Reducing stress and fuel consumption providing road information
Bilah Samping Artikel
ISSN online:
2255-2863
Prefijo Doi:
10.14201
Isi Artikel Utama
Vol. 3 No. 4 (2014), Articles, pages 35-47
Abstract
In this paper, we propose a solution to reduce the stress level of the driver, minimize fuel consumption and improve safety. The system analyzes the driving style and the driver’s workload during the trip while driving. If it discovers an area where the stress increases and the driving style is not appropriate from the point of view of energy efficiency and safety for a particular driver, the location of this area is saved in a shared database. On the other hand, the implemented solution warns a particular user when approaching a region where the driving is difficult (high fuel consumption and stress) using the shared database based on previous recorded knowledge of similar drivers in that area. In this case, the proposal provides an optimal deceleration profile if the vehicle speed is not adequate. Therefore, he or she may adjust the vehicle speed with both a positive impact on the driver workload and fuel consumption. The Data Envelopment Analysis algorithm is used to estimate the efficiency of driving and the driver’s workload in in each area. We employ this method because there is no preconceived form on the data in order to calculate the efficiency and stress level. A validation experiment has been conducted using both a driving simulator and a real environment with 12 participants who made 168 driving tests. The system reduced the slowdowns (38%), heart rate (4.70%), and fuel consumption (12.41%) in the real environment. The proposed solution is implemented on Android mobile devices and does not require the installation of infrastructure on the road. It can be installed on any model of vehicle.
Keywords:
Intelligent Transport System, Fuel Consumption Optimization, Data Envelopment Analysis (DEA), Driving Assistant, Android, Applications, Mobile Computing
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References
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Ben Dhaou, I. (2011). Fuel estimation model for Eco-Driving and Eco-Routing. IEEE Intelligent Vehicles Symposium IV, 37-42. doi:10.1109/IVS.2011.5940399
Changxu, W., & Yili, L. (2007). Queuing network modeling of driver workload and performance. IEEE Transactions On Intelligent Transportation Systems, 8(3), 528-537. doi:10.1109/TITS.2007.903443
Charnes, A., Cooper, W., & Rhodes, E. (1978). Measuring the Efficiency of Decision Making Units. European Journal of Operational Research, 2(6), 429-444. doi:10.1016/0377-2217(78)90138-8
Charnes, A., Cooper, W., Golany, B., & Seiford, L. (1985). Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. Journal of Econometrics, 30(1-2), 91-107. doi:10.1016/0304-4076(85)90133-2
Dong, Y., Hu, Z., Uchimura, K., & Murayana, N. (2011). Driver inattention monitoring system for intelligent vehicles: A review. IEEE Transactions on Intelligent Transportation Systems, 12(2), 596–614. doi:10.1109/TITS.2010.2092770
Engströma, J., Johanssona, E., & Östlundb, J. (2005). Effects of visual and cognitive load in real and simulated motorway driving. Transportation Research Part F: Traffic Psychology and Behaviour, 8(2), 97–120. doi:10.1016/j.trf.2005.04.012
Frith, W., & Cenek, P. (2012). AA Research: Standard Metrics for Transport and Driver Safety and Fuel Economy. Opus International Consultants Central Laboratories.
Garmin. (9 de 1 de 2015). Garmin HUB. Obtenido de https://buy.garmin.com/en-US/US/prod155059.html
Godavarty, S., Broyles, S., & Parten, M. (2000). Interfacing to the on-board diagnostic system. IEEE-VTS Fall VTC 2000, 4, págs. 2000-2004. doi:10.1109/VETECF.2000.886162
Google. (9 de 01 de 2015). Google Glass. Obtenido de http://www.google.com/glass/start
Itoh, M., Kawakita, E., & Oguri, K. (2010). “National motor vehicle crash causation survey,” Washington, DC, USA, Tech. Rep. DOT HS 811 059, Jul. ]. 17th ITS World Congress, (págs. 1-11). Busan, South Korea.
Ji, Q., Zhu, Z., & Lan, P. (2004). Real-time non-intrusive monitoring and prediction of driver fatigue. 53(4), 1052–1068. doi:10.1109/TVT.2004.830974
Kim, J. H., Kim, Y. S., & Lee, W. S. (2011). Real-time monitoring of driver’s cognitive distraction. Spring Conf. Korean Soc. Autom. Eng., (págs. 1197–1202).
Nesamani, K., & Subramanian, K. (2011). Development of a driving cycle for intra-city buses in Chennai. Atmospheric Environment, 45(31), 5469–5476. doi:10.1016/j.atmosenv.2011.06.067
OBDLink. (9 de 1 de 2015). OBDLink ScanTool. Obtenido de http://www.scantool.net
Peissner, M., Doebler, V., & Metze, F. (2011). Can voice interaction help reducing the level of distraction and prevent accidents? Meta-Study on Driver Distraction and Voice Interaction. White Paper.
Riener, A., Ferscha, A., Frech, P., Hackl, M., & Kaltenberger, M. (2010). Subliminal vibro-tactile based notification of CO2 economy while driving. 2nd International Conference on Automotive User Interfaces and Interactive Vehicular Applications AutomotiveUI, (págs. 92-101). Pittsburgh, Pennsylvania, USA.
Sega, S., Iwasaki, H., Hiraishi, H., & Mizoguchi, F. (2011). Verification of Driving Workload Using Vehicle Signal Data for Distraction-Minimized Systems on ITS. 18th ITS World Congress, (págs. 1-12). Orlando Florida.
Simulator, O. D. (25 de 9 de 2015). OpenDS Driving Simulator. Obtenido de http://www.opends.de
Teh, E. T., Jamson, S., & Carsten, O. (2012). How does a lane change performed by a neigh-boring vehicle affect driver workload? 19th ITS World Congress, (págs. 1-8). Vienna , Austria.
Transportation, U. D. (2008). National motor vehicle crash causation survey.
Young, K., & Regan, M. (2007). Driver distraction: A review of the literature. NSW: Australasian College of Road Safety.
Zhang, Y., Owechko, Y., & Zhang, J. (2008). Learning-Based Driver Workload Estimation. En Computational Intelligence in Automotive Applications (págs. 1-17). doi:10.1007/978-3-540-79257-4_1