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

Extended crossover model for human-control of fractional order plants

A data-driven generalization of the crossover model is proposed, characterizing the human control of systems with both integer and fractional-order plant dynamics. The model is developed and validated using data obtained from human subjects operating in compensatory and pursuit tracking tasks. From the model, it is inferred that humans possess a limited but consistent capability to compensate for fractional-order plant dynamics. Further, a review of potential sources of fractionality within such man–machine systems suggests that visual perception, based on visual cues that contain memory, and muscular dynamics are likely sources of fractional-order dynamics within humans themselves. Accordingly, a possible mechanism for fractional-order compensation, operating between visual and muscular sub-systems, is proposed. Deeper analysis of the data shows that human response is more highly correlated to fractional-order representations of visual cues, rather than directly to objective engineering variables, as is commonly proposed in human control models in the literature. These results are expected to underpin future design developments in human-in-the-loop cyber-physical systems, for example, in semi-autonomous highway driving

Modeling and Recognizing Driver Behavior Based on Driving Data: A Survey

In recent years, modeling and recognizing driver behavior have become crucial to understanding intelligence transport systems, human-vehicle systems, and intelligent vehicle systems. A wide range of both mathematical identification methods and modeling methods of driver behavior are presented from the control point of view in this paper based on the driving data, such as the brake/throttle pedal position and the steering wheel angle, among others. Subsequently, the driver’s characteristics derived from the driver model are embedded into the advanced driver assistance systems, and the evaluation and verification of vehicle systems based on the driver model are described

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 143

This supplement to Aerospace Medicine and Biology (NASA SP-7011) lists 251 reports, articles and other documents announced during June 1975 in Scientific and Technical Aerospace Reports (STAR) or in International Aerospace Abstracts (IAA). The first issue of the bibliography was published in July 1964; since that time, monthly supplements have been issued. In its subject coverage, Aerospace Medicine and Biology concentrates on the biological, physiological, and environmental effects to which man is subjected during and following simulated or actual flight in the earth's atmosphere or in interplanetary space. References describing similar effects of biological organisms of lower order are also included. Such related topics as sanitary problems, pharmacology, toxicology, safety and survival, life support systems, exobiology, and personnel factors receive appropriate attention. In general, emphasis is placed on applied research, but references to fundamental studies and theoretical principles related to experimental development also qualify for inclusion

Understanding and Modeling the Human Driver

This paper examines the role of the human driver as the primary control element within the traditional driver-vehicle system. Lateral and longitudinal control tasks such as path-following, obstacle avoidance, and headway control are examples of steering and braking activities performed by the human driver. Physical limitations as well as various attributes that make the human driver unique and help to characterize human control behavior are described. Example driver models containing such traits and that are commonly used to predict the performance of the combined driver-vehicle system in lateral and longitudinal control tasks are identified.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65021/1/MacAdam_2003 VSD Understanding and Modelling the Driver.pd

Modeling pilot interaction with automated digital avionics systems: Guidance and control algorithms for contour and nap-of-the-Earth flight

A collection of technical papers are presented that cover modeling pilot interaction with automated digital avionics systems and guidance and control algorithms for contour and nap-of-the-earth flight. The titles of the papers presented are as follows: (1) Automation effects in a multiloop manual control system; (2) A qualitative model of human interaction with complex dynamic systems; (3) Generalized predictive control of dynamic systems; (4) An application of generalized predictive control to rotorcraft terrain-following flight; (5) Self-tuning generalized predictive control applied to terrain-following flight; and (6) Precise flight path control using a predictive algorithm

Eleventh Annual Conference on Manual Control

Human operator performance and servomechanism analyses for manual vehicle control tasks are studied

Modeling Human Control Behavior in Command-following Tasks

Humans interact with a variety of complex dynamic systems on a daily basis. However, they are often the lesser understood component of human-in-the-loop (HITL) systems. In this dissertation, we present the results of two HITL experiments to investigate the control strategies that humans use when performing command-following tasks. The first experiment is designed to investigate the control strategies that humans use to interact with nonlinear dynamic systems. Two groups of human subjects interact with a dynamic system and perform a command-following task. One group interacts with a linear time-invariant (LTI) dynamic system and the other group interacts with a Wiener system, which consists of the same LTI dynamics cascaded with a static output nonlinearity. In the second experiment, we examine the impacts of a relaxed command-following control objective on the control strategies used by humans. Two groups of human subjects interact with the same dynamic system and perform a command-following task; however, the groups have different control objectives. One group\u27s control objective is to follow the reference command as closely as possible at all times, while the other group\u27s control objective is to follow the reference command with some allowable error. We develop and utilize a new subsystem identification (SSID) algorithm to model control behavior of the human subjects participating in these HITL experiments. This SSID algorithm can identify the feedback and feedforward controllers used by human subjects, and is applicable to both linear and nonlinear dynamic systems. The SSID results of the first experiment indicate that adaptive feedforward inversion is the main control strategy used by human subjects for both linear and nonlinear plants. The results of the second experiment suggest that not all the human subjects who are instructed to perform a relaxed command-following task adopt adaptive feedforward inversion as their primary control strategy. The control behavior of those human subjects contains significant nonlinearities, which cannot be captured by a LTI control model. We present a nonlinear feedforward control architecture that can model several aspects of their control behavior