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V. Corcoba Magaña is an Assistant Professor at Computer Science Department at University of Oviedo (Spain). In the past, he was a postdoctoral researcher for Telematic Engineering at Universidad Carlos III de Madrid (Spain). He obtained a Ph.D. from Universidad Carlos III (Spain) after he obtained the MSc at the University of Granada (Spain). He is working on wearables devices, stress detection, and intelligent systems for improving driving safety and fuel consumption. He has more than nine years of experience working on research projects related intelligent transportation system. He is the author of four books, and more than 40 articles about energy efficient and safety on vehicles. V. Corcoba Magaña is member of the University Institute of Industrial Technology of Asturias (IUTA) and member of SMIOT research group.
A large percentage of traffic accidents are due to human errors. Driving behavior and driving stress influence the probability of making these mistakes. Both are influenced by multiple factors, among which might be elements such as age, gender, sleeping hours, or working hours. The objective of this paper is to study, in a real scenario and without forcing the driver’s state, the relationship between driving behavior, driving stress, and these elements. Furthermore, we aim to provide guidelines to improve driving assistants. In this study, we used 1050 driving samples obtained from 35 volunteers. The driving samples correspond to regular commutes from home to the workplace. ANOVA and ANCOVA tests were carried out to check if there are significant differences in the four factors analyzed. Although the results show that driving behavior and driving stress are affected by gender, age, and sleeping hours, the most critical variable is working hours. Drivers with long working days suffer significantly more driving stress compared to other drivers, with the corresponding effect on their driving style. These drivers were the worst at maintaining the safety distance.
Víctor Magaña; Xabiel Pañeda; Roberto Garcia; Sara Paiva; Laura Pozueco. Beside and Behind the Wheel: Factors that Influence Driving Stress and Driving Behavior. Sustainability 2021, 13, 4775 .
AMA StyleVíctor Magaña, Xabiel Pañeda, Roberto Garcia, Sara Paiva, Laura Pozueco. Beside and Behind the Wheel: Factors that Influence Driving Stress and Driving Behavior. Sustainability. 2021; 13 (9):4775.
Chicago/Turabian StyleVíctor Magaña; Xabiel Pañeda; Roberto Garcia; Sara Paiva; Laura Pozueco. 2021. "Beside and Behind the Wheel: Factors that Influence Driving Stress and Driving Behavior." Sustainability 13, no. 9: 4775.
The transport network and mobility aspects are constantly changing, and major changes are expected in the coming years in terms of safety and sustainability purposes. In this paper, we present the main conclusions and analysis of data collected from a survey of drivers in Spain and Portugal regarding user preferences, highlighting the main functionalities and behavior that an advanced driver assistance system must have in order to grant it special importance on the road to prevent accidents and also to enable drivers to have a pleasant journey. Based on the results obtained from the survey, we developed and present a working prototype for an advanced driver assistance system (ADAS), its architecture and rules systems that allowed us to create and test some scenarios in a real environment.
Sara Paiva; Xabiel Pañeda; Victor Corcoba; Roberto García; Próspero Morán; Laura Pozueco; Marina Valdés; Covadonga del Camino. User Preferences in the Design of Advanced Driver Assistance Systems. Sustainability 2021, 13, 3932 .
AMA StyleSara Paiva, Xabiel Pañeda, Victor Corcoba, Roberto García, Próspero Morán, Laura Pozueco, Marina Valdés, Covadonga del Camino. User Preferences in the Design of Advanced Driver Assistance Systems. Sustainability. 2021; 13 (7):3932.
Chicago/Turabian StyleSara Paiva; Xabiel Pañeda; Victor Corcoba; Roberto García; Próspero Morán; Laura Pozueco; Marina Valdés; Covadonga del Camino. 2021. "User Preferences in the Design of Advanced Driver Assistance Systems." Sustainability 13, no. 7: 3932.
