Features extracted from APPES to enable the categorization of heavy-duty vehicle drivers

Abstract

Abstract-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 in available 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.

Attention Horizon as a Predictor for the Fuel Consumption Rate of Drivers

“…Recently, Carpatorea et al. have introduced an approach that analyzes the driving behavior based on the extracted sequence of drivers’ actions [ 29 ]. These actions are clusters extracted by a Gaussian mixture model from a 2D histogram (APPES) of accelerator pedal position (APP) and engine speed (ES).…”