## 4 Cyber Security Attacks in Vehicular Ad Hoc Networks (VANETs)  
Autonomous vehicles (AVs) operate utilizing two principal communication channels, namely vehicle-to-vehicle (V2V) and Vehicle-to-infrastructure (V2I) communications, which are critical components of the broader Vehicular ad hoc network (VANET) ecosystem. A detailed analysis of the structure and operations of a VANET is depicted in Figure 4.  
The V2V communication paradigm leverages the principles of peer-to-peer networking, enabling vehicles to establish mutual connections. This system is underpinned by the IEEE 802.11p protocol and is designed based on the assumption that vehicles within a specified range of radio communication can automatically form an ad hoc network. Within this network, nodes represented by the vehicles can share vital data points such as positional coordinates, speed metrics, directional vectors, and more. Combined with V2V, the V2I communication mechanism allows vehicles to establish connections with embedded electronic devices in the broader transportation infrastructure. The information exchanged between vehicles and infrastructure can be utilized for various applications, including enhancing traffic management, optimizing traffic flow, promoting fuel efficiency, and reducing environmental impact.  
Vehicular communication systems, underpinned by the IEEE 802.11p protocol, have emerged as a transformative force for improving road safety and traffic efficiency [45]. These systems leverage vehicle-to-vehicle (V2V) communication for sharing of pivotal data points such as positional coordinates and speed metrics, and vehicle-to-infrastructure (V2I) communication to interface with the embedded electronic devices in transportation infrastructure. However, the effectiveness of these communication methods relies heavily on the accuracy of wireless channel estimates, such as channel state information (CSI) and received signal strength. Consequently, researchers have been mobilizing efforts towards the development of deep-learning-based channel prediction algorithms and conducting measurement campaigns to generate reliable wireless channel estimates [45].  
Simultaneously, road traffic safety remains a vital concern in the domain of vehicular communication. [PERSON] et al. highlighted the development of predictive models to gauge crash risks based on varying driving conditions and the implementation of optimization techniques such as path selection and rest-break scheduling to augment road safety [46]. However, bridging the gap between research outcomes and real-time crash risk optimization remains a challenge. Complementing safety, security issues have gained attention due to the highly active and ever-changing topology of vehicular environments. Proposals for security models grounded in evolutionary game theory aim to identify common attacks and establish defenses [47]. Concurrently, the rise in smart car sensors and applications reliant on artificial intelligence and augmented reality has escalated challenges related to computational resources and latency requirements. Efforts are underway to advance secure multiaccess edge computing and intelligent vehicle control systems to meet these demands [48]. Overall, vehicular communication systems present an array of opportunities alongside a spectrum of challenges, underscoring the need for ongoing research and innovation.