Intelligent Sensors for Real-Time Hazard Detection and Visual Indication on Highways
Abstract
Traffic collisions, in particular high speed car accidents often result in huge damages, long traffic queues and loss of human lives. In this work we present an intelligent modular system that monitors traffic in highways and alerts drivers of sudden stops, in poor visual conditions. The system is composed of several identical modules, to be placed in the middle of a highway’s lane, that sense the lights and communicate their presence and velocity to their neighbor modules via RF. With such information, the nearby modules estimate the velocity of the passing cars. When the module ahead detects a car passing at a much slower speed than what was previously estimated, it alerts the other modules, so they produce a visual indication for the oncoming drivers, preventing accidents. The system operates autonomously using solar energy harvesting.
Keywords
Full Text:
PDFReferences
Handbook of Transport Systems and Traffic Control, Volume 3. Emerald, Inc., 2010.
European Commission. Statistics on accidents data. http://ec.europa.eu/transport/road_safety/specialist/statistics/index_en.htm, 2016.
Texas Instruments. MSP430FR5969. http://www.ti.com/product/msp430fr5969.
Guillaume Leduc. Road traffic data: Collection methods and applications. Technical report, JRC - European Commision, 2008.
Filipe Palhinha, Duarte Carona, António Serrador, and Tomás Canas. Wireless magnetic based sensor system for vehicles classification. Procedia Technology, 17(0):632 – 639, 2014. Conference on Electronics, Telecommunications and Computers (CETC 2013).
RadixTraffic. Idu600 - daytime dimming photocell. http://radixtraffic.co.uk/assets/uploads/ IDU600.pdf, 2013.
S. Sadeky, A. Al-Hamadiy, B. Michaelisy, and U. Sayed. Real-time automatic traffic accident recognition using hfg. In Pattern Recognition (ICPR), 2010 20th International Conference on, pages 3348–3351, Aug 2010.
H.M. Sherif, M.A. Shedid, and S.A. Senbel. Real time traffic accident detection system using wireless sensor network. In Soft Computing and Pattern Recognition (SoCPaR), 2014 6th International Conference of, pages 59–64, Aug 2014.
Jules White, Chris Thompson, Hamilton Turner, Brian Dougherty, and DouglasC. Schmidt. Wreckwatch: Automatic traffic accident detection and notification with smartphones. Mobile Networks and Applications, 16(3):285–303, 2011.
Kimin Yun, Hawook Jeong, Kwang Moo Yi, Soo Wan Kim, and Jin Young Choi. Motion interaction field for accident detection in traffic surveillance video. In Pattern Recognition (ICPR), 2014 22nd International Conference on, pages 3062–3067, Aug 2014.
DOI: http://dx.doi.org/10.34629/ipl.isel.i-ETC.29
Refbacks
- There are currently no refbacks.
Copyright (c) 2017 Rui Duarte
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.