All Title Author
Keywords Abstract


You Take Care of the Drive, I Take Care of the Rule: A Traffic-Rule Awareness System Using Vehicular Sensors and Mobile Phones

DOI: 10.1155/2012/319276

Full-Text   Cite this paper   Add to My Lib

Abstract:

Traffic rules are used to regulate drivers’ behaviours in modern traffic systems. In fact, all driving behaviours are presented by vehicles’ behaviours. If vehicles have awareness of their behaviours, it is possible that traffic rules are able to regulate vehicles instead of drivers. There are three advantages of vehicle regulation: (1) without worrying about violations of traffic rules and searching for traffic signs, drivers can pay more attention on emergency situations, such as jaywalking. (2) Many traffic violations are due to attention distraction; machines do not have the attention issues; therefore they can provide good traffic-rule obeying. (3) New traffic rules can be spread and applied more quickly and effectively through the Internet or Vehicular Ad hoc NETworks (VANETs). In this paper, we propose a novel traffic-rule awareness system using vehicular sensors and mobile phones. It translates traffic rules into combinations of vehicular sensors, GPS device, and Geography Information System (GIS); the system can tell whether a driver violates the traffic rules and help him to amend his driving behaviour immediately. Experiments in real driving environments show that our system can be aware of the traffic rules accurately and immediately. 1. Introduction During the year 2011, there were 11856 traffic accidents that happened in Sichuan province of China and 95% of the accidents are caused by traffic violations [1]. If traffic-violation rate can be significantly decreased, a lot of lives can be saved from traffic accidents. In fact, most traffic violations are not on purpose; the reason of high traffic-violation rate is that the traffic rules are designed to regulate drivers’ behaviours. As long as drivers are human beings, they will suffer from memory issues and attention distraction. Their memory and concentration will be severely affected by mood, alcohol, drugs, and environment. Even a short conversation during driving will distract drivers and cause unnecessary traffic violations. Google self-driving car [2] is a good attempt to decrease the traffic-violation rate, because machines do not have the memory and concentration issues like humans. However, google self-driving car only focuses on self-driving; it has not yet taken traffic rules into consideration except for traffic lights. Some other works like [3] monitor dangerous driving behaviours like aggressive turns, acceleration, and braking and help drivers to correct these unsafe behaviours. However, they did not take traffic rules into consideration. Actually, even if driving behaviours

References

[1]  Sichuan traffic accidents statistics, http://www.sc122.gov.cn/system/2012/02/01/013433326.shtml.
[2]  How google self-driving car works, http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/how-google-self-driving-car-works.
[3]  D. A. Johnson and M. M. Trivedi, “Driving style recognition using a smartphone as a sensor platform,” in Proceedings of the 14th International IEEE Conference on Intelligent Transportation Systems, Washington, DC, USA, 2011.
[4]  On board diagnostics parameter ids, http://en.wikipedia.org/wiki/Table of OBD-II_Codes#Bitwise_encoded_PIDs.
[5]  On board diagnostics, http://en.wikipedia.org/wiki/On-board-diagnostics.
[6]  Elm327 diagnostic scanner, http://www.tmart.com/OBD-Diagnostics/.
[7]  Android sensor coordinate system, http://developer.android.com/reference/android/hardware/SensorEvent.html.
[8]  Orientate sensor, http://developer.android.com/guide/topics/sensors/sensors_position.html.
[9]  D. Mitrovi?, “Reliable method for driving events recognition,” IEEE Transactions on Intelligent Transportation Systems, vol. 6, no. 2, pp. 198–205, 2005.
[10]  G. A. ten Holt, M. J. Reinders, and E. A. Hendriks, “Multidimensional dynamic time warping for gesture recognition,” in Proceedings of the 13th Annual Conference of the Advanced School for Computing and Imaging, 2007.
[11]  R. Muscillo, S. Conforto, M. Schmid, P. Caselli, and T. D'Alessio, “Classification of motor activities through derivative dynamic time warping applied on accelerometer data,” Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '07), vol. 2007, pp. 4930–4933, 2007.
[12]  2002 chevrolet corvette obd-ii codes, http://www.obd-codes.com/trouble_codes/chevrolet/2002-corvette-obd-ii-codes.php.
[13]  S. Sen, R. R. Choudhury, and S. Nelakuditi, “CSMA/CN: carrier sense multiple access with collision notification,” in Proceedings of the 16th Annual Conference on Mobile Computing and Networking (MobiCom '10), pp. 25–36, September 2010.
[14]  S. Y. Cheng and M. M. Trivedi, “Turn-intent analysis using body pose for intelligent driver assistance,” IEEE Pervasive Computing, vol. 5, no. 4, pp. 28–37, 2006.
[15]  E. Murphy-Chutorian, A. Doshi, and M. M. Trivedi, “Head pose estimation for driver assistance systems: a robust algorithm and experimental evaluation,” in Proceedings of the 10th International IEEE Conference on Intelligent Transportation Systems (ITSC '07), pp. 709–714, October 2007.
[16]  C. Tran and M. M. Trivedi, “Towards a vision-based system exploring 3D driver posture dynamics for driver assistance: issues and possibilities,” in Proceedings of the IEEE Intelligent Vehicles Symposium (IV '10), pp. 179–184, June 2010.
[17]  J. C. McCall and M. M. Trivedi, “Driver behavior and situation aware brake assistance for intelligent vehicles,” Proceedings of the IEEE, vol. 95, no. 2, pp. 374–387, 2007.
[18]  J. Dai, J. Teng, X. Bai, Z. Shen, and D. Xuan, “Mobile phone based drunk driving detection,” in Proceedings of the 4th International Conference on Pervasive Computing Technologies for Healthcare (Pervasive Health '10), March 2010.
[19]  G. A. ten Holt, M. J. Reinders, and E. A. Hendriks, “Multidimensional dynamic time warping for gesture recognition,” in Proceedings of the 13th annual conference of the Advanced School for Computing and Imaging, 2007.
[20]  R. Muscillo, S. Conforto, M. Schmid, P. Caselli, and T. D'Alessio, “Classification of motor activities through derivative dynamic time warping applied on accelerometer data,” Proceedings of the 29th Annual International Conference of the IEEE, vol. 2007, pp. 4930–4933, 2007.
[21]  E. Koukoumidis, L. S. Peh, and M. R. Martonosi, “SignalGuru: leveraging mobile phones for collaborative traffic signal schedule advisory,” in Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services (MobiSys '11), pp. 127–140, ACM, New York, NY, USA, July 2011.

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

微信:OALib Journal