Data-driven Steering Torque Behaviour Modelling with Hidden Markov Models

TL;DR

This paper presents a data-driven Hidden Markov Model approach to predict driver steering torque, improving accuracy and smoothness, and capturing human variability for enhanced ADAS performance.

Contribution

It introduces a novel HMM-based method with an extensive parameter selection framework for accurate, smooth, and personalized driver steering torque prediction.

Findings

92% steering torque prediction accuracy

37% increase in signal smoothness

90% reduction in data requirements

Abstract

Modern Advanced Driver Assistance Systems (ADAS) are limited in their ability to consider the drivers intention, resulting in unnatural guidance and low customer acceptance. In this research, we focus on a novel data-driven approach to predict driver steering torque. In particular, driver behavior is modeled by learning the parameters of a Hidden Markov Model (HMM) and estimation is performed with Gaussian Mixture Regression (GMR). An extensive parameter selection framework enables us to objectively select the model hyper-parameters and prevents overfitting. The final model behavior is optimized with a cost function balancing between accuracy and smoothness. Naturalistic driving data covering seven participants is obtained using a static driving simulator at Toyota Motor Europe for the training, evaluation, and testing of the proposed model. The results demonstrate that our approach achieved a 92% steering torque accuracy with a 37% increase in signal smoothness and 90% fewer data compared to a baseline. In addition, our model captures the complex and nonlinear human behavior and inter-driver variability from novice to expert drivers, showing an interesting potential to become a steering performance predictor in future user-oriented ADAS.