Abstract—As the technologies for vehicular safety grow and mature, several advanced driving assisting systems are deployed to mitigate negative impacts on driving safety and protect drivers and passengers efficiently. However, the rate of fatalities and serious injuries in traffic accidents is still high. Based on the statistics from the Ministry of the Interior and National Highway Police Bureau in Taiwan, most critical accidents are caused by the circumstance that drivers do not aware the potential danger to their surroundings. Currently, several driving assisting systems deployed in vehicles can sense surrounding information for potential accidents prevention in time. In order to prevent potential accidents in advance for safer driving, a cloud-assisted safe driving framework is proposed in this paper. The framework leverages sensing devices in vehicles to acquire particular information of the front vehicles. The information will be acquired, recognized, and analyzed based on historical data preserved in the cloud. In addition, the information and comments on social network sites based on the recognized information will be collected and analyzed. Based on historical data and social information, a reputation for the recognized vehicle will be calculated in the cloud. Thus, corresponding suggestion based on the reputation is given to the drivers to perform defensive driving if necessary. Through the cloud-assisted safe driving framework, the driving safety can be achieved in advance efficiently.
Index Terms—Cloud computing, crowd sensing, reputation, driving safety.
Chia Hung Kao is with the Department of Applied Mathematics, National Taitung University, Taiwan (e-mail: chkao@nttu.edu.tw).
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Cite: Chia Hung Kao, "Cloud-Assisted Safe Driving Framework," International Journal of Modeling and Optimization vol. 7, no. 6, pp. 346-351, 2017.
