Latest revision as of 12:33, 25 January 2021

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. The research leading to these results has received funding from the “HERMES-SMART DRIVER” project TIN2013- 46801-C4-2-R within the Spanish "Plan Nacional de I+D+I" under the Spanish Ministerio de Economía y Competitividad and from the Spanish Ministerio de Economía y Competitividad funded projects (co-financed by the Fondo Europeo de Desarrollo Regional (FEDER)) IRENE (PT-2012-1036-370000), COMINN (IPT-2012-0883-430000) and REMEDISS (IPT-2012-0882-430000) within the INNPACTO program.

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• http://dx.doi.org/10.14201/adcaij2014343649 under the license https://creativecommons.org/licenses/by-nc-nd

• http://hdl.handle.net/10016/21811

• http://revistas.usal.es/index.php/2255-2863/article/download/ADCAIJ2014343547/13647

• https://e-archivo.uc3m.es/bitstream/10016/25506/3/reducing_ADCAIJ_2014.pdf under the license cc-by-nc-nd

• http://hdl.handle.net/10016/25506,

https://doi.org/10.14201/ADCAIJ2014343649 under the license cc-by-nc-nd

• http://link.springer.com/content/pdf/10.1007/978-3-319-19695-4_3,

http://dx.doi.org/10.1007/978-3-319-19695-4_3 under the license http://creativecommons.org/licenses/by-nc-nd/3.0/es/

• https://link.springer.com/chapter/10.1007/978-3-319-19695-4_3,

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https://academic.microsoft.com/#/detail/1824152895

• https://revistas.usal.es/index.php/2255-2863/article/view/13272,

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DOIS: 10.1007/978-3-319-19695-4_3 10.14201/adcaij2014343649