2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) 2016
DOI: 10.1109/icmla.2016.0194
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Learning of Aggregate Features for Comparing Drivers Based on Naturalistic Data

Abstract: Abstract-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. … Show more

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Cited by 3 publications
(1 citation statement)
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“…We state that unmeasured factors give a biased result when assessing driver performance and in our other work we argue that we can take into account the effect of said factors. We then want to see how well we can predict a modified response variable based on FPC, see [14].We want to analyze how much of the unmeasured factors we are able to capture using the APPES patterns. FPC contains the effect of other factors, such as air drag, facilitating comparison of desired variables, in our case the driver, under similar conditions.…”
Section: Methodsmentioning
confidence: 99%
“…We state that unmeasured factors give a biased result when assessing driver performance and in our other work we argue that we can take into account the effect of said factors. We then want to see how well we can predict a modified response variable based on FPC, see [14].We want to analyze how much of the unmeasured factors we are able to capture using the APPES patterns. FPC contains the effect of other factors, such as air drag, facilitating comparison of desired variables, in our case the driver, under similar conditions.…”
Section: Methodsmentioning
confidence: 99%