Ibrahim, Ahmed M.
(Nile University,Wireless Intelligent Networks Center (WINC),Giza,Egypt,12588)
,
Hassan, Rania M.
(Automaker Company,Valeo Incorporation,Egypt)
,
Tawfiles, Andrew E.
(Institute of Aviation Engineering and Technology,Egypt)
,
Ismail, Tawfik
(Nile University,Wireless Intelligent Networks Center (WINC),Giza,Egypt,12588)
,
Darweesh, M. Saeed
(Nile University,Wireless Intelligent Networks Center (WINC),Giza,Egypt,12588)
This paper aims to help self-driving cars and autonomous vehicles systems to merge with the road environment safely and ensure the reliability of these systems in real life. Crash avoidance is a complex system that depends on many parameters. The forward-collision warning system is simplified into f...
This paper aims to help self-driving cars and autonomous vehicles systems to merge with the road environment safely and ensure the reliability of these systems in real life. Crash avoidance is a complex system that depends on many parameters. The forward-collision warning system is simplified into four main objectives: detecting cars, depth estimation, assigning cars into lanes (lane assign) and tracking technique. The presented work targets the software approach by using YOLO (You Only Look Once), which is a deep learning object detector network to detect cars with an accuracy of up to 93%. Therefore, apply a depth estimation algorithm that uses the output boundary box’s dimensions (width and height) from YOLO. These dimensions used to estimate the distance with an accuracy of 80.4%. In addition, a real-time computer vision algorithm is applied to assign cars into lanes. However, a tracking proposed algorithm is applied to evaluate the speed limit to keep the vehicle safe. Finally, the real-time system achieved for all algorithms with streaming speed 23 FPS (frame per second).
This paper aims to help self-driving cars and autonomous vehicles systems to merge with the road environment safely and ensure the reliability of these systems in real life. Crash avoidance is a complex system that depends on many parameters. The forward-collision warning system is simplified into four main objectives: detecting cars, depth estimation, assigning cars into lanes (lane assign) and tracking technique. The presented work targets the software approach by using YOLO (You Only Look Once), which is a deep learning object detector network to detect cars with an accuracy of up to 93%. Therefore, apply a depth estimation algorithm that uses the output boundary box’s dimensions (width and height) from YOLO. These dimensions used to estimate the distance with an accuracy of 80.4%. In addition, a real-time computer vision algorithm is applied to assign cars into lanes. However, a tracking proposed algorithm is applied to evaluate the speed limit to keep the vehicle safe. Finally, the real-time system achieved for all algorithms with streaming speed 23 FPS (frame per second).
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