. This approach aligns with the need for robust simulation tools that can predict and mitigate potential safety issues before AVs are deployed in real-world environments [278]. [PERSON] et al. contributed a probabilistic model that studies the impact of AV perception system imperfections and safety mechanisms, particularly in the presence of road hazards. This model underscores the importance of accounting for uncertainty and variability in safety analysis, ensuring that AV systems can operate safely even in challenging conditions [279].  
[PERSON] et al. focused on the application of deep learning (DL) methods to enhance traffic video analysis, offering a comprehensive processing pipeline that extracts operational safety metrics. These metrics are crucial for improving traffic safety, as they provide real-time insights into the performance and safety of AV systems [280].  
These papers collectively highlight the importance of integrating advanced modeling techniques, robust driver models, and cutting-edge CV and DL methods to enhance the safety and reliability of AVs in traffic environments. Each study provides a piece of the puzzle, from theoretical frameworks and practical verification methods to real-world applications and future challenges, forming a comprehensive body of knowledge essential for advancing AV traffic safety analysis.