Road accidents and safe driving are one of the main concerns of transportation systems and the companies that explore different solutions to reduce the accident rate. The most interesting option to achieve this goal is through an on-board training of professional drivers to apply safe driving techniques during their work activity. The purpose of this study is to analyze a monitoring system that is not limited to the real-time vehicle tracking but is also capable of monitoring and providing real-time feedback and in-vehicle training. We analyze the influence of different sociodemographic factors on driving behavior. The analyzed data correspond to an urban public transport company, obtained from a study performed on 246 drivers. The drivers received training based on a blended learning system with an on-board feedback device, accompanied by both theoretical and practical sessions. The driving behavior of each driver is obtained from the data gathered from the vehicles that allow us to characterize their driving patterns. The information related to safe driving is completed with a list of the records of road accidents. The results of the sociodemographic influence on driving behavior provide significant information, giving an elaborated classification of safety driving patterns in order to apply intelligent transportation systems.
Laura Pozueco; Nishu Gupta; Xabiel G. Paneda; Roberto Garcia; Alejandro G. Tuero; David Melendi; Abel Rionda; Victor Corcoba. Analysis of Driving Patterns and On-Board Feedback-Based Training for Proactive Road Safety Monitoring. IEEE Transactions on Human-Machine Systems 2020, 50, 529 -537.
AMA StyleLaura Pozueco, Nishu Gupta, Xabiel G. Paneda, Roberto Garcia, Alejandro G. Tuero, David Melendi, Abel Rionda, Victor Corcoba. Analysis of Driving Patterns and On-Board Feedback-Based Training for Proactive Road Safety Monitoring. IEEE Transactions on Human-Machine Systems. 2020; 50 (6):529-537.
Chicago/Turabian StyleLaura Pozueco; Nishu Gupta; Xabiel G. Paneda; Roberto Garcia; Alejandro G. Tuero; David Melendi; Abel Rionda; Victor Corcoba. 2020. "Analysis of Driving Patterns and On-Board Feedback-Based Training for Proactive Road Safety Monitoring." IEEE Transactions on Human-Machine Systems 50, no. 6: 529-537.
Globalization has increased the number of road trips and vehicles. The result has been an intensification of traffic accidents, which are becoming one of the most important causes of death worldwide. Traffic accidents are often due to human error, the probability of which increases when the cognitive ability of the driver decreases. Cognitive capacity is closely related to the driver’s mental state, as well as other external factors such as the CO2 concentration inside the vehicle. The objective of this work is to analyze how these elements affect driving. We have conducted an experiment with 50 drivers who have driven for 25 min using a driving simulator. These drivers completed a survey at the start and end of the experiment to obtain information about their mental state. In addition, during the test, their stress level was monitored using biometric sensors and the state of the environment (temperature, humidity and CO2 level) was recorded. The results of the experiment show that the initial level of stress and tiredness of the driver can have a strong impact on stress, driving behavior and fatigue produced by the driving test. Other elements such as sadness and the conditions of the interior of the vehicle also cause impaired driving and affect compliance with traffic regulations.
Víctor Corcoba Magaña; Wilhelm Daniel Scherz; Ralf Seepold; Natividad Martínez Madrid; Xabiel García Pañeda; Roberto Garcia. The Effects of the Driver’s Mental State and Passenger Compartment Conditions on Driving Performance and Driving Stress. Sensors 2020, 20, 5274 .
AMA StyleVíctor Corcoba Magaña, Wilhelm Daniel Scherz, Ralf Seepold, Natividad Martínez Madrid, Xabiel García Pañeda, Roberto Garcia. The Effects of the Driver’s Mental State and Passenger Compartment Conditions on Driving Performance and Driving Stress. Sensors. 2020; 20 (18):5274.
Chicago/Turabian StyleVíctor Corcoba Magaña; Wilhelm Daniel Scherz; Ralf Seepold; Natividad Martínez Madrid; Xabiel García Pañeda; Roberto Garcia. 2020. "The Effects of the Driver’s Mental State and Passenger Compartment Conditions on Driving Performance and Driving Stress." Sensors 20, no. 18: 5274.
Victor Corcoba Magana; Xabiel G. Paneda; Alejandro Garcia Tuero; Laura Pozueco; Roberto Garcia; David Melendi; Abel Rionda. A Method for Making a Fair Evaluation of Driving Styles in Different Scenarios With Recommendations for Their Improvement. IEEE Intelligent Transportation Systems Magazine 2018, 13, 136 -148.
