Automated-Manual Transitions: Human Capabilities and Adaptive Cruise Control

ITS innovations in California are likely to include automated systems for vehicle guidance. Such systems will supplant manual controls during certain types of vehicle operation. However, the alternative manual control must remain intact in the vehicle. Thus, epochs of automated-manual transition (A-MT) are inevitable. The problem is how to characterize a given transition type and then how to optimize it. In this study we examined one of the several predictable transitions that use of the Adaptive Cruise Control (ACC) will lead to. This predictable A-MT is the event that would ensue when a lead vehicle (LV) suddenly brakes maximally and a following vehicle (FV) under the control of ACC must react appropriately (hereafter referred to as the LV Braking Scenario). Such an event is neither unlikely nor is it benign. It can be shown that within the current design of ACC, this event will lead to a collision. Collision avoidance is possible but only if the human operator (HO) of the FV assumes manual control in a timely way. In TO 4221 the conditions required for a graceful A-MT in this scenario were investigated. We focused attention on two features of the HO, those visual capabilities required by the HO to determine the need to assume manual control and also those features of an in-vehicle warning signal (initiated by either vehicle) that could reliably prompt appropriate HO action. This was accomplished in the context of three tasks.

[1]  Kenneth R. Boff,et al.  Engineering data compendium : human perception and performance , 1988 .

[2]  J. Swets,et al.  A decision-making theory of visual detection. , 1954, Psychological review.

[3]  O. Braddick,et al.  Serial Search for Targets Defined by Divergence or Deformation of Optic Flow , 1991, Perception.

[4]  M Sivak,et al.  Reaction times to neon, LED, and fast incandescent brake lamps. , 1994, Ergonomics.

[5]  P. Bennett,et al.  Deriving behavioural receptive fields for visually completed contours , 2000, Current Biology.

[6]  D Regan,et al.  Accuracy of estimating time to collision using binocular and monocular information , 1998, Vision Research.

[7]  D. Regan,et al.  Looming detectors in the human visual pathway , 1978, Vision Research.

[8]  D. Regan,et al.  Visual perception of changing size: The effect of object size , 1979, Vision Research.

[9]  D. Regan,et al.  Visual processing of looming and time to contact throughout the visual field , 1995, Vision Research.

[10]  J. F. Brown The visual perception of velocity , 1931 .

[11]  A. Sekuler Simple-pooling of unidirectional motion predicts speed discrimination for looming stimuli , 1992, Vision Research.

[12]  David Regan,et al.  Visual Processing of the Motion of an Object in Three Dimensions for a Stationary or a Moving Observer , 1995, Perception.

[13]  G J Andersen,et al.  Speed, size, and edge-rate information for the detection of collision events. , 1999, Journal of experimental psychology. Human perception and performance.

[14]  H W Leibowitz,et al.  Grade Crossing Accidents and Human Factors Engineering , 1985 .

[15]  D. Regan,et al.  Visual processing of four kinds of relative motion , 1986, Vision Research.

[16]  K. Hoffmann,et al.  Optic flow and eye movements. , 2000, International review of neurobiology.

[17]  William Epstein,et al.  Does retinal size have a unique correlate in perceived size? , 1969 .

[18]  P. Artes,et al.  Response time as a discriminator between true- and false-positive responses in suprathreshold perimetry. , 2002, Investigative ophthalmology & visual science.

[19]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .

[20]  E. Hoffmann Note on Detection of Vehicle Velocity Changes , 1966, Human factors.

[21]  Albert J. Ahumada,et al.  Technique to extract relevant image features for visual tasks , 1998, Electronic Imaging.

[22]  D. Regan,et al.  Texture changes versus size changes as stimuli for motion in depth , 1983, Vision Research.

[23]  J. F. Brown The thresholds for visual movement , 1931 .

[24]  Tony Greenfield,et al.  Theory and Problems of Probability and Statistics , 1982 .

[25]  Errol R. Hoffmann,et al.  Scaling of Relative Velocity between Vehicles , 1994 .

[26]  W Schiff,et al.  Information Used in Judging Impending Collision , 1979, Perception.

[27]  William Epstein,et al.  Size and distance judgments under reduced conditions of viewing , 1969 .

[28]  W H Warren,et al.  Visual control of braking: a test of the tau hypothesis. , 1995, Journal of experimental psychology. Human perception and performance.

[29]  BRYAN L. GROS,et al.  Relative Efficiency for the IDetectionof Apparent Motion* , 1997 .

[30]  W. Gogel,et al.  Perceived size and motion in depth from optical expansion , 1986, Perception & psychophysics.

[31]  David Whitaker,et al.  Non-veridical size perception of expanding and contracting objects , 1999, Vision Research.

[32]  David W. Moore,et al.  Historical development and current effectiveness of rear lighting systems , 1999 .

[33]  M T Turvey,et al.  Optical information about the severity of upcoming contacts. , 1993, Journal of experimental psychology. Human perception and performance.