# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 8 Challenges and Limitations  
The integration of autonomous vehicles (AVs) with human-driven vehicles (HDVs) presents several challenges and limitations, as highlighted in recent research. While these studies provide valuable insights, they often lack a thorough examination of the practical limitations and challenges associated with implementing these methods in real-world scenarios. [PERSON] et al. [287] focused on modeling mixed traffic flows in off-ramp diverging areas, highlighting the deterioration in safety and efficiency at higher proportions of cooperative adaptive cruise control (CACC) vehicles. They suggest that guiding lane changes can improve traffic flow efficiency. However, they did not deeply explore how these lane-change strategies might be affected by factors such as varying traffic densities, driver compliance rates, or the accuracy of vehicle communication systems. [PERSON] et al. [288] proposed an algorithm using transmissibility operators to detect and mitigate faults in mixed vehicle platoons, emphasizing the challenge of predicting human-driver behaviors and the need for robust control methods. Yet, the practical challenges of accurately modeling human-driver unpredictability and the potential computational overhead of implementing such a robust control system in real time are not fully addressed. [PERSON] et al. [289] examined cooperative driving between AVs and intelligent human vehicles (IHVs), addressing the stochastic nature of human input and system delays. Their stochastic model predictive control framework aims to optimize vehicle coordination despite these uncertainties. However, these authors could have delved further into the limitations posed by real-world communication delays, sensor inaccuracies, and the computational complexity of their predictive models in dynamically changing traffic conditions. [PERSON] et al. [290] investigated the altruistic behavior of AVs towards HDVs during car-following events, finding that such behavior can significantly reduce disturbances for HDV followers, though it may reduce efficiency for AV passengers. Nevertheless, the potential trade-offs between passenger comfort and overall traffic efficiency, as well as the long-term impacts of such altruistic behavior on traffic patterns, remain underexplored. Collectively, these studies underscore the importance of adaptive control strategies, robust fault detections, and cooperative behaviors in ensuring the seamless integration of AVs and HDVs. However, the limitations and challenges of implementing these methods in real-world scenarios deserve a more detailed exploration to understand the potential barriers to their widespread adoption. The challenge of uncertainty in autonomous vehicle (AV) behavior has been explored in various recent studies. While these approaches are promising, the practical implications of these uncertainty management strategies require a deeper investigation. [PERSON] et al. [291] proposed an uncertainty-aware decision-making framework utilizing a fully parameterized quantile network (FPQN) and conditional value at risk (CVaR) to improve AV decision making at uncontrolled intersections, highlighting the role of surrounding vehicles and pedestrians in decision making. However, the complexity of real-world interactions at such intersections and the potential computational demands of these methods warrant further analysis. [PERSON] et al. [292] introduce an uncertainty-bound reinforcement learning (UBRL) framework to estimate and constrain the performance uncertainty of deep reinforcement learning (DRL) models, ensuring safer AV decisions by comparing them against baseline policies. Nonetheless, the limitations of reinforcement learning in dynamically changing environments and the challenge of generalizing learned policies across diverse scenarios need further exploration. [PERSON] et al.