AMA StyleVictor Corcoba Magana, Xabiel G. Paneda, Alejandro Garcia Tuero, Laura Pozueco, Roberto Garcia, David Melendi, Abel Rionda. A Method for Making a Fair Evaluation of Driving Styles in Different Scenarios With Recommendations for Their Improvement. IEEE Intelligent Transportation Systems Magazine. 2018; 13 (1):136-148.
Chicago/Turabian StyleVictor Corcoba Magana; Xabiel G. Paneda; Alejandro Garcia Tuero; Laura Pozueco; Roberto Garcia; David Melendi; Abel Rionda. 2018. "A Method for Making a Fair Evaluation of Driving Styles in Different Scenarios With Recommendations for Their Improvement." IEEE Intelligent Transportation Systems Magazine 13, no. 1: 136-148.
Jorge Y. Fernández-Rodríguez; Juan A. Álvarez-García; Jesús Arias Fisteus; Miguel R. Luaces; Victor Corcoba Magaña. Benchmarking real-time vehicle data streaming models for a smart city. Information Systems 2017, 72, 62 -76.
AMA StyleJorge Y. Fernández-Rodríguez, Juan A. Álvarez-García, Jesús Arias Fisteus, Miguel R. Luaces, Victor Corcoba Magaña. Benchmarking real-time vehicle data streaming models for a smart city. Information Systems. 2017; 72 ():62-76.
Chicago/Turabian StyleJorge Y. Fernández-Rodríguez; Juan A. Álvarez-García; Jesús Arias Fisteus; Miguel R. Luaces; Victor Corcoba Magaña. 2017. "Benchmarking real-time vehicle data streaming models for a smart city." Information Systems 72, no. : 62-76.
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.
Victor Corcoba Magana; Mario Munoz-Organero. Toward Safer Highways: Predicting Driver Stress in Varying Conditions on Habitual Routes. IEEE Vehicular Technology Magazine 2017, 12, 69 -76.
AMA StyleVictor Corcoba Magana, Mario Munoz-Organero. Toward Safer Highways: Predicting Driver Stress in Varying Conditions on Habitual Routes. IEEE Vehicular Technology Magazine. 2017; 12 (4):69-76.
Chicago/Turabian StyleVictor Corcoba Magana; Mario Munoz-Organero. 2017. "Toward Safer Highways: Predicting Driver Stress in Varying Conditions on Habitual Routes." IEEE Vehicular Technology Magazine 12, no. 4: 69-76.
Víctor Corcoba-Magaña; Mario Muñoz-Organero; Xabiel G. Pañeda. Prediction of motorcyclist stress using a heartrate strap, the vehicle telemetry and road information. Journal of Ambient Intelligence and Smart Environments 2017, 9, 579 -593.
AMA StyleVíctor Corcoba-Magaña, Mario Muñoz-Organero, Xabiel G. Pañeda. Prediction of motorcyclist stress using a heartrate strap, the vehicle telemetry and road information. Journal of Ambient Intelligence and Smart Environments. 2017; 9 (5):579-593.
Chicago/Turabian StyleVíctor Corcoba-Magaña; Mario Muñoz-Organero; Xabiel G. Pañeda. 2017. "Prediction of motorcyclist stress using a heartrate strap, the vehicle telemetry and road information." Journal of Ambient Intelligence and Smart Environments 9, no. 5: 579-593.
Victor Corcoba Magaña; Mario Muñoz Organero; Juan A. Álvarez-García; Jorge Yago Fernández Rodríguez. Estimation of the Optimum Speed to Minimize the Driver Stress Based on the Previous Behavior. Advances in Intelligent Systems and Computing 2016, 31 -39.
AMA StyleVictor Corcoba Magaña, Mario Muñoz Organero, Juan A. Álvarez-García, Jorge Yago Fernández Rodríguez. Estimation of the Optimum Speed to Minimize the Driver Stress Based on the Previous Behavior. Advances in Intelligent Systems and Computing. 2016; ():31-39.
