A Unifying Theory of Driver Perception and Steering Control on Straight and Winding Roads

Abstract

Novel driver support systems potentially enhance road safety by cooperating with the human driver. To optimize the design of emerging steering support systems, a profound understanding of driver steering behavior is required. This article proposes a new theory of driver steering, which unifies visual perception and control models. The theory is derived directly from measured steering data, without any a priori assumptions on driver inputs or control dynamics. Results of a human-in-the-loop simulator experiment are presented, in which drivers tracked the centerline of straight and winding roads. Multiloop frequency response function (FRF) estimates reveal how drivers use visual preview, lateral position feedback, and heading feedback for control. Classical control theory is used to model all three FRF estimates. The model has physically interpretable parameters, which indicate that drivers minimize the bearing angle to an "aim point" (located 0.25-0.75 s ahead) through simple compensatory control, both on straight and winding roads. The resulting unifying perception and control theory provides a new tool for rationalizing driver steering behavior, and for optimizing modern steering support systems.

Effects of Target Trajectory Bandwidth on Manual Control Behavior in Pursuit and Preview Tracking

“…First, the current models should be extended to capture HC behavior in a wider range of real-life control tasks. As a first step, we already adapted the quasi-linear preview model to capture driver steering on winding roads [27]. This extension explains how the near-and far-viewpoint preview responses relate to prevailing two-level theories of driver steering [27] (e.g., see Land and Horwood [28]).…”

“…As a first step, we already adapted the quasi-linear preview model to capture driver steering on winding roads [27]. This extension explains how the near-and far-viewpoint preview responses relate to prevailing two-level theories of driver steering [27] (e.g., see Land and Horwood [28]). Second, as noted by Mulder et al [1], the quasi-linear framework would benefit from an update to capture time-variations of the HC's behavior.…”

Drivers use active gaze to monitor waypoints during automated driving

“…Theories of the visual control of steering (derived from a broad literature base crossing vision science and engineering) suggest that points that are farther away in the visual scene will have different informational characteristics than closer points (e.g. 27 ). According to the literature, gaze time headway should affect steering control such that gaze directed closer to the driver will lead to more precise steering (i.e.…”

“…less positional error) but at the expense of steering smoothness 15 , 16 , 28 . The empirical data has been accompanied by models of steering control that use perceptual inputs that are sampled primