<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

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

Department of Computer Science and Information Engineering

National Yunlin University of Science and Technology

Department of Multimedia Animation and Application

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Cooperative adaptive cruise control (CACC) for human and autonomous self-driving aims to achieve active safe driving that avoids vehicle accidents or traffic...

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

Online veröffentlicht: 04. Juli 2019

Seitenbereich: 213 - 225

Eingereicht: 31. Juli 2018

Akzeptiert: 01. Feb. 2019

DOI: https://doi.org/10.2478/amcs-2019-0016

Schlüsselwörter
mobile edge computing, active safe driving, cooperative platoon driving, cooperative adaptive cruise control

© 2019 Ben-Jye Chang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Online veröffentlicht: 04. Juli 2019

Seitenbereich: 213 - 225

Eingereicht: 31. Juli 2018

Akzeptiert: 01. Feb. 2019

DOI: https://doi.org/10.2478/amcs-2019-0016

Schlüsselwörtermobile edge computing, active safe driving, cooperative platoon driving, cooperative adaptive cruise control

© 2019 Ben-Jye Chang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
mobile edge computing, active safe driving, cooperative platoon driving, cooperative adaptive cruise control