A review of human sensory dynamics for application to models of driver steering and speed control.

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.This work was supported by the UK Engineering and Physical Sciences Research Council (EP/P505445/1) (studentship for Nash).This is the published version. It first appeared from Springer at http://dx.doi.org/10.1007/s00422-016-0682-x

Occupant–vehicle dynamics and the role of the internal model

With the increasing need to reduce time and cost of vehicle development there is increasing advantage in simulating mathematically the dynamic interaction of a vehicle and its occu- pant. The larger design space arising from the introduction of automated vehicles further increases the potential advantage. The aim of the paper is to outline the role of the internal model hypothesis in understanding and modelling occupant-vehicle dynamics, specifically the dynamics associated with direction and speed control of the vehicle. The internal model is the driver’s or passenger’s understanding of the vehicle dynamics and is thought to be employed in the perception, cognition and action processes of the brain. The internal model aids the estimation of the states of the vehicle from noisy sensory measurements. It can also be used to optimise cognitive control action by predicting the consequence of the action; thus model predictive control theory (MPC) provides a foundation for modelling the cognition process. The stretch reflex of the neuromuscular system also makes use of the prediction of the internal model. Extensions to the MPC approach are described which account for: interaction with an automated vehicle; robust control; intermittent control; and cognitive workload. Further work to extend understanding of occupant-vehicle dynamic interaction is outlined. This paper is based on a keynote presentation given by the author to the 13th International Symposium on Advanced Vehicle Control (AVEC) conference held in Munich, September 2016

Effect of transient event frequency content and scale on the human detection of road surface type

This paper describes two laboratory-based experiments which evaluate the effect of transient event frequency content and scale on the human detection of road surface type by means of steering wheel vibration. This study used steering wheel tangential direction acceleration time histories which had been measured in a mid-sized European automobile that was driven over two different types of road surface. The steering acceleration stimuli were manipulated by means of the mildly non-stationary mission synthesis (MNMS) algorithm in order to produce test stimuli which were selectively modified in terms of the number, and size, of transient vibration events they contained. Fifteen test participants were exposed to both unmanipulated and manipulated steering wheel rotational stimuli by means of a steering wheel vibration simulator. For each road surface type a total of 45 vibration test stimuli were presented to each participant. Each participant was asked to state, by means of a simple "yes" or "no" answer, whether each individual stimuli was from a road surface which was being presented in front of the simulator as a picture on a large board. Using Signal Detection Theory as the analytical framework the results were summarized by means of the detectability index d' and by means of receiver operating curve (ROC) points. Improvements of up to 20 percentage points in the rate of correct detection were achieved by means of selective manipulation of the steering vibration stimuli. The results suggested that no single setting of the MNMS algorithm proved optimal for both two road surface types that were investigated

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

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 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

Modelling the effect of sensory dynamics on a driver’s control of a nonlinear vehicle

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

Visuomotor control, eye movements, and steering : A unified approach for incorporating feedback, feedforward, and internal models

The authors present an approach to the coordination of eye movements and locomotion in naturalistic steering tasks. It is based on recent empirical research, in particular, on driver eye movements, that poses challenges for existing accounts of how we visually steer a course. They first analyze how the ideas of feedback and feedforward processes and internal models are treated in control theoretical steering models within vision science and engineering, which share an underlying architecture but have historically developed in very separate ways. The authors then show how these traditions can be naturally (re)integrated with each other and with contemporary neuroscience, to better understand the skill and gaze strategies involved. They then propose a conceptual model that (a) gives a unified account to the coordination of gaze and steering control, (b) incorporates higher-level path planning, and (c) draws on the literature on paired forward and inverse models in predictive control. Although each of these (a–c) has been considered before (also in the context of driving), integrating them into a single framework and the authors’ multiple waypoint identification hypothesis within that framework are novel. The proposed hypothesis is relevant to all forms of visually guided locomotion.Peer reviewe

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

It is well established that not only vision but also other sensory modalities affect drivers’ control of their vehicles, and that drivers adapt over time to persistent changes in sensory cues (for example in driving simulators), but the mechanisms underlying these behavioural phenomena are poorly understood. Here, we consider the existing literature on how driver steering in slalom tasks is affected by down-scaling of vestibular cues, and propose, for the first time, a computational model of driver behaviour that can, based on neurobiologically plausible mechanisms, explain the empirically observed effects, namely: decreased task performance and increased steering effort during initial exposure, followed by a partial reversal of these effects as task exposure is prolonged. Unexpectedly, the model also reproduced another previously unexplained empirical finding: a local optimum for motion down-scaling, where path-tracking is better than when one-to-one motion cues are available. Overall, our findings suggest that: (1) drivers make direct use of vestibular information as part of determining appropriate steering actions, and (2) motion down-scaling causes a yaw rate underestimation phenomenon, where drivers behave as if the simulated vehicle is rotating more slowly than it is. However, (3) in the slalom task, a certain degree of such underestimation brings a path-tracking performance benefit. Furthermore, (4) behavioural adaptation in simulated slalom driving tasks may occur due to (a) down-weighting of vestibular cues, and/or (b) increased sensitivity in timing and magnitude of steering corrections, but (c) seemingly not in the form of a full compensatory rescaling of the received vestibular input. The analyses presented here provide new insights and hypotheses about simulated driving and simulator design, and the developed models can be used to support research on multisensory integration and behavioural adaptation in both driving and other task domains

Using Driver Control Models to Understand and Evaluate Behavioral Validity of Driving Simulators

For a driving simulator to be a valid tool for research, vehicle development, or driver training, it is crucial that it elicits similar driver behavior as the corresponding real vehicle. To assess such behavioral validity, the use of quantitative driver models has been suggested but not previously reported. Here, a task-general conceptual driver model is proposed, along with a taxonomy defining levels of behavioral validity. Based on these theoretical concepts, it is argued that driver models without explicit representations of sensory or neuromuscular dynamics should be sufficient for a model-based assessment of driving simulators in most contexts. As a task-specific example, two parsimonious driver steering models of this nature are developed and tested on a dataset of real and simulated driving in near-limit, low-friction circumstances, indicating a clear preference of one model over the other. By means of closed-loop simulations, it is demonstrated that the parameters of this preferred model can generally be accurately estimated from unperturbed driver steering data, using a simple, open-loop fitting method, as long as the vehicle positioning data are reliable. Some recurring patterns between the two studied tasks are noted in how the model’s parameters, fitted to human steering, are affected by the presence or absence of steering torques and motion cues in the simulator

A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles

Vehicle to Vehicle (V2V) communication has a great potential to improve reaction accuracy of different driver assistance systems in critical driving situations. Cooperative Adaptive Cruise Control (CACC), which is an automated application, provides drivers with extra benefits such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as cutting-into the CACC platoons by interfering vehicles or hard braking by leading cars. To address this problem, a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme is proposed in the first part of this paper. Next, a probabilistic framework is developed in which the cut-in probability is calculated based on the output of the mentioned cut-in prediction block. Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed which incorporates this cut-in probability to enhance its reaction against the detected dangerous cut-in maneuver. The overall system is implemented and its performance is evaluated using realistic driving scenarios from Safety Pilot Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I

Modelling the effect of sensory dynamics on a driver’s control of a nonlinear vehicle

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