## 1 Introduction
### Related Works
The paper [41] provides a comprehensive examination of a wireless sensor system for traffic monitoring. It includes computationally efficient and reliable algorithms for vehicle detection, speed and length estimation, classification, and time synchronization. Field studies conducted on both highways and urban roads across various scenarios and traffic conditions yielded impressive results, including 99.98% detection accuracy, 97.11% speed estimation accuracy, and 97% accuracy in vehicle classification based on length. [PERSON] and [PERSON] conducted a classification of road types and surfaces using data from three deep neural network (DNN) models. They achieved an average accuracy of 94.27% for learning data and 92.70% for verifying road surface classification validity, distinguishing between asphalt, gravel, or dirt road sections [42]. In order to identify road potholes, traffic lights, and parking lots, [PERSON], [PERSON], and [PERSON] developed an algorithm that integrated with a mobile phone, achieving an accuracy of 92% [43]. The paper [44] highlights a detailed study of a Technology Roadmap on Intelligent and Connected Vehicles (2020) with a focus on its strategic value, technical content, and characteristics. They assessed the effects of this strategy on researchers, industries, and international strategies, highlighting the vehicle's technical structure as the \"three rows and two columns\" architecture. The methodology for the roadmap was presented via a case study, which is a technology analysis and the possible development routes. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Study [Ref.]** & **Focus Area** & **Key Aspects** \\ \hline
[41] & vehicle detection, speed, length & Wireless sensor, Algorithms \\ \hline
[42] & road surface, road type & deep neural network (DNN) 94.27\% \\ \hline
[43] & road surface & Sensors and Algorithm \textgreater{}92\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._The study [45] was focused on traffic congestion in weaving areas by suggesting different active fine lane management methods that can be applied in scenarios with Intelligent Connected Vehicles (ICVs). The authors carried out a VISSIM simulation to study the traffic flow as well as vehicle driving behavior in the urban expressway weaving zones. The study showed that if ICVs are instructed to complete lane changes before entering the weaving area, most time can be saved during this phase, and traffic delays can be eliminated, thus leveraging lane management and widening traffic capacity. The researchers in [46] assessed the safety impacts of a variety of connected vehicle work zone advisory systems. They performed a comparative study using microsimulation experiments to demonstrate that CV-based warning systems are more effective than dynamic message signs (DMS). The study recommended the choice of optimal DMS positioning to achieve safe and high traffic flow conditions and stressed that real testing in the real world is needed to validate these suggestions. The manuscript [47] was concerned with anomaly detection in intelligent transportation systems (ITS) served by a Wavelet Kernel Network with Omni-Scale Convolutional (WKN-OC). This method emphasizes the processing of high-frequency signals and feature extraction to enhance the system's ability to detect data anomalies traced to a cyberattack or sensor failures in Connected Automated Vehicles (CAVs). The authors validated that the WKN-OC model works on the SPMD data set and proved that the model has a high degree of accuracy when detecting multi- and mixed anomalies. Finally, this research process [48] examined the current state of planning and control algorithms, focusing on self-driving vehicles that operate in urban areas, particularly.