]> 2026-04-04T17:13:36+02:00 Publications:Methods to quantify and qualify truck driver performance 500 en <p>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.</p><p>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.</p><p>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.</p><p>Throughout the thesis and attached articles we show that both methods are able to operate within the specified conditions and achieve the set goal.</p> http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1072213 Licentiate Thesis Halmstad University Dissertations ; 28 Methods to quantify and qualify truck driver performance 2017 2017-02-08T21:22:43Z 2457793.3907755 Methods to quantify and qualify truck driver performance 500 0 plainlist list 1 [[:Publications:Methods to quantify and qualify truck driver performance]] Methods to quantify and qualify truck driver performance# QUERY1462046075c166b039d54e2d68a83b08 500 [2,"smw-datavalue-property-restricted-annotation-use","Publisher"] Methods to quantify and qualify truck driver performance# ERR6c155b5d70b69ddb590b4f2e73389c2a 500 [2,"smw-datavalue-wikipage-property-invalid-title","Supervisors","Nowaczyk, Slawomir (slanow), Associate Professor (H\u00f6gskolan i Halmstad (2804), Akademin f\u00f6r informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), ;;CAISR Centrum f\u00f6r till\u00e4mpade intelligenta system (IS-lab) (13650))R\u00f6gnvaldsson, Thorsteinn (denni), Professor (H\u00f6gskolan i Halmstad (2804), Akademin f\u00f6r informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), ;;CAISR Centrum f\u00f6r till\u00e4mpade intelligenta system (IS-lab) (13650))"] Methods to quantify and qualify truck driver performance# ERR7bf7a32fd5dfdd1bdbd08a2360b6c72f 500 [2,"smw-datavalue-property-restricted-annotation-use","Author"] Methods to quantify and qualify truck driver performance# ERRe24f17e090c98448f3de99d03182b7b6