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== Abstract == | == Abstract == | ||
| − | 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 | + | 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. Publicado |
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== Original document == | == Original document == | ||
Latest revision as of 07:59, 2 February 2021
Abstract
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. Publicado
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Published on 01/01/2014
Volume 2014, 2014
DOI: 10.1109/iccve.2014.7297570
Licence: CC BY-NC-SA license
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