Chicago/Turabian StyleVictor Corcoba Magaña; Mario Muñoz Organero; Juan A. Álvarez-García; Jorge Yago Fernández Rodríguez. 2016. "Estimation of the Optimum Speed to Minimize the Driver Stress Based on the Previous Behavior." Advances in Intelligent Systems and Computing , no. : 31-39.
Traffic incidents (heavy traffic, adverse weather conditions, and traffic accidents) cause an increase in the frequency and intensity of the acceleration and deceleration. The result is a very significant increase in fuel consumption. In this paper, we propose a solution to reduce the impact of such events on energy consumption. The solution detects the traffic incidents based on measured telemetry data from vehicles and the different driver profiles. The proposal takes into account the rolling resistance coefficient, the road slope angle, and the vehicles speeds, from vehicles which are on the scene of the traffic incident, in order to estimate the optimal deceleration profile. Adapted advice and feedback are provided to the drivers in order to appropriately and timely release the accelerator pedal. The expert system is implemented on Android mobile devices and has been validated using a dataset of 150 tests using 15 different drivers. The main contribution of this paper is the proposal of a system to detect traffic incidents and provide an optimal deceleration pattern for the driver to follow without requiring sensors on the road. The results show an improvement on the fuel consumption of up to 13.47%.
V. Corcoba Magaña; M. Muñoz-Organero. WATI: Warning of Traffic Incidents for Fuel Saving. Mobile Information Systems 2016, 2016, 1 -16.
AMA StyleV. Corcoba Magaña, M. Muñoz-Organero. WATI: Warning of Traffic Incidents for Fuel Saving. Mobile Information Systems. 2016; 2016 ():1-16.
Chicago/Turabian StyleV. Corcoba Magaña; M. Muñoz-Organero. 2016. "WATI: Warning of Traffic Incidents for Fuel Saving." Mobile Information Systems 2016, no. : 1-16.
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.
Victor Corcoba Magana; Mario Munoz-Organero. Artemisa: A Personal Driving Assistant for Fuel Saving. IEEE Transactions on Mobile Computing 2015, 15, 2437 -2451.
AMA StyleVictor Corcoba Magana, Mario Munoz-Organero. Artemisa: A Personal Driving Assistant for Fuel Saving. IEEE Transactions on Mobile Computing. 2015; 15 (10):2437-2451.
Chicago/Turabian StyleVictor Corcoba Magana; Mario Munoz-Organero. 2015. "Artemisa: A Personal Driving Assistant for Fuel Saving." IEEE Transactions on Mobile Computing 15, no. 10: 2437-2451.
Stress 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.
Victor Corcoba Magana; Victor Corcoba Magaña. Reducing stress on habitual journeys. 2015 IEEE 5th International Conference on Consumer Electronics - Berlin (ICCE-Berlin) 2015, 153 -157.
AMA StyleVictor Corcoba Magana, Victor Corcoba Magaña. Reducing stress on habitual journeys. 2015 IEEE 5th International Conference on Consumer Electronics - Berlin (ICCE-Berlin). 2015; ():153-157.
Chicago/Turabian StyleVictor Corcoba Magana; Victor Corcoba Magaña. 2015. "Reducing stress on habitual journeys." 2015 IEEE 5th International Conference on Consumer Electronics - Berlin (ICCE-Berlin) , no. : 153-157.
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.
Víctor Corcoba Magaña; Mario Muñoz Organero. Reducing Stress and Fuel Consumption Providing Road Information. Advances in Intelligent Systems and Computing 2015, 23 -31.
AMA StyleVíctor Corcoba Magaña, Mario Muñoz Organero. Reducing Stress and Fuel Consumption Providing Road Information. Advances in Intelligent Systems and Computing. 2015; ():23-31.
Chicago/Turabian StyleVíctor Corcoba Magaña; Mario Muñoz Organero. 2015. "Reducing Stress and Fuel Consumption Providing Road Information." Advances in Intelligent Systems and Computing , no. : 23-31.
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.
Víctor Corcoba Magaña; Mario Muñoz Organero. Reducing stress and fuel consumption providing road information. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 2014, 3, 35 -47.
AMA StyleVíctor Corcoba Magaña, Mario Muñoz Organero. Reducing stress and fuel consumption providing road information. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal. 2014; 3 (4):35-47.
