Mobile Edge Computing-Based Vehicular Cloud of Cooperative Adaptive Driving for Platooning Autonomous Self Driving

Abstract

The Cooperative Adaptive Cruise Control (CACC) for Human and Autonomous Self-Driving aims to achieve active safe driving that avoids vehicle accidents or traffic jam by exchanging the road traffic information (e.g., traffic flow, traffic density, velocity variation, etc.) among neighbor vehicles. However, in CACC, the butterfly effect is happened while exhibiting asynchronous brakes that easily lead to backward shockwaves and difficult to be removed. Several critical issues should be addressed in CACC, including: 1) difficult to adaptively control the inter-vehicle distances among neighbor vehicles and the vehicle speed, 2) suffering from the butterfly effect, 3) unstable vehicle traffic flow, etc. For addressing above issues in CACC, this paper proposes the Mobile Edge Computing-based vehicular cloud of Cooperative Adaptive Driving (CAD) approach to avoid shockwaves efficiently in platoon driving. Numerical results demonstrate that CAD approach outperforms the compared approaches in number of shockwaves, average vehicle velocity, and average travel time. Additionally, the adaptive platoon length is determined according to the traffic information gathered from the global and local clouds.