Abstract

Edge computing is revolutionizing autonomous vehicle (AV) networks by enabling real-time data processing, low-latency communication, and enhanced decision-making at the network edge. Autonomous vehicles generate massive volumes of data from sensors such as LiDAR, radar, cameras, and GPS, which require rapid analysis to ensure safe navigation, collision avoidance, and traffic optimization. Traditional cloud computing solutions often struggle with latency and bandwidth constraints, potentially compromising the safety and efficiency of AV operations. By processing data locally on edge devices, edge computing reduces dependency on centralized servers, allowing instantaneous responses to dynamic driving conditions and improving reliability in mission-critical scenarios.
In AV networks, edge nodes can include roadside units, vehicle-mounted processors, and micro data centers, all collaborating to share relevant traffic, environmental, and vehicle status information. Integration with vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication enhances cooperative driving, traffic management, and predictive maintenance. Additionally, edge computing supports AI and machine learning algorithms for real-time object detection, path planning, and adaptive control, while preserving privacy by minimizing the transmission of sensitive data to central servers.
Challenges include heterogeneous hardware management, cybersecurity risks, efficient resource allocation, and scalability to support dense urban traffic networks. Advances in 5G connectivity, vehicular fog computing, and software-defined networking (SDN) are expected to further optimize edge-enabled AV networks, enabling robust, low-latency, and intelligent transportation systems. Future developments may also explore energy-efficient edge architectures, collaborative multi-access edge computing (MEC), and integration with blockchain for secure data exchange.
By leveraging edge computing, autonomous vehicle networks can achieve higher safety standards, improved traffic flow, reduced latency, and enhanced overall operational efficiency, paving the way for the widespread adoption of intelligent, autonomous, and connected transportation systems.