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Driving Risk Rating for Driver Monitoring Based on Satellite Data

机译:基于卫星数据的驾驶员监控驾驶风险评级

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According to the recent study, Thailand has the 2nd most dangerous road in the world. Based on many researches, the driver is the main influencers of the traffic fatalities. Since the more dangerous the driver drive, the more chance of accident become. Therefore, driver’s monitoring system become one of the solutions that acceptable and reliable, especially for fleet management and public transportation.This paper’s goal is to find an algorithm that can distinguish driving behaviour based on cars’ acceleration and velocity, calling it as Risk Driving Score (RDS). The algorithm was tested by driving test by volunteers on highways with observers, who were told to rank the drivers in terms of driving risk from the 1-5 point. Meanwhile, the drivers were asked to drive in 3 different styles, normal, safety, and hurry. All drives were recorded by satellite and video data then filtered and used for the algorithm calculation. After that, the linear regression shows that there is a trend of driving score evaluated by algorithm and observers in term of linear equation with high correlation. In conclusion, the algorithm can replace the observers in driver monitoring method and can be used with satellite data.
机译:根据最近的一项研究,泰国拥有世界上第二条最危险的道路。根据许多研究,司机是交通事故的主要影响因素。由于司机驱动器越危险,事故的可能性越大。因此,驾驶员的监控系统成为可接受可靠的解决方案之一,特别是舰队管理和公共交通。本文的目标是找到一种可以基于汽车的加速和速度来区分驾驶行为的算法,称为风险驾驶分数(RDS)。该算法通过与观察者的高速公路上的志愿者进行测试来测试,他被告知在从1-5点开始驾驶风险方面的驾驶员。同时,司机被要求推动3种不同的款式,正常,安全和急忙。所有驱动器都被卫星和视频数据记录,然后过滤并用于算法计算。之后,线性回归表明,在具有高相关的线性方程项中,通过算法和观察者评估的驾驶评分趋势。总之,该算法可以取代驾驶员监测方法中的观察者,可以与卫星数据一起使用。

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