## 4 Related Surveys  
The authors in [64] reported different trust management schemes based on three types of models: entity-centric trust models, data-centric trust models, and combined trust models: a multifaceted trust, a trust and reputation infrastructure-based proposal, a distributed trust, a deterministic approach, a trust model based on various factors of a message, a voting system based on distance from the event, categorized decentralized trust management, an evaluation scheme, and an attack-resistant trust management scheme. They did not, however, present an ML scheme to enhance trust. In the survey in [65], security aspects of IoV, including security requirements and challenges, were considered. Various security threats and existing security solutions for each threat were explored. However, neither ML-based solutions nor trust management were examined in this analysis.  
ML-based trust evaluations are free from the inadequacies of traditional trust evaluation methods, as they can carry out trust evaluation to establish a trust model using data about other available trust-related features. ML-based trust evaluations are capable of finding substitute data for the unavailable indirect recommendation and direct historical interaction information in newcomer trustees. ML methods aim to improve the trust evaluation's accuracy compared with traditional trust evaluation methods [66]. Based on IoV's security requirements, such as authentication and availability, the authors of [67] classified various security attacks and suggested possible solutions. ML-based solutions were not demonstrated.  
Many different trust management models were examined in the survey in [68], and the authors specifically focused on IoVs. One of the intelligent solutions suggested for trust management is the use of context awareness. The authors attempted to demonstrate the potential benefits of context awareness in vehicular networks; however, the authors did not consider ML as a potential solution to the problems of trust and security in IoV. The observation of genetic algorithms (GA), one type of optimization technique, is the focus of reference [69], which aimed to improve the security of IoV networks through the observation of GA. According to the survey, GA is frequently utilized to improve the security of IoV comparisons via various swarm intelligence optimizations. The utilization of the GA in an IoV security system was carried out to improve the accuracy of the network as well as the detection of malicious nodes. However, this survey omitted the use of ML and the improvement in IoV trust.  
In [70], the authors discussed the use of ML solutions in VANET such as applications, routing, security, resource allocation and access technologies, mobility management, and integrated architectures. The challenge of trust in the VANET environment was not discussed. In [50], the authors focused on the proposed ML method for ITS challenges. Security and trust were not addressed, but they addressed ML-based works from the point of view of vision-based perception, infrastructure, and resource management as well as the prediction of traffic flow, the behavior of vehicles and users, and the road occupancy of ITS. In [71], the authors investigated the potential integration of Digital Twins (DTs) into IoV, focusing on improving the system design without mentioning machine learning or trust. In [72], the security challenges within vehicular networks are discussed primarily, with an emphasis on attacks and preventive measures, but without explicit mentions of machine learning or trust. In [73], the author examined the security challenges of IoV environments, emphasizing various attack types and the need for privacy preservation and strong authentication, thereby contributing to discussions on security but without ML techniques.  
We provide a comprehensive discussion of related surveys, the difference between our survey and these related surveys, as well as the survey's main focus, which is shown in Figure 2. Despite