Abstract
Autonomous driving technology offers a promising solution to reduce road accidents, traffic congestion, and fuel consumption. The management of vehicular networks is challenging as it demands mobility, location awareness, high reliability and low latency of data traffic. In this paper, we propose a novel communication architecture for vehicular network with 5G Mobile Networks and SDN technologies to support multiple core networks for autonomous vehicles and to tackle the potential challenges raised by the autonomous driving vehicles. Data requirements are evaluated for vehicular networks with respect to number of lanes and cluster size, to efficiently use the frequency and bandwidth. Also, the network latency requirements are analysed, which are mandatory constraints for all the applications where real time end-to-end communication is necessary. A test environment is also formulated to evaluate improvement in vehicular network using SDN-based approach over traditional core networks.
Highlights
The automotive industry has recently shifted from developing advanced vehicles to smart transportation, which focuses on the evolution of new intelligent vehicles for autonomous driving and control capabilities [1]
The test scenario is being created with Cisco devices and HP Software Defined Network (SDN) compatible devices, where hosts act as RSUs and the server acts as core network with to achieve 915.7 Mbps while SDN (TCP) based traffic flow
It is important that the autonomous driving system is extended to network level instead of a stand-alone solution, in order to access the full benefits of the communication technology and to implement a secondary layer of safety
Summary
The automotive industry has recently shifted from developing advanced vehicles to smart transportation, which focuses on the evolution of new intelligent vehicles for autonomous driving and control capabilities [1]. The autonomous driving vehicles (ADVs) are highly-complex multidisciplinary products, which integrate sensors, automotive control, information processing, artificial intelligence and ultrafast communication capabilities. In order to incorporate autonomous driving, vehicles should be capable of sensing the environment and performing control and path planning without any human intervention [3]. Several challenges still need to be fully addressed for autonomous driving [5, 6], such as: To have knowledge of the exact position of the vehicle and to decide how to reach the destination optimally
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