Sleepiness during driving is a dangerous problem faced by all countries. Many studies have been conducted and stated that sleepiness threatens the driver himself and other peoples. The victim not only suffered minor injuries but also many of them ended in death. Nowadays, there are many kinds of studies to improve sleep detection methods. But it faces difficulties such as lack of accuracy, and poor performance of detection; thus the system inadequate works in real-time. Recently, automobile companies have begun manufacturing special equipment to recognize sleepiness driver. However, the technologies are only implemented in certain cars since the price is still quite expensive. Therefore, a system with a comprehensive method is needed to discover the driver's sleepiness accurately at an affordable price. This study proposed driver sleepiness detection implemented on a smartphone. The system is capable to identify closed eyes using the extraction of Facial Landmark points and analysis of a calculation result of the Eye Aspect Ratio (EAR). The System qualified works in real-time since it uses a particular library designed in a mobile application. Based on some experiments that have been done, the proposed method adequate to identify sleepy drivers accurately by 92.85%.
Road damage produces serious problems for the driver such as travel efficiency, vehicle value, and even driver safety. In some cases, road damage causes accidents and ends in death. Currently, road damage detection research extends to grow and present various approaches such as the implementation of an accelerometer sensor. However, the implementations face lacks of accuracy since unable to work in real-time and poor implementation. In the end, the system inadequate to identify damaged roads effectively. Therefore, a comprehensive study was proposed. Firstly, data collection is conducted by applying a low-pass filter to obtain accurate data. The next step is estimating the range value of the accelerometer graph. In the final step, the classification is performed to identify road conditions into smooth, medium and poor. Based on some experiments that have been done, the proposed method accurately recognizes road conditions by 86.67%.
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