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Features extracted from APPES to enable the categorization of heavy-duty vehicle drivers
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-8797-5112
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-7796-5201
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0001-5163-2997
Volvo Group Trucks Technology, Göteborg, Sweden.
2017 (engelsk)Inngår i: 2017 Intelligent Systems Conference (IntelliSys), 2017, s. 476-481Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Improving the performance of systems is a goal pursued in all areas and vehicles are no exception. In places like Europe, where the majority of goods are transported over land, it is imperative for fleet operators to have the best efficiency, which results in efforts to improve all aspects of truck operations. We focus on drivers and their performance with respect to fuel consumption. Some of relevant factors are not accounted for inavailable naturalistic data, since it is not feasible to measure them. An alternative is to set up experiments to investigate driver performance but these are expensive and the results are not always conclusive. For example, drivers are usually aware of the experiment’s parameters and adapt their behavior.

This paper proposes a method that addresses some of the challenges related to categorizing driver performance with respect to fuel consumption in a naturalistic environment. We use expert knowledge to transform the data and explore the resulting structure in a new space. We also show that the regions found in APPES provide useful information related to fuel consumption. The connection between APPES patterns and fuel consumption can be used to, for example, cluster drivers in groups that correspond to high or low performance. © 2017 IEEE

sted, utgiver, år, opplag, sider
2017. s. 476-481
Emneord [en]
truck driver, driver performance, driver behavior, fuel economy, heavy-duty vehicle performance
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-33232DOI: 10.1109/IntelliSys.2017.8324336ISI: 000456827800062Scopus ID: 2-s2.0-85050862809ISBN: 978-1-5090-6435-9 (digital)ISBN: 978-1-5090-6436-6 (tryckt)OAI: oai:DiVA.org:hh-33232DiVA, id: diva2:1072493
Konferanse
Intelligent Systems Conference (IntelliSys 2017), London, United Kingdom, 7-8 September, 2017
Tilgjengelig fra: 2017-02-08 Laget: 2017-02-08 Sist oppdatert: 2020-02-03bibliografisk kontrollert
Inngår i avhandling
1. Methods to quantify and qualify truck driver performance
Åpne denne publikasjonen i ny fane eller vindu >>Methods to quantify and qualify truck driver performance
2017 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Fuel consumption is a major economical component of vehicles, particularly for heavy-duty vehicles. It is dependent on many factors, such as driver and environment, and control over some factors is present, e.g. route, and we can try to optimize others, e.g. driver. The driver is responsible for around 30% of the operational cost for the fleet operator and is therefore important to have efficient drivers as they also inuence fuel consumption which is another major cost, amounting to around 40% of vehicle operation. The difference between good and bad drivers can be substantial, depending on the environment, experience and other factors.

In this thesis, two methods are proposed that aim at quantifying and qualifying driver performance of heavy duty vehicles with respect to fuel consumption. The first method, Fuel under Predefined Conditions (FPC), makes use of domain knowledge in order to incorporate effect of factors which are not measured. Due to the complexity of the vehicles, many factors cannot be quantified precisely or even measured, e.g. wind speed and direction, tire pressure. For FPC to be feasible, several assumptions need to be made regarding unmeasured variables. The effect of said unmeasured variables has to be quantified, which is done by defining specific conditions that enable their estimation. Having calculated the effect of unmeasured variables, the contribution of measured variables can be estimated. All the steps are required to be able to calculate the influence of the driver. The second method, Accelerator Pedal Position - Engine Speed (APPES) seeks to qualify driver performance irrespective of the external factors by analyzing driver intention. APPES is a 2D histogram build from the two mentioned signals. Driver performance is expressed, in this case, using features calculated from APPES.

The focus of first method is to quantify fuel consumption, giving us the possibility to estimate driver performance. The second method is more skewed towards qualitative analysis allowing a better understanding of driver decisions and how they affect fuel consumption. Both methods have the ability to give transferable knowledge that can be used to improve driver's performance or automatic driving systems.

Throughout the thesis and attached articles we show that both methods are able to operate within the specified conditions and achieve the set goal.

sted, utgiver, år, opplag, sider
Halmstad: Halmstad University Press, 2017. s. 23
Serie
Halmstad University Dissertations ; 28
Emneord
Driver performance, heavy-duty vehicle, fuel economy, fuel consumption, fuel prediction, truck driver
HSV kategori
Identifikatorer
urn:nbn:se:hh:diva-33229 (URN)978-91-87045-59-2 (ISBN)978-91-87045-58-5 (ISBN)
Presentation
2017-02-10, Wigforssalen, Visionen, Kristian IV:s väg 3, Halmstad, 13:40 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2017-02-08 Laget: 2017-02-07 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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