Magana Organero 2014a - Revision history

Scipediacontent at 06:59, 2 February 2021

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Scipediacontent: Scipediacontent moved page Draft Content 923291647 to Magana Organero 2014a

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Scipediacontent moved page Draft Content 923291647 to Magana Organero 2014a

Scipediacontent: Created page with " == 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,..."

2021-01-25T09:57:24Z

Created page with " == 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,..."

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== 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 âHERMES-SMART DRIVERâ project TIN2013-46801-C4-2-R within the Spanish "Plan Nacional de I+D+I" under the Spanish Ministerio de EconomÃa y Competitividad and from the Spanish Ministerio de EconomÃa 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

== Original document ==

The different versions of the original document can be found in:

* [http://hdl.handle.net/10016/21854 http://hdl.handle.net/10016/21854]

* [https://e-archivo.uc3m.es/bitstream/10016/21854/1/lesy_ICCV_2014_ps.pdf https://e-archivo.uc3m.es/bitstream/10016/21854/1/lesy_ICCV_2014_ps.pdf]

* [http://xplorestaging.ieee.org/ielx7/7284675/7297490/07297570.pdf?arnumber=7297570 http://xplorestaging.ieee.org/ielx7/7284675/7297490/07297570.pdf?arnumber=7297570],
: [http://dx.doi.org/10.1109/iccve.2014.7297570 http://dx.doi.org/10.1109/iccve.2014.7297570]

* [https://ieeexplore.ieee.org/document/7297570 https://ieeexplore.ieee.org/document/7297570],
: [https://dblp.uni-trier.de/db/conf/iccve/iccve2014.html#MaganaO14 https://dblp.uni-trier.de/db/conf/iccve/iccve2014.html#MaganaO14],
: [https://trid.trb.org/view/1427605 https://trid.trb.org/view/1427605],
: [https://academic.microsoft.com/#/detail/1946947839 https://academic.microsoft.com/#/detail/1946947839]

@@ Line 2: / Line 2: @@
 == 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 EconomÃa 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
+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
 == Original document ==

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