Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:6 Issue:3 Year: 18 April,2017 Pages:1362-1369
It is conceivable to determine the state of the road from the driving experience of the vehicle as it covers an extent of it. This data when communicated can be made accessible to the future users of that road, so all around educated choices can be made about changing the speed or maintaining a strategic distance from deceptive streets even though this data is pointless to the vehicle that produced it. This paper displays the TOC framework which plans to record a vehicle's experience while at the same time driving that specific extent of the road. The information gathered from the vehicle is broke down and made accessible to the future clients of the street consequently effectively Transforming Obsolete information into Contemporary information. The primary subject of this paper will be one of the two calculations utilized by the TOC framework to examine the information produced by the vehicles. This algorithm is utilized to collect useful vehicular information from the rest.
vibration sensor, rash driving behaviour, terrain information
#1. Shilpa Garg, Pushpendra Singh, Parameswaran Ramanathan, Rijurekha Sen, "Vividhavahana: smartphone based vehicle classification and its applications in developing region" , Mobiquitous, London, Great Britain, 2014. #2. Jakob Eriksson, Lewis Girod, Bret Hull, Ryan Newton, Samuel Madden , Hari Balakrishnan, "The pothole patrol: using mobile sensor network for road surface monitoring", MobiSys'08, Breckenridge, Colorado, USA, 2008. #3. Pushpendra Singh, Nikita Juneja, Shruti Kapoor, "Using mobile phone sensors to detect driving behaviour", Dev'13, Bangalore, India, 2013 #4. Ki-Yong Lee,, Jeong-Min Park,, Joon-Woong Lee “Estimation of longitudinal profile of road surface from stereo disparity using Dijkstra algorithm” Robotics and Automation, International Journal of Control, Automation and Systems August 2014, Volume 12, Issue 4, pp 895-903. #5. Andreas Wedel, Hernan Badino, Clemens Rabe, Heidi Loose, Uwe Franke, and Daniel Cremers, Member, IEEE “B-Spline Modeling of Road Surfaces With an Application to Free-Space Estimation” IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 10, NO. 4, DECEMBER 2009 #6. R. Labayrade, D. Aubert, and J. P. Tarel, “Real time obstacle detection in stereovision on non flat road geometry through “V-disparity” representation,” Proc. IEEE Intelligent Vehicles Symposium, pp. 646– 651, 2002H. #7. J. Weber, D. Koller, Q.-T. Luong, and J. Malik, “An integrated stereobased approach to automatic vehicle guidance,” in Proc. 5th Int. Conf.Comput. Vis. , 1995, pp. 52–57. #8. R. Labayrade, D. Aubert, and J.-P. Tarel, “Real time obstacle detection on non flat road geometry through v-disparity representation,” inProc. IEEE Intell. Veh. Symp. , Versailles, France, 2002, pp. 646–651.[Online]. Available: http://perso.lcpc.fr/tarel.jean-philippe/iv02.html #9. R. Labayrade and D. Aubert, “A single framework for vehicle roll, pitch, yaw estimation and obstacles detection by stereovision,” in Proc. IEEE Intell. Veh. Symp. , Columbus, OH, Jun. 2003, pp. 31–36. #10. N. Suganuma and N. Fujiwara, “An obstacle extraction method using virtual disparity image,” in Proc. IEEE Intell. Veh. Symp. , Istanbul, Turkey, Jun. 2007, pp. 456–461. #11. F. Oniga, S. Nedevschi, M. Meinecke, and T. Binh, “Road surface and obstacle detection based on elevation maps from dense stereo,” in Proc. IEEE Intell. Trans. Syst. , Seattle, WA, 2007, pp. 859–865.