Towards a complete human driver model: The effect of vision on driving performance

The detection of impending collisions and the subsequent choice and regulation of maneuvers to deal with them are important tasks for automobile drivers. In carrying out these activities, drivers rely chiefly (oftentimes exclusively) upon their visual systems. To date, however, this aspect of a driver's performance has largely been neglected, with the result that human driver models often incorporate visual components based upon idealized behavior and in some cases questionable assumptions. If the visual component is incorrect, the entire model will to some degree be compromised. In this study we begin to address this deficiency by quantifying the visual system's ability to detect and track a vehicle's approach, as represented by the rate of change of the angle 0 that its image subtends on the retina of the eye. The shortcomings of human driver models based upon current assumptions are discussed, and the development of improved models based the dynamic response characteristics of the visual system and the principles of signal detection are described. To focus our efforts we have assumed a fairly constrained driving scenario (the "Lead Vehicle Braking" scenario), but these results are applicable to any scenario (automotive or not) in which the observer has an unobstructed view of the approach of an object or stationary obstacle.