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| Publications:Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data |
| Abstract | <p>Fuel used by heavy duty trucks is … <p>Fuel used by heavy duty trucks is a major cost for logistics companies, and therefore improvements in this area are highly desired. Many of the factors that influence fuel consumption, such as the road type, vehicle configuration or external environment, are difficult to influence. One of the most under-explored ways to lower the costs is training and incentivizing drivers. However, today it is difficult to measure driver performance in a comprehensive way outside of controlled, experimental setting.</p><p>This paper proposes a machine learning methodology for quantifying and qualifying driver performance, with respect to fuel consumption, that is suitable for naturalistic driving situations. The approach is a knowledge-based feature extraction technique, constructing a normalizing fuel consumption value denoted Fuel under Predefined Conditions (FPC), which captures the effect of factors that are relevant but are not measured directly.</p><p>The FPC, together with information available from truck sensors, is then compared against the actual fuel used on a given road segment, quantifying the effects associated with driver behavior or other variables of interest. We show that raw fuel consumption is a biased measure of driver performance, being heavily influenced by other factors such as high load or adversary weather conditions, and that using FPC leads to more accurate results. In this paper we also show evaluation the proposed method using large-scale, real-world, naturalistic database of heavy-duty vehicle operation.</p>of heavy-duty vehicle operation.</p> |
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| Author | Iulian Carpatorea + , Sławomir Nowaczyk + , Thorsteinn Rögnvaldsson + , Marcus Elmer + , Johan Lodin + |
| Conference | IEEE 15th International Conference on Machine Learning and Applications, ICMLA 2016, Anaheim, United States, 18-20 December, 2016 |
| DOI | http://dx.doi.org/10.1109/ICMLA.2016.0194 + |
| Diva | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1068979 |
| EndPage | 1072 + |
| HostPublication | Proceedings : 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) + |
| PublicationType | Conference Paper + |
| Publisher | IEEE Computer Society + |
| StartPage | 1067 + |
| Title | Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data + |
| Year | 2016 + |
| Has queryThis property is a special property in this wiki. | Publications:Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data + , Publications:Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data + , Publications:Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data + , Publications:Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data + , Publications:Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data + , Publications:Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data + , Publications:Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data + , Publications:Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data + , Publications:Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data + , Publications:Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data + |
| Categories | Publication + |
| Modification dateThis property is a special property in this wiki. | 14 February 2017 21:22:04 + |
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