Issue |
MATEC Web Conf.
Volume 77, 2016
2016 3rd International Conference on Mechanics and Mechatronics Research (ICMMR 2016)
|
|
---|---|---|
Article Number | 09006 | |
Number of page(s) | 4 | |
Section | Artificial Intelligence | |
DOI | https://doi.org/10.1051/matecconf/20167709006 | |
Published online | 03 October 2016 |
Intelligent Smartphone based system for detecting speed bumps and reducing car speed
Computer Engineering Department, College of Engineering and Technology, Palestine Technical University-Kadoorie, Palestine
Although speed bumps are used to force drivers reduce car speed for avoiding accidents, these bumps may cause car crash or accident when drivers do not notice them. Studies have proposed different methods to detect bumps and alert drivers. However, these methods have limitations and require modifications to enable accurate detection. Also these methods did not propose speed reduction approaches. Therefore, in this research, we propose a method that utilizes smartphone microelectronic mechanical technology for speed bump detection. The system uses the gravity sensor to detect the vertical vibration of cars passes over bumps and the GPS to determine the position of the bump. To give accurate detection results, data are collected from crowd, stored and processed on the cloud. The system also contains a speed reduction unit which is attached to the brake pedal and reduces the speed if a bump is detected. A small scale experiment showed that the system detected the position and the height of bumps with a very small error. The system also reduced the speed of cars at the moment they hit the bumps to a point that does not cause any harm to cars or passengers.
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.