Estimating eco-friendly driving behavior in various traffic situations, using machine learning

Sammanfattning: This thesis investigates how various driver signals, signals that a truck driver can interact with, influences fuel consumption and what are the optimal values of these signals in various traffic conditions. More specifically, the objective is to estimate good driver behavior in various traffic conditions and compare bad driver behavior in similar situations to see how performing a specific driver action, changing a driver signal from the bad driver value to the corresponding good driver value impacts the fuel consumption. The result is an AI-based algorithm that utilizes the transformer model architecture to estimate good driver behavior, based on environmental describing signals, as well as fuel consumption. Utilizing these, causal inference is used to estimate how much fuel can be saved by switching a driver signal from a bad driver value to a good driver value.