In comparison with the high level of knowledge about vehicle dynamics which exists nowadays, the role of the driver in the driver–vehicle system is still relatively poorly understood. A large variety of driver models exist for various applications; however, few of them take account of the driver’s sensory dynamics, and those that do are limited in their scope and accuracy. A review of the literature has been carried out to consolidate information from previous studies which may be useful when incorporating human sensory systems into the design of a driver model. This includes information on sensory dynamics, delays, thresholds and integration of multiple sensory stimuli. This review should provide a basis for further study into sensory perception during driving.

A recent review of the literature has indicated that sensory dynamics play an important role in the driver-vehicle steering task, motivating the design of a new driver model incorporating human sensory systems. This paper presents a full derivation of the linear driver model developed in previous work, and extends the model to control a vehicle with nonlinear tyres. Various nonlinear controllers and state estimators are compared with different approximations of the true system dynamics. The model simulation time is found to increase significantly with the complexity of the controller and state estimator. In general the more complex controllers perform best, although with certain vehicle and tyre models linearised controllers perform as well as a full nonlinear optimisation. Various extended Kalman filters give similar results, although the driver's sensory dynamics reduce control performance compared with full state feedback. The new model could be used to design vehicle systems which interact more naturally and safely with a human driver.

Most existing models of driver steering control do not consider the driver's sensory dynamics, despite many aspects of human sensory perception having been researched extensively. The authors recently reported the development of a driver model that incorporates sensory transfer functions, noise and delays. The present paper reports the experimental identification and validation of this model. An experiment was carried out with five test subjects in a driving simulator, aiming to replicate a real-world driving scenario with no motion scaling. The results of this experiment are used to identify parameter values for the driver model, and the model is found to describe the results of the experiment well. Predicted steering angles match the linear component of measured results with an average 'variance accounted for' of 98% using separate parameter sets for each trial, and 93% with a single fixed parameter set. The identified parameter values are compared with results from the literature and are found to be physically plausible, supporting the hypothesis that driver steering control can be predicted using models of human perception and control mechanisms. ARTICLE HISTORY

In previous work, a new model of driver steering control incorporating sensory dynamics was derived and used to explain the performance of drivers in a simulator with full-scale motion feedback. This paper describes further experiments investigating how drivers steer with conflicts between their visual and vestibular measurements, caused by scaling or filtering the physical motion of the simulator relative to the virtual environment. The predictions of several variations of the new driver model are compared with the measurements to understand how drivers perceive sensory conflicts. Drivers are found to adapt well in general, unless the conflict is large, in which case they ignore the physical motion and rely on visual measurements. Drivers make greater use of physical motion which they rate as being more helpful, achieving a better tracking performance. Sensory measurement noise is shown to be signal-dependent, allowing a single set of parameters to be found to fit the results of all the trials. The model fits measured linear steering behavior with an average “variance accounted for (VAF)” of 86%.

In earlier work, a driver model incorporating sensory dynamics was identified from driving simulator experiments involving random disturbances, random target paths and linear vehicle dynamics. In the present paper, the driver model and experiments are extended to include transient disturbances, discrete target paths and nonlinear vehicle dynamics. The predictions of the model are compared with measurements from the experiments. Simulator motion is found to have a significant beneficial effect on drivers' responses, giving faster driver reaction times and more successful disturbance rejection and path following. The driver model predicts the measured responses well. The model suggests that drivers are unable to develop an accurate internal model of motion cueing filters, perceiving phase and gain distortions introduced by filtering as disturbances. Drivers are found able to account for the time-varying operating point of a nonlinear vehicle. The driver model is also able to match the behaviour of experienced drivers near the friction limit of the tyres, however, further work is necessary to understand how an inaccurate internal model impedes the performance of less experienced drivers. The findings contribute new knowledge to the fields of driver simulation and motion cueing.

In previous work a linear model of driver steering control was developed which takes account of human sensory dynamics and limitations. In this paper various approaches to modelling a driver's control of a nonlinear vehicle are compared. In contrast to research focussed on modelling the optimal driver, the aim of this work is to develop a realistic model of driver steering behaviour. Simulations were run to compare various nonlinear state estimators and controllers. In general a trade-off was found between simulation time, which could also represent mental load, and controller performance. Experiments are planned to compare the results of these simulations against measured steering behaviour from human drivers.

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