Cooperative Adaptive Driving for Platooning Autonomous Self Driving Based on Edge Computing

Ben Jye Chang, Ren Hung Hwang*, Yueh Lin Tsai, Bo Han Yu, Ying Hsin Liang


研究成果: Article同行評審

6 引文 斯高帕斯(Scopus)


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 encountered while exhibiting asynchronous brakes that easily lead to backward shock-waves and are difficult to remove. Several critical issues should be addressed in CACC, including (i) difficulties with adaptive steering of the inter-vehicle distances among neighbor vehicles and the vehicle speed, (ii) the butterfly effect, (iii) unstable vehicle traffic flow, etc. To address the above issues in CACC, this paper proposes the mobile edge computing-based vehicular cloud of the cooperative adaptive driving (CAD) approach to avoid shock-waves efficiently in platoon driving. Numerical results demonstrate that the CAD approach outperforms the compared techniques in the number of shock-waves, average vehicle velocity, average travel time and time to collision (TTC). Additionally, the adaptive platoon length is determined according to the traffic information gathered from the global and local clouds.

頁(從 - 到)213-225
期刊International Journal of Applied Mathematics and Computer Science
出版狀態Published - 1 6月 2019


深入研究「Cooperative Adaptive Driving for Platooning Autonomous Self Driving Based on Edge Computing」主題。共同形成了獨特的指紋。