## 2 Overview of Artificial Intelligence and Machine Learning in Road Transport Systems
### Advance Approaches for Incident Detectors of Road Traffic Accidents Using AI and ML
#### 2.5.6 Incident Detector through Wireless Communications with the Internet of Vehicles  
In recent years, extensive research has been conducted on the effectiveness of wireless communication systems, either under the influence of small- and large-scale fading or their composite fading [77]. The freeway accident log includes local road traffic data from 15,000 loop detectors and tweets about accidents. Over 80% of the tweets tied to accidentscan be correlated to nearby abnormal traffic data, according to the accident log, which can detect roughly 66% of accident-related tweets [78].  
Incident detection by wire communication is crucial but some require data transfer from a vehicle to an infrastructure (V2I), while others are vehicle-to-vehicle (V2V). For three basic reasons, wireless communication for automobiles is complicated. Firstly, there are a lot of surfaces that reflect radio waves in the environment where automobiles operate, particularly in cities. Secondly, different types of communication disruption are caused by the large variety of speeds that cars drive at. Finally, adjacent transmitters and sources within cars frequently cause radio frequency (RF) interference [79].  
Figure 3 demonstrates how vehicular ad hoc networks (VANETs) enable wireless communication between vehicle-to-vehicle (V2V) and vehicle-to-fixed infrastructure (V2 FI) in this situation (V2I). Additionally, by connecting to the city's roadside units (RSUs) via multi-hop V2V communication, VANET routing protocols can lessen the need for fixed infrastructure [80].  
This study created the HNMI to measure the networks of various suppliers simultaneously. We find that the lone wireless network struggles to provide the internet access demands in the high-speed mobility scenario. Heterogeneous network utilization in its entirety provides several benefits [81]. Initial networks are compressed, using network quantization by lowering the bit count for each weight parameter. Quantization utilizes low-precision data to represent the original high-precision data by converting 32-bit floating-point data into 16-bit floating-point data, 16-bit fixed-point data, 8-bit fixed-point data, etc.; this is the conventional method. Binarized neural networks, ternary weight networks, and XNOR networks are a few of the related technologies [82].  
V2I vehicle-to-infrastructure communication offers a solution for longer-range vehicular networks in the detection of the incident using vehicle-to-vehicle (V2V) communication multi-hop/multicast techniques. It is most effective in close-proximity vehicular networks (such as traffic signal timing adaptation) (e.g., route guidance). According to their principal use, VANET applications can be divided into two broad categories: safety applications, such as collision and accident avoidance, and non-safety applications. Various wireless communication technologies are employed for VANET communications to create a local and integrated information system [83] and address the characteristics of wireless infrastructure-based and ad hoc networks for communication between mobile devices in automobiles. As there are so many different ports and interfaces for wired communication due to the heterogeneity of gadgets, wireless communication systems are extremely attractive for automobile communication [84].  
The seamless integration and convergence of vehicular communication networks, information and transportation systems, and mobile devices and networks will face nu  
Figure 3: Epic wireless communication with the internet of vehicles.  
merous technical, economic, and governmental challenges. It is crucial to create vehicle communication systems that allow drivers and other participants to share information in real time with high dependability. This enables pervasive sensing to keep track of vehicles' conditions and surroundings, develop data analytics tools to process the massive amounts of data generated by connected vehicles, and create middleware platforms for data management and sharing [85].