Modelling the influence of sensory dynamics on linear and nonlinear driver steering control

Abstract

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.

Modelling visual-vestibular integration and behavioural adaptation in the driving simulator

“…The present model and simulation analyses, together with the existing empirical findings from slalom experiments, instead suggest a stronger account, whereby drivers make direct use of motion information as part of shaping their steering to reach their intended targets, also in the absence of any external perturbances or high-level adaptations of trajectory or speed. It is clear that existing multisensory models of drivers (Nash and Cole, 2018) and pilots (e.g., Mulder et al, 2013) also suggest this deep form of involvement of vestibular cues in control, but we are not aware of any prior analysis of empirical data providing support for it in driving.…”

“…1(c) is a combination of the models in (a) and (b), with the optimal cue integration being carried out as part of a Kalman filter. This type of scheme has been common in optimal control theoretic models of various sensorimotor control tasks (van der Kooij et al, 1999;van Beers et al, 2002;Franklin and Wolpert, 2011), and was recently applied by Nash and Cole (2018) in their multisensory optimal control model of driver steering. In these models, the Kalman filter makes use of knowledge of the own past control actions as well as internal forward models of the system being controlled, including the own sensory dynamics.…”

“…for a simulation of duration T , with N discrete samples, in which n discrete steering adjustments with amplitudes g i are applied, and with lateral positions of vehicle and desired path y k and y * k , respectively. This is a common form of cost function for optimal control theory models of steering (Plochl and Edelmann, 2007;Nash and Cole, 2018). The weighting parameters were set to k path = 1 and k steer = 10 to get similar magnitudes across both terms in typical simulations with the model.…”

“…The main alternative to the models tested here is the one proposed by Nash and Cole (2018). This model assumes optimal control in a more complete sense, with access to explicit internal models of the vehicle as well as of the sensory and neuromuscular dynamics.…”

Identification and validation of a driver steering control model incorporating human sensory dynamics

“…An active control task such as driving requires attention to be shared between the task itself and the perception of concurrent sensory stimuli, in contrast with passive perception tasks where the subject is concentrating solely on one sensory stimulus. Nash and Cole [12], building upon the work of Bigler [6], and upon a review of the literature [13], developed an improved driver model incorporating sensory dynamics. Preliminary analysis of this model was carried out [14] using published results from an experiment in a flight simulator [15] to validate the modelling approach for an aeroplane control task.…”