Steering toward a goal by equalizing taus

A Two-Point Visual Control Model of Steering

When steering down a winding road, drivers have been shown to use both near and far regions of the road for guidance during steering. We propose a model of steering that explicitly embodies this idea, using both a ‘near point’ to maintain a central lane position and a ‘far point’ to account for the upcoming roadway. Unlike control models that integrate near and far information to compute curvature or more complex features, our model relies solely on one perceptually plausible feature of the near and far points, namely the visual direction to each point. The resulting parsimonious model can be run in simulation within a realistic highway environment to facilitate direct comparison between model and human behavior. Using such simulations, we demonstrate that the proposed two-point model is able to account for four interesting aspects of steering behavior: curve negotiation with occluded visual regions, corrective steering after a lateral drift, lane changing, and individual differences.

Perceiving Possibilities for Action: On the Necessity of Calibration and Perceptual Learning for the Visual Guidance of Action

Tasks such as steering, braking, and intercepting moving objects constitute a class of behaviors, known as visually guided actions, which are typically carried out under continuous control on the basis of visual information. Several decades of research on visually guided action have resulted in an inventory of control laws that describe for each task how information about the sufficiency of one's current state is used to make ongoing adjustments. Although a considerable amount of important research has been generated within this framework, several aspects of these tasks that are essential for successful performance cannot be captured. The purpose of this paper is to provide an overview of the existing framework, discuss its limitations, and introduce a new framework that emphasizes the necessity of calibration and perceptual learning. Within the proposed framework, successful human performance on these tasks is a matter of learning to detect and calibrate optical information about the boundaries that separate possible from impossible actions. This resolves a long-lasting incompatibility between theories of visually guided action and the concept of an affordance. The implications of adopting this framework for the design of experiments and models of visually guided action are discussed.

Influence of optic flow on the control of heading and target egocentric direction during steering toward a goal

Although previous studies have shown that people use both optic flow and target egocentric direction to walk or steer toward a goal, it remains in question how enriching the optic flow field affects the control of heading specified by optic flow and the control of target egocentric direction during goal-oriented locomotion. In the current study, we used a control-theoretic approach to separate the control response specific to these two cues in the visual control of steering toward a goal. The results showed that the addition of optic flow information (such as foreground motion and global flow) in the display improved the overall control precision, the amplitude, and the response delay of the control of heading. The amplitude and the response delay of the control of target egocentric direction were, however, not affected. The improvement in the control of heading with enriched optic flow displays was mirrored by an increase in the accuracy of heading perception. The findings provide direct support for the claim that people use the heading specified by optic flow as well as target egocentric direction to walk or steer toward a goal and suggest that the visual system does not internally weigh these two cues for goal-oriented locomotion control.

Distributed control of multiple non-holonomic robots with sector vision and range-only measurements for target capturing with collision avoidance

We consider a team of autonomous kinematically controlled non-holonomic planar Dubins car-like vehicles. The team objective is to encircle a given target so that all vehicles achieve a common and pre-specified distance from it and are uniformly distributed over the respective circle, and the entire formation rotates around the target with a prescribed angular velocity. The robots do not communicate with each other and any central decision-maker. The sensing capacity of any vehicle is heavily restricted: It has access only to the distance to the target and to the distances to the companion vehicles that are in a given disc sector centered at the vehicle at hand; no robot can distinguish between its companions, and does not know their parameters. A distributed control law is proposed, and mathematically rigorous proofs of its non-local convergence as well as collision avoidance property are presented. The performance of the control law is illustrated by computer simulations and experiments with real robots.

