Thematic Regional Paper: Latin America

human development, climate change

Eco-driving: energy saving based on driver behavior

Ponencia presentada en: XVI Jornadas de ARCA sobre Sistemas Cualitativos y sus Aplicaciones en Diagnosis, Robótica e Inteligencia Ambiental (JARCA 2014), celebrado los días del 24/06/2014 a 27/06/2014, en Rota, Cádiz (España)The number of vehicles has grown in recent years. As a result, it has increased the fuel consumption and the emission of gaseous pollutants. The emission of gaseous pollutants causes more deaths than traffic accidents. On the other hand, the energy resources are limited and the increase in demand causes them even more expensive. In addition, the percentage of old vehicles is very high. Eco-driving is a good solution in order to minimize the fuel consumption because it is independent of the vehicle age. In this paper, a driving assistant is presented. This solution allows the user acquires knowledge about eco-driving. Unlike other solutions, our proposal adapts the recommendations to the user profile. It also provides information in advance such as: optimal average speed, anomalous events, deceleration pattern, and so on. These recommendations prevent that the user performs inefficient actions. In these type of systems, motivation is very important. Drivers lose the interest over time. To solve this problem, we employ gamification techniques that contribute to avoid drivers coming back to their previous driving habitsThe 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.Publicad

The impact of using gamification on the eco-driving learning

The proceeding at: 5th International Symposium on Ambient Intelligence (ISAmI 2014), took is at 2014, June, 4-6, in Salamanca (Spain). The Event Web Site: http://isami.usal.es/isami2014/This paper analyses and validates the impact of using gamification techniques for improving eco-driving learning. The proposal uses game mechanisms such as the score and achievements systems in order to encourage the driver to drive efficiently. The score is calculated using fuzzy logic techniques that allow us to evaluate the driver in a similar way as a human being would do. We also define the eco-driving tips that are issued while driving in order to help the driver to improve the fuel consumption. Every time the system detects an inefficient action of the driver to a previously known situation such as a bad reaction to a detected traffic sign or a detected traffic accident, it warns the user. The proposal is validated using 14 different drivers performing more than 300 drives with 5 different models of vehicles on 4 different regions of Spain. The conclusions show a positive correlation in the use of gamification techniques and the application of the proposed of eco-driving tips, especially for aggressive drivers. Furthermore, these techniques contribute to avoid drivers coming back to their previous driving habits.The research leading to these results has received funding from the ARTEMISA project TIN2009-14378-C02-02 within the Spanish "Plan Nacional de I+D+I and from the IRENE (PT-2012-1036-370000), COMINN (IPT-2012-0883-430000) and REMEDISS (IPT-2012-0882-430000) projects all funded by the Spanish MINECO Ministry.Publicad

LESY-ECO: Learning system for eco-driving based on the imitation

Proceedings of 2014 International Conference on Connected Vehicles and Expo (ICCVE,IEEE), took place 2014, November, 03-07, in Viena (Austria).In this paper, we propose a learning method for eco-driving based on imitation. The system uses Data Envelopment Analysis (DEA) in order to calculate the driving efficiency from the point of view of the fuel consumption. The input and output parameters have been selected taking into account the Longitudinal Vehicle Dynamics Model. This technique allows us to notify the user about who is the most efficient driver close to him or her and to suggest the imitation of the behavior of such driver. The proposed method promotes learning by observation and imitation of efficient drivers in a practical rather than theoretical way such as attending eco-driving lessons. The DEA algorithm does not depend on the definition of a preconceived form of the data in order to calculate the efficiency. The DEA algorithm estimates the inefficiency of a particular DMU by comparing it to similar DMUs considered as efficient. This is very important due to the dynamic nature of the traffic. A validation experiment has been conducted with 10 participants who made 500 driving tests in Spain. The results show that combining eco-driving lessons with the proposed learning system, drivers achieve a very significant improvement on fuel saving (15.82%)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.Publicad

Predicting upcoming values of stress while driving

The levels of stress while driving affect the way we drive and have an impact on the likelihood of having an accident. Different types of sensors, such as heart rate or skin conductivity sensors, have been previously used to measure stress related features. Estimated stress levels could be used to adapt the driver's environment to minimize distractions in high cognitive demanding situations and to promote stress-friendly driving behaviors. The way we drive has an impact on how stressors affect the perceived cognitive demands by drivers, and at the same time, the perceived stress has an impact on the actions taken by the driver. In this paper, we evaluate how effectively upcoming stress levels can be predicted considering current stress levels, current driving behavior, and the shape of the road. We use features, such as the positive kinetic energy and severity of curves on the road to estimate how stress levels will evolve in the next minute. Different machine learning techniques are evaluated and the results for both intra and inter-city driving and for both intra and inter driver data are presented. We have used data from four different drivers with three different car models and a motorbike and more than 220 test drives. Results show that upcoming stress levels can be accurately predicted for a single user ( correlation r = 0.99 and classification accuracy 97.5%) but prediction for different users is more limited ( correlation r = 0.92 and classification accuracy 46.9%).This work was supported in part by HERMES-SMART DRIVER Project through Spanish MINECO under Project TIN2013-46801-C4-2-R, in part by the Ministerio de Educación Cultura y Deporte under Grant PRX15/0003

Considerations for a Research Program on Drought in Mexico

The impacts of drought in Mexico have led water authorities to define a strategy for action to diminish their costs. But the proposed actions at regional levels require a solid scientific basis on the subject to answer several of the questions that decision makers have, not only on the dynamics of climate variability, but on the context that produces hydrological, agricultural and socioeconomic droughts. This work makes some considerations on the research topics and approach to be followed in the scientific Plan for the Program against Drought in Mexico (known as Pronacose), in a risk management context. This approach requires that, in addition to monitoring strategies and the study of processes and prediction on drought, vulnerability be characterized as a dynamic and multifactorial element within the analysis on risk to drought. This approach also requires the collaboration of stakeholders from various sectors and regions, in order to define structural and non-structural measures against drought, such as early warning systems

Artemisa: a personal driving assistant for fuel saving

In this paper, we propose a driving assistant that makes recommendations in order to reduce the fuel consumption. The solution only requires a smartphone and an OBD/Bluetooth device. Eco-driving advices try to avoid situations that cause an increase in the fuel consumption such as inappropriate speed or slow reaction to the detection of traffic signs and traffic incidents. The main contribution of this paper is the use of artificial intelligence techniques in order to issue the eco-driving tips that are best adapted to the user profile, the characteristics of the vehicle, and the road state conditions. This is very important because the driver may lose the interest due to the high requirements that tend to be provided by general use eco-driving assistants. In order to properly assess and validate the proposed solution, it has been implemented on several Android mobile devices and has been validated using a dataset of 2,250 driving tests using three different models of vehicles with 25 different drivers on three distinct routes. The results show that the system reduces the fuel consumption by 11.04 percent on average and even, in certain cases, the fuel saving is greater than 15 percent.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 Economíıa 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

Toward safer highways: predicting driver stress in varying conditions on habitual routes

Driver stress is a growing problem in the transportation industry. It causes a deterioration of cognitive skills, resulting in poor driving and an increase in the likelihood of traffic accidents. Prediction models allow us to avoid or at least minimize the negative consequences of stress. In this article, an algorithm based on deep learning is proposed to predict driver stress. This type of algorithm detects complex relationships among variables. At the same time, it avoids overfitting. The prediction of the upcoming stress level is made by taking into account driving behavior (acceleration, deceleration, speed) and the previous stress level.This work was supported by the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R, funded by the Spanish Ministry of Economy, Industry and Competitiveness, from the grant PRX15/00036 from the Ministerio de Educación Cultura y Deporte, and from a sabbatical leave by the Carlos III University of Madrid.Publicad