en خودروهای خودران Autonomous vehicles پژوهشي Research <span font-size:="" mso-ansi-language:="" mso-ascii-theme-font:="" mso-bidi-language:="" mso-bidi-theme-font:="" mso-fareast-font-family:="" mso-fareast-language:="" mso-fareast-theme-font:="" mso-hansi-theme-font:="" new="" style="color: black; line-height: 115%; font-family: " times="">Today most accidents are caused by drivers&rsquo; fatigue, drowsiness and losing attention on the road ahead. In this paper, a system is introduced, using RGB-D cameras to automatically identify drowsiness and give warning. In this system two important modules have been utilized simultaneously to identify the state of driver&rsquo;s mouth and eyes for detecting drowsiness.<span dir="RTL"> </span>At first, using the depth information, the mouth area and its state are identified. Then using CNN networks, to predict whether the eyes are open or closed, a semi-VGG architecture is used .The results of yawning and eyes states detection are integrated to decide whether an alarm should be issued. The results show an accuracy of about 90% which is encouraging.</span> Driver Drowsiness,Yawning Detection, Deep Learning, Depth Information, Active Contour 3033 3044 http://ase.iust.ac.ir/browse.php?a_code=A-10-402-1&slc_lang=en&sid=1 Mina Zohoorian Yazdi Mina.zjy93@gmail.com 180031947532846002242 180031947532846002242 No Iran University of Science and Technology Mohsen Soryani soryani@iust.ac.ir 180031947532846002243 180031947532846002243 Yes Associate Professor