Rapid recalibration based on optic flow in visually guided action

Action capabilities are always subject to limits. Whether on foot or in a vehicle, people can only move so fast, slow down so quickly, and turn so sharply. The successful performance of almost any perceptual-motor task requires actors to learn and continually relearn their ever-changing action capabilities. Such learning can be considered an example of perceptual-motor calibration. The present study includes two experiments designed to address basic questions about the nature of this calibration process. Subjects performed a simulated braking task, using a foot pedal to slow down to a stop in front of an obstacle in the path of motion. At one point in the experiment, the strength of the brake was increased or decreased unbeknownst to subjects, and behavior before and after the change in brake strength was analyzed for evidence of recalibration. Experiment 1 showed that actors rapidly recalibrate following a change in brake dynamics, even when they are unaware of the change. In Experiment 2, the scene turned black one second after braking was initiated. Subjects still recalibrated following the change in brake strength, suggesting that information in the sensory consequences of the initial brake adjustment is sufficient for recalibration, even in the absence of feedback about the outcome (i.e., in terms of final position error) of the task. Discussion focuses on the critical but often overlooked role of calibration in continuously controlled visually guided action, and the nature of the information used for recalibration.

Interaction with an immersive virtual environment corrects users' distance estimates

OBJECTIVE

Two experiments examined whether prior interaction within an immersive virtual environment (VE) enabled people to improve the accuracy of their distance judgments and whether an improved ability to estimate distance generalized to other means of estimating distances.

BACKGROUND

Prior literature has consistently found that users of immersive VEs underestimate distances by approximately 50%.

METHOD

In each of the two experiments, 16 participants viewed objects in an immersive VE and estimated their distance to them by means of blindfolded walking tasks before and after interacting with the VE.

RESULTS

The interaction task significantly corrected users' underestimation bias to nearly veridical. Differences between pre- and post-interaction mean distance estimation accuracy were large (d = 4.63), and significant (p < .001), and they generalized across response task.

CONCLUSION

This finding limits the generality of the underestimation effect in VEs and suggests that distance underestimation in VEs may not be a road block to the development of VE applications.

APPLICATION

Potential or actual applications of this research include the improvement of VE systems requiring accurate spatial awareness.

Calibration, Information, and Control Strategies for Braking to Avoid a Collision

This study explored visual control strategies for braking to avoid collision by manipulating information about speed of self-motion. Participants watched computer-generated displays and used a brake to stop at an object in the path of motion. Global optic flow rate and edge rate were manipulated by adjusting eye-height and ground-texture size. Stopping distance, initiation of braking, and the magnitude of brake adjustments were influenced by both optical variables, but global optic flow rate had a stronger effect. A new model is introduced according to which braking is controlled by keeping the perceived ideal deceleration, based in part on global optic flow rate, within a "safe" region between 0 and the maximum deceleration of the brake.

Controlling steering and judging heading: retinal flow, visual direction, and extraretinal information

The contribution of retinal flow (RF), extraretinal (ER), and egocentric visual direction (VD) information in locomotor control was explored. First, the recovery of heading from RF was examined when ER information was manipulated; results confirmed that ER signals affect heading judgments. Then the task was translated to steering curved paths, and the availability and veracity of VD were manipulated with either degraded or systematically biased RF. Large steering errors resulted from selective manipulation of RF and VD, providing strong evidence for the combination of RF, ER, and VD. The relative weighting applied to RF and VD was estimated. A point-attractor model is proposed that combines redundant sources of information for robust locomotor control with flexible trajectory planning through active gaze.

Behavioral dynamics of steering, obstacle avoidance, and route selection

The authors investigated the dynamics of steering and obstacle avoidance, with the aim of predicting routes through complex scenes. Participants walked in a virtual environment toward a goal (Experiment 1) and around an obstacle (Experiment 2) whose initial angle and distance varied. Goals and obstacles behave as attractors and repellers of heading, respectively, whose strengths depend on distance. The observed behavior was modeled as a dynamical system in which angular acceleration is a function of goal and obstacle angle and distance. By linearly combining terms for goals and obstacles, one could predict whether participants adopt a route to the left or right of an obstacle to reach a go (Experiment 3). Route selection may emerge from on-line steering dynamics, making explicit path planning unnecessary.