dc.description.abstract | According to the World Health Organization (WHO). Road traffic accidents, the leading cause of death by injury and the tenth-leading cause of all deaths globally. An estimated 1.2 million people are killed in road crashes each year, and as many as 50 million are injured. At the same time, the economic costs derived from traffic accidents are quite high, also the gross domestic product (GDP) of the world is 3% lost due to traffic accidents each year. It is easily to tell that traffic accidents will not only bring the injured but also affect global economy.
Risk driving behavior is easy to cause traffic accidents. In order to identify risk behaviors, it is necessary to construct a driving behavior model. In the past, many teams have already collected risk driving behaviors in a real driving environment and build risk driving behavior models for verification. Collecting risk driving behaviors in real environment cost extremely high, in maintenance, and we believe that there is still considerable risk in collecting risk driving behaviors at any locations.
In the past research, a novel driver identity verification system based on Gaussian Mixture Model’s driving behavior was proposed. The system constructs a simulated driving environment which collects the driver′s wrist behavior data from a smartwatch. Due to the smartwatch based, it has the advantages of constructing simulation environment with low cost and strong portability also lots of risk driving behaviors are directly related to the wrist movements. These factors make it more suitable for collecting risk driving behaviors in this environment.
However, no research has clearly pointed out the feasibility of using the simulated driving behavior prediction model to identify real driving behavior at present. Moreover, there are no precedents for effectively utilizing the simulated dangerous driving behavior model. Therefore, we expect this study to achieve: In other applications such as driver identification, the simulated driving behavior model can completely replace the real driving behavior model. With this concept, for all driving behaviors that do not conform to the normal road driving principles, such as: fatigue driving, drunk driving and other dangerous driving behavior, we can collect these dangerous driving behavior data through the simulated environment and apply it to identify dangerous driving behaviors in the real environment, thus providing a safer and more reliable dangerous driving discriminating mechanism in the market.
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