Open Access
Subscription Access
Advanced Driver Assistant System
Subscribe/Renew Journal
Road accidents are the most terrifying thing that can happen to a driver. Worst of all, we refuse to learn from our mistakes along the way. The majority of road users are aware of the general rules and safety measures to take while on the road but injuries and crashes are caused by the negligence of road users. The most common cause of accidents and collision is human error. The aim of this project is to automate and improve the safety of vehicles. Lane Departure Warning System (LDWS) and Emergency Driver Assist System (EDAS) are described in this paper. The LDWS uses camera to track lane markers to see if the driver is drifting accidentally. In this project, whenever the vehicle is moving out of the lane, the device gives the driver a warning in the form of audio or visual signal. Whenever the driver's attention deviates from driving activity for a particular interval of time, EDAS alerts the driver.
Keywords
ADAS, EDAS, Lane Detection, LDWS, OpenCV, Sliding Window, TensorFlow.
Manuscript Received : May 24, 2021 ; Revised : June 24, 2021 ; Accepted : July 7, 2021. Date of Publication : August 5, 2021.
User
Subscription
Login to verify subscription
Font Size
Information
- P. R. Nagrale and V. P. Kshirsagar, “Lane detection with lane departure warning system,” Int. J. of Scientific Develop. and Res., vol. 4, no. 8, pp. 14–17, 2019. [Online]. Available: https://www.ijsdr.org/papers/IJSDR1908003.pdf
- F. Habeeb, K. N. Kunan and N. Tuturaja, “A study on lane departure warning system and object detection on roads for forward collision avoidance,” Int. J. of Innovative Res. in Elect., Electron., Instrumentation and Control Eng., vol. 6, no. 5, pp. 51–54, 2018. [Online]. Available:https://ijireeice.com/wp-content/uploads/2018/05/IJIREEICE-11.pdf
- M. J. Flores, J. M. Armingol, and A. Escalera, “Real-time drowsiness detection system for an intelligent vehicle,” 2008 IEEE Intelligent Vehicles Symp., vol. 2008, pp. 637–642, June 2008. [Online]. Available: https://doi.org/10.1109/IVS.2008.4621125
- N. Alioua, A. Amine, and M. Rziza “Driver’s fatigue detection based on yawning extraction,” Int. J. of Veh. Technol., vol. 2014, pp. 1–7. [Online]. Available: https://doi.org/10.1155/2014/678786
- B. Akrout and W. Mahdi, “Visual based approach for drowsiness detection,” vol. 2013, pp. 1324–1329, IEEE Intelligent Vehicles Symp,. June 2013. [Online]. Available: https://doi.org/10.1109/IVS.2013.6629650
Abstract Views: 520
PDF Views: 0