Platoon-Based Cooperative Adaptive Cruise Control for Achieving Active Safe Driving Through Mobile Vehicular Cloud Computing

The cooperative adaptive cruise control (CACC) aims to achieve active safe driving that avoids vehicle accidents or a 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. Thus, the driving stability is degraded significantly by backward shockwaves and affects the safe driving performance in CACC. 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 thus proposes the cooperative adaptive driving (CAD) approach that consists of three contributions: cooperative vehicle platooning (CVP), shockwave-avoidance driving (SAD), and adaptive platoon synchronization (APS). First, a platoon-based cooperative driving among neighbor vehicles is proposed in CVP. Second, in SAD, the predictive shockwave detection is proposed to avoid shockwaves efficiently. Third, based on the traffic states, APS determines the adaptive platoon length and velocity for achieving synchronous control and reduces the butterfly effect when vehicles suddenly brake. Numerical results demonstrate that the proposed CAD approach outperforms the compared approaches in number of shockwaves, average affection range 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.