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

Eco-driving: ahorro de energía basado en el comportamiento del conductor

El crecimiento del número de vehículos en circulación ha experimentado un fuerte aumento en los últimos 20 años. La generalización del uso del automóvil ha tenido efectos muy positivos en la economía de los países. Sin embargo, también ha provocado grandes problemas debido la contaminación y a la cantidad de energía que consumen. Por otra parte, la mayoría de los vehículos emplean hidrocarburos, que no se encuentran disponibles en todas las regiones, provocando dependencias energéticas entre países. Además, su extracción tiene un impacto muy grande en el medioambiente. Los vehículos se han convertido en un problema importante para los gobiernos y los habitantes, que sufren enfermedades respiratorias provocadas por los gases que emiten. Ante estos inconvenientes, los gobiernos han desarrollado normativas para regular las emisiones de los vehículos. Las conductores también han empezado a exigir vehículos que consuman menos debido al aumento del precio del combustible, convirtiéndose en un factor muy importante a la hora de comprar un vehículo. Todo esto ha contribuido a que los fabricantes introduzcan en los vehículos mejoras orientadas a reducir el consumo de combustible como: optimización del motor, reducción del peso del vehículo, motores híbridos, mejoras aerodinámicas. Sin embargo, estas medidas resultan insuficientes porque el porcentaje de vehículos antiguos en circulación es muy alto. El consumo de combustible depende del vehículo, el entorno y el comportamiento del conductor. Recientemente ha surgido un método para reducir el consumo denominado “Eco-Driving”. Esta técnica de conducción se basa en el control óptimo de las variables que controla el conductor como la velocidad, la aceleración, la desaceleración y la marcha. El ahorro se consigue al minimizar las pérdidas de energía. Este método para mejorar el consumo y reducir la emisión de gases contaminantes es muy útil porque es independiente de la tecnología del vehículo. Sin embargo, el conductor necesita contar con conocimientos sobre conducción eficiente. Esta tesis se centra en la necesidad de que los conductores aprendan cómo conducir eficientemente siguiendo unas normas, basadas en la física, que reducen las demandas innecesarias de energías. Para ello se propone el uso de un asistente que evalúa el comportamiento del conductor desde el punto de vista de la eficiencia, y le propone mejoras. Además, a diferencia de otras propuestas, el sistema proporciona información sobre el entorno cercano para que el usuario pueda tomar decisiones con la suficiente antelación. Esta información es muy importante porque la clave del “eco-driving” es la capacidad del conductor para predecir el estado de la carretera en un futuro cercano. Otro de los objetivos principales de este trabajo de investigación es desarrollar métodos para motivar al usuario a conducir eficientemente, ya que en muchos estudios previos se demuestra que el conductor tiende a volver a sus hábitos de conducción previos a pesar de haber recibido formación sobre “eco-driving”. Los resultados obtenidos en la etapa de experimentación muestran un ahorro de hasta el 22.5% de combustible utilizando la propuesta descrita en esta tesis. No obstante, este porcentaje depende de la respuesta del usuario a las recomendaciones y de su estilo de conducción previo. Además se observó que es importante conocer el perfil de conducción del usuario para ajustar los consejos, y que el aprendizaje se haga de forma progresiva. En caso contrario, el conductor perderá el interés. Conocer el tipo de usuario también es relevante para elegir el incentivo que se usará como retroalimentación. Finalmente, se constata que es imprescindible emplear métodos para motivar al usuario a conducir eficientemente, siendo la gamificación una buena estrategia para conseguirlo.The growth in the number of vehicles in circulation has experienced a strong increase in the last 20 years. The widespread use of the automobile has had very positive effects on the economy of the countries. However, they have also led to major problems due to the pollution produced and the amount of energy required. On the other hand, most of the vehicles employ hydrocarbons, which are not available in all regions, causing energy dependencies between countries. In addition, its extraction has a very large impact on the environment. The vehicles have become an important problem for governments and residents who suffer from respiratory diseases caused by greenhouses emissions. In order to avoid these problems, governments have developed regulations to control the emissions from vehicles. The drivers have also begun to require efficient vehicles due to the increase in fuel prices. Currently, the fuel consumption is a very important factor to buy a vehicle. All this has caused manufacturers to introduce improvements in vehicles in order to minimize the fuel consumption such as: engine optimization, vehicle weight reduction, hybrid engines, aerodynamic improvements. However, these measures are insufficient because the percentage of older vehicles is still very high. Fuel consumption depends on the vehicle, the environment and the behavior of the driver. Recently, a method called "Eco-Driving" has gained popularity. This driving allows us to save fuel. It is based on the optimization of the parameters that the user controls such as: speed, acceleration, deceleration and gear. Fuel saving is achieved by minimizing the energy losses. This method to improve fuel consumption and reduce the emission of greenhouse gases is very useful because it is independent of the vehicle technology. However, the driver needs to know about efficient driving rules. This thesis is focused on the need to learn how to drive efficiently following a set of rules, based on physics, which reduce unnecessary energy demands. In this work, we propose the use of an assistant who evaluates the driver behavior from the point of view of efficiency, and recommends improvements in order to save fuel. Furthermore, unlike other proposals, the system provides information about the nearby environment. Therefore, the user can take decisions in advance. This information is very important because the key to "eco-driving" is the driver's ability to predict the road state in the near future. The other main objective is to develop methods to motivate the user for driving efficiently, since many previous studies demonstrates that the driver tends to return to their previous driving habits, even after having received training on "eco-driving". The results of the experimentation show a saving of up to 22.5% of fuel using the proposal described in this thesis. However, this percentage depends on user´s response to the recommendations and their previous driving style. We also highlight that is essential to know the driving profile in order to adjust the eco-driving tips, and make the learning progressively. Otherwise, the driver will lose interest. In addition, understanding the user type is relevant to choose the type of feedback. Finally, the work in this thesis demonstrates that it is essential to use methods for motivating the user. Gamification is a good strategy to get it.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", from the European Union's Seventh Framework Programme managed by REA-Research Executive Agency (FP7/2007-2013) under grant agreement n° 286533 and from the Spanish funded HAUS IPT-2011-1049-430000 project.Programa en Ingeniería TelemáticaPresidente: José Vehi Casellas; Vocal: Antonio Cañas Vargas; Secretario: Pedro José Muñoz Merin