Chicago/Turabian StyleVíctor Corcoba Magaña; Mario Muñoz Organero. 2014. "Reducing stress and fuel consumption providing road information." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 3, no. 4: 35-47.
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.
Víctor Corcoba Magaña; Mario Muñoz Organero. The Impact of Using Gamification on the Eco-driving Learning. Advances in Intelligent Systems and Computing 2014, 45 -52.
AMA StyleVíctor Corcoba Magaña, Mario Muñoz Organero. The Impact of Using Gamification on the Eco-driving Learning. Advances in Intelligent Systems and Computing. 2014; ():45-52.
Chicago/Turabian StyleVíctor Corcoba Magaña; Mario Muñoz Organero. 2014. "The Impact of Using Gamification on the Eco-driving Learning." Advances in Intelligent Systems and Computing , no. : 45-52.
This paper implements and validates an expert system that, based on the detection or previous knowledge of certain types of traffic signals, proposes a method to reduce fuel consumption by calculating optimal deceleration patterns, minimizing the use of braking. The expert system uses a mobile device's embedded camera to monitor the environment and to recognize certain types of static traffic signals that force or can force a vehicle to stop. The system uses an adaptation of the algorithm proposed by Viola and Jones for the recognition of faces in real time, adapted to the detection of traffic signals. Detected signals are also incorporated into a central database for future use. When the vehicle approaches an upcoming traffic signal, the algorithm estimates the distance required to stop the vehicle without using the brakes, taking into account the rolling resistance coefficient and the road slope angle. Appropriate advice and feedback are provided to the driver to release the accelerator pedal. The expert system is implemented on Android mobile devices and has been validated using a data set of 180 tests with five different models of vehicles and nine different drivers. The main contribution of this paper is the proposal of an assistant that uses information from the environment and from the vehicle to calculate optimal deceleration patterns when approaching traffic signals that force or may force the vehicle to stop. In addition, the proposed solution does not require the installation of infrastructure on the road, and it can be installed into any vehicle.
Mario Munoz-Organero; Victor Corcoba Magana. Validating the Impact on Reducing Fuel Consumption by Using an EcoDriving Assistant Based on Traffic Sign Detection and Optimal Deceleration Patterns. IEEE Transactions on Intelligent Transportation Systems 2013, 14, 1023 -1028.
AMA StyleMario Munoz-Organero, Victor Corcoba Magana. Validating the Impact on Reducing Fuel Consumption by Using an EcoDriving Assistant Based on Traffic Sign Detection and Optimal Deceleration Patterns. IEEE Transactions on Intelligent Transportation Systems. 2013; 14 (2):1023-1028.
Chicago/Turabian StyleMario Munoz-Organero; Victor Corcoba Magana. 2013. "Validating the Impact on Reducing Fuel Consumption by Using an EcoDriving Assistant Based on Traffic Sign Detection and Optimal Deceleration Patterns." IEEE Transactions on Intelligent Transportation Systems 14, no. 2: 1023-1028.
This paper implements and validates a system to save fuel based on the collaboration of drivers. The system gets the optimal speed pattern evaluating the driving of nearby drivers. A fuzzy logic system is used to assess drivers and the information about nearby vehicles is obtained through WIFI-Direct. Best driver sends the optimal speed pattern to the other vehicles and the mobile device notifies the user through a vibration pattern or speaker if the user should slow down or speed up.
Víctor Corcoba Magaña; Mario Muñoz Organero. AndroWI: Collaborative System for Fuel Saving Using Android Mobile Devices. Advances in Intelligent Systems and Computing 2013, 219, 49 -55.
AMA StyleVíctor Corcoba Magaña, Mario Muñoz Organero. AndroWI: Collaborative System for Fuel Saving Using Android Mobile Devices. Advances in Intelligent Systems and Computing. 2013; 219 ():49-55.
Chicago/Turabian StyleVíctor Corcoba Magaña; Mario Muñoz Organero. 2013. "AndroWI: Collaborative System for Fuel Saving Using Android Mobile Devices." Advances in Intelligent Systems and Computing 219, no. : 49-55.