| الوصف: |
Vehicle-to-Everything (V2X) communication has emerged as a transformative technology in automotive engineering, fostering a paradigm shift towards intelligent transportation systems (ITS). This communication paradigm enables real-time data exchange between vehicles, infrastructure, and pedestrians, paving the way for enhanced safety, traffic efficiency, and environmental sustainability. However, the sheer volume and complexity of data generated in V2X networks necessitate robust and intelligent processing techniques. This paper delves into the synergistic integration of Artificial Intelligence (AI) with V2X communication, exploring its potential to revolutionize automotive engineering. The paper commences by establishing the critical role of V2X communication in ITS. It elaborates on the different types of V2X communication, including vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-pedestrian (V2P) communication. The paper then dissects the challenges associated with V2X networks, such as data overload, latency issues, and security vulnerabilities. These challenges can significantly impede the effectiveness of V2X communication and hinder the realization of its full potential. To address these challenges, the paper investigates the transformative power of AI in enhancing V2X communication. It provides a comprehensive overview of various AI techniques that can be leveraged for this purpose. Machine learning (ML) algorithms, a prominent subset of AI, play a pivotal role. Supervised learning techniques, such as support vector machines (SVMs) and random forests, can be employed to classify and prioritize critical information exchanged within the V2X network. This enables vehicles to focus on safety-critical data, ensuring timely decision-making in dynamic traffic scenarios. Unsupervised learning algorithms, like k-means clustering and anomaly detection, can be utilized to identify patterns in traffic flow and detect potential accidents or infrastructure malfunctions. This facilitates ... |