Reducing stress and fuel consumption providing road information

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ISSN online: 2255-2863

Prefijo Doi: 10.14201

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Víctor Corcoba Magaña
University Carlos III
Spain
Mario Muñoz Organero
University Carlos III
Spain
Vol. 3 No. 4 (2014), Articles, pages 35-47
DOI: https://doi.org/10.14201/ADCAIJ2014343649
Accepted: Oct 5, 2015
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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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