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. 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.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.

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

“…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.…”

A Knowledge-Based AI Framework for Mobility as a Service

“…For example, Mohammadnazar et al [11] extracted volatility measures based on speed, lateral longitudinal acceleration, and temporal driving volatility (using a 3-s time-frame window) from a set of data and used them for cluster drivers (in aggressive, normal, and calm) using K-means and K-medoids methods. In another study, Carpatorea et al [12] proposed a machine learning methodology for quantifying and qualifying driver performance, concerning fuel consumption, based on naturalistic driving data. In a recent study [13], two ensemble learning algorithms (random forests and AdaBoost) were used to predict the traffic intensity before vehicles reach the intersection.…”