Main Article Content

Víctor Corcoba Magaña
University Carlos III
Mario Muñoz Organero
University Carlos III
Vol. 3 No. 4 (2014), Articles, pages 35-47
Accepted: Oct 5, 2015


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.


Download data is not yet available.

Article Details


Adell, E., Varhelyi, A., & Fontana, M. (2011). The effects of a driver assistance system for safe speed and safe distance: A real-life field study. Transportation Research Part C: Emerging Technologies, 19(1), 145–155. doi:10.1016/j.trc.2010.04.006

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

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

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

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

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