Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1942
Title: EyeDriver: Intelligent Driver Assistance System
Authors: Gayadeeptha, P
Baddewithana, T. P
Pannegama, K. V
Samarakkody, C. S
Samarasinghe, P
Siriwardana, S
Keywords: EyeDriver
Intelligent Driver
Assistance System
Issue Date: 18-Dec-2019
Publisher: IEEE
Citation: P. Gayadeeptha, T. P. Baddewithana, K. V. Pannegama, C. S. Samarakkody, P. Samarasinghe and S. Siriwardana, "EyeDriver: Intelligent Driver Assistance System," 2019 14th Conference on Industrial and Information Systems (ICIIS), 2019, pp. 272-277, doi: 10.1109/ICIIS47346.2019.9063273.
Series/Report no.: 2019 14th Conference on Industrial and Information Systems (ICIIS);Pages 272-277
Abstract: “EyeDriver” is a driver assistance system that analyzes and provides real-time driver assistant data from four separate components. These main components are drowsiness detection and head pose estimation, over-speed detection, lane departure, and front collision avoidance. It is a compact product that included a Raspberry pi board, a USB camera module, Pi camera, and a TFT LCD. Since the “EyeDriver” is a first affordable aftermarket solution in Sri Lanka, it can be mounted and configured in any vehicle without any professional knowledge in less effort. Drowsiness detection and head pose estimation component will monitor the driver's eyes and keep track of whether the driver's head's position is inconsistent or deviated from the optimal position. In accordance with the road's recommended speed, the vehicle's actual speed is analyzed and if it is more than the permitted, the system makes a notification. It is done by the over-speed detection component. Lane departure component consists of assisting in keeping the vehicle stable on the desired lane on the road. Also, when the driver makes an intended lane change, the system provides a notification. The Front collision avoidance part will detect the frontal obstacle on the road and provide pre-collision/proximity warning notification. The notification makes according to the vehicle speed and distance between the object and the vehicles. The whole system is based on the Raspberry Pi 3 Model B+ board and the implementation of the system has been done by using OpenCV and Python.
URI: http://rda.sliit.lk/handle/123456789/1942
ISSN: 2164-7011
Appears in Collections:Department of Information Technology-Scopes
Research Publications -Dept of Information Technology

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