Steering bends and changing lanes: The impact of optic flow and road edges on two point steering control

Abstract

Successful driving involves steering corrections that respond to immediate positional errors while also anticipating upcoming changes to the road layout ahead. In popular steering models these tasks are often treated as separate functions using two points: the near region for correcting current errors, and the far region for anticipating future steering requirements. Whereas two-point control models can capture many aspects of driver behavior, the nature of perceptual inputs to these two "points" remains unclear. Inspired by experiments that solely focused on road-edge information (Land & Horwood, 1995), two-point models have tended to ignore the role of optic flow during steering control. There is recent evidence demonstrating that optic flow should be considered within two-point control steering models (Mole, Kountouriotis, Billington, & Wilkie, 2016). To examine the impact of optic flow and road edges on two-point steering control we used a driving simulator to selectively and systematically manipulate these components. We removed flow and/or road-edge information from near or far regions of the scene, and examined how behaviors changed when steering along roads where the utility of far-road information varied. While steering behaviors were strongly influenced by the road-edges, there were also clear contributions of optic flow to steering responses. The patterns of steering were not consistent with optic flow simply feeding into two-point control; rather, the global optic flow field appeared to support effective steering responses across the time-course of each trajectory.

New Measures of Visual Scanning Efficiency and Cognitive Effort

“…The central consideration of this is to place the most importance on the perceived motion variations employed for the completion of visual scanning and of driving. That is, motion variation, which always and naturally happens in driving environments [24], is taken as a motivation force for visuo-motor behavior and for driving activity. And, the motion variation perceived for visual scanning during the task of driving indicates the top-down modulation exerted by driver to fulfill the task.…”

“…The probability distribution of fixations, namely the fixation distribution, which is constructed based on the normalized histogram of fixation locations in a 3D environment, is used as a basis for understanding the mechanism of visual scanning in this paper. In addition, the optical flow, which effectively reflects a motion variation resulted from a dynamic stimulus [23], always happens in driving situations [24]. In this paper, the optical flows induced by fixations are detected and used to establish the optical flow probability distribution.…”

“…And then a divergence between fixation distribution and optical flow distribution is proposed to define the visual scanning efficiency. Notice that, optical flow represents a vector, according to its original definition [17] [18]; but, for the purpose of this paper, unless otherwise specified, optical flow is always used for representing its magnitude.…”

A Jensen-Shannon Divergence Driven Metric of Visual Scanning Efficiency Indicates Performance of Virtual Driving

“…The cen-tral consideration of this is to place the most importance on the perceived visual motions employed for the completion of visual scanning and of driving. That is, visual motion, which always and naturally happens in driving environments [12], is taken as a bottom-up motivation force for visuo-motor behavior and for driving activity. And, the visual motion perceived for visual scanning during the task of driving is obtained from the top-down modulation exerted by driver to fulfill the task.…”

“…The probability distribution of fixations, namely the fixation distribution, which is constructed based on the normalized histogram of fixation locations in a 3D environment, is used as a basis for understanding the mechanism of visual scanning in this pa-per. In addition, the optical flow, which effectively reflects a visual motion resulted from a dynamic stimulus [11], always happens in driving situations [12]. In this paper, the optical flows induced by fixations are detected and used to establish the optical flow probability distribution.…”