Current commercial Driver Steering Assistance Systems (DSAS) focus on path-tracking performance without taking into account driver intentions. Improved driver-automation interaction can be achieved by sharing vehicle lateral control through torques. Furthermore, integrating a driver steering-torque model allows to better match driver intentions. In this research, an existing driver model is adapted and parametrized for estimating driver steering-torque. Driver behaviour is modelled by learning the parameters of a Hidden Markov Model (HMM) and estimation is performed with Gaussian Mixture Regression (GMR). A parameter selection framework enables to select model hyper-parameters objectively. First, feature relevance is determined with an extensive feature
selection step. Thereafter, an iterative overfitting criteria is employed to select the number of hidden states. Final model behaviour is determined by adjusting the metric weights of a linear cost-function with the aim to trade-off estimation accuracy and smoothness. Naturalistic driver steering-torque data from seven participants was gathered in a fixed-base driving simulator at Toyota Motor
Europe for the training, evaluation, and testing of the proposed model. The results demonstrate that a 92% model accuracy can be achieved while the estimated steering-torque is 37% smoother and requires 90% less data compared to a baseline model.