Data-driven Steering Torque Behaviour Modelling with Hidden Markov Models

Data-driven Steering Torque Behaviour Modelling with Hidden Markov Models

van Wijk, Robert (Student TU Delft)
Lazcano, Andrea Michelle Rios (Toyota Motor Europe)
Akutain, Xabier Carrera (Toyota Motor Europe)
Shyrokau, B. (TU Delft Intelligent Vehicles)

2022

Modern Advanced Driver Assistance Systems (ADAS) are limited in their ability to consider the driver's 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.

Data-Driven
Driver Modelling
Feature Selection
Hidden Markov Model
Simulator

http://resolver.tudelft.nl/uuid:1012511d-b978-449c-ac19-feb3efd4b81d

https://doi.org/10.1016/j.ifacol.2022.10.227

1474-6670

IFAC-PapersOnLine, 55 (29), 31-36

15th IFAC Symposium on Analysis, Design and Evaluation of Human Machine Systems, HMS 2022, 2022-09-12 → 2022-09-15, San Jose, United States

Institutional Repository

journal article

© 2022 Robert van Wijk, Andrea Michelle Rios Lazcano, Xabier Carrera Akutain, B. Shyrokau