Modeling User Behavior with Reduced Canonical Form (RCF): Exploring the Future with Human Machine Interface (HMI) for ITS
Shivatmica Murgai, Sharv Murgai · 2024
Understanding user behavior and anticipating user decisions is difficult. Human Machine Interfaces (HMIs) provide guiding principles for delivering a seamless interface between users, machines, and infrastructure for Intelligent Transportation Systems (ITS). User behavior comprehension and prediction is unique for every user and involves understanding several dynamic factors. Research has been done leveraging machine learning (ML) and artificial intelligence (AI) techniques to address this interesting interplay by training Neural Network (NN) models to predict the user’s next action based on various input features. The proposed method is to employ Reduced Canonical Form (RCF), a mathematical framework, to represent and simplify user behavior parameters, which enables the production of more intuitive and responsive HMIs for ITS. After representing the user behavior model using RCF, to prove our hypothesis, we have introduced mathematical proofs to show the effectiveness and validity of RCF in order to map user behavior efficiently.