Connected vehicles cloud computing (CVCC): applications, challenges, and communication models

Abstract

If the dream of autonomous vehicles is to be realized, sophisticated computational and communication frameworks must be developed. While it is immediately apparent that such a system must be accurate, it may be less obvious that it must also be very efficient. In this paper, an autonomous vehicle system framework is presented. A case study, prepared to demonstrate the system, is detailed.

Connected Vehicular Cloud Computing (CVCC) is a mobile computing model that substitutes the stationary nodes of traditional cloud computing for mobile nodes attached to vehicles. CVCC architecture involves a one-to-one communication with a Cloud platform. This situation is already efficient for inter-vehicle operations. However, for inter-vehicle events, such a communication system can result in unacceptable delays in response time. To deal with this fact, a parallel system has been proposed.

One such system is the Vehicular Ad-Hoc Network (VANET). The VANET employs sensors and transmitters on vehicles to convey traffic information to stationary roadside units. The roadside units rely on Cloud resources to process the incoming information, and then convey results to affected vehicles within range.

It is proposed that CVCC and VANET could be combined to produce real-time monitoring and smart adjustments for autonomous vehicles in traffic conditions. The data collected by such a system make possible new artifacts and processes for traffic management and public safety. Several proposals are discussed in this paper.

The limitations of the CVCC/VANET system are set out, as well as the limitations of competing systems. Finally, the Least Action Principle (LAP) is introduced. The LAP is proposed for use as a method to simplify the complex communication network required for the safe regulation of autonomous vehicle traffic.