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

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

GAFU: Using a gamification tool to save fuel

In this paper, we propose, implement and user-validate a training tool for saving fuel that uses some elements from games in order to promote efficient driving and provide feedback to the user. The proposed system uses a fuzzy logic system in order to assess the driving style from the point of view of the fuel consumption. The output is a score between 0 (not efficient) and 10 (efficient). This value can be compared with the scores obtained by other users of the solution that have similar characteristics in order to do a fair comparison and to obtain eco-driving advices adapted to the user's context and environment (e.g., braking frequency is greater on urban road than highway). Providing feedback to the user is essential in eco-driving systems for changing bad driving habits and not returning back to them. In our case, the system provides two types of feedback. The first type of feedback is provided in real time. When the user does not comply with some of a preconfigured set of eco-driving rules, he or she gets a warning message. The second type of feedback is based on a calculated relative score for each user according to his or her driving style, positioning the user into a ranking of eco-driving users and generating a set of eco-driving tips. A validation experiment has been conducted with 36 participants on three different routes in Spain. The results show that the use of gamification tools and techniques in eco-driving assistants helps drivers not to lose interest for fuel saving and helps them not to return back to their previous bad driving habits.The research leading to these results has received fund-ing 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 Competi-tividad 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

Reducing Stress and Fuel Consumption Providing Road Information

The porceeding at: 6th International Symposium on Ambient Intelligence (ISAmI 2015), took place in June,3-5, 2015, in Salamanca (Spain).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 and driver workload during the trip. If it discovers an area where the stress increases and the driving style is worse from the point of view of energy efficiency, a photo is taken and is saved along with its location in a shared database. On the other hand, the solution warns the user when is approaching a region where the driving is difficult (high fuel consumption and stress) using the shared database. In this case, the proposal shows on the screen of the mobile device the image captured previously of the area. The aim is that driver knows in advance the driving environment. Therefore, he or she may adjust the vehicle speed and the driver workload decreases. Data Envelopment Analysis is used to estimate the efficiency of driving and driver workload 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 with 6 participants who made 96 driving tests in Spain. The system reduces the slowdowns (38 %), heart rate (4.70 %), and fuel consumption (12.41 %). 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 programPublicad

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

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

SmartDriver: an assistant for reducing stress and improve the fuel consumption

JARCA 2015: Actas de las XVII Jornadas de ARCA: Sistemas Cualitativos y sus Aplicaciones en Diagnosis, Robótica, Inteligencia Ambiental y Ciudades Inteligentes = Proceedings of the XVII ARCA Days: Qualitative Systems and its Applications in Diagnose Robotics, Ambient Intelligence and Smart Cities, Vinaros (Valencia), 23 al 27 de Junio de 2015The stress, safety and fuel consumption are variables that are strongly related. If the stress is high, the driver is more likely to make mistakes and have ac- cidents. In addition, he or she will make decisions at short notice. The acceleration and deceleration increases, minimizing the use of energy generated by the engine. However, the stress can be reduced if we provide information about the environment in ad- vance. In this paper, we propose a driving assistant which issues tips to the driver in order to improve the stress level. These tips are based on speed. The solution estimates the optimal average speed for each road section. In addition, the solution provides a slowdown profile when the user is close to a stress area. The objective is the initial vehicle speed minimizes the stress level and the sharp acceleration (positive and negative). In addition, the system em- ploys gamification tools to encourage the driver to follow the recommendations. On the other hand, the proposal provides information about the driver and the road state in an anonymous way in order to improve the management of the city traffic. The proposal is run on an Android device and the driver stress is estimated using non intrusives sensors and telemetry from the 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-and 370000), COMINN (IPT-2012-0883-430000) the within (IPT-2012-0882-430000) REMEDISS INNPACTO progra

Reducing stress on habitual journeys

2015 IEEE 5th International Conference on Consumer Electronics–Berlin (ICCE-Berlin), September 6-9, 2015Stress is the cause of a large number of traffic accidents. The driver increases driving mistakes when he or she is in this mental state. Furthermore, the fuel consumption gets worse. In this paper, we propose an algorithm to estimate the optimum speed from the point of view of the stress level for each road section. When the driver completes a road section, the solution provides him or her with feedback. This feedback consists of recommendations such as: "You have driven too fast". The aim is that the driver adjusts speed when he or she repeats the trip. Optimization of the speed reduces stress and improves the driving from the point of view of energy saving. The optimal average speed is estimated using Particle Swarm Optimization (PSO) and MultiLayer Perceptron (MLP). The solution was deployed on Android mobile devices. The results show that the drivers drive smoother and reduce stress when they use the proposal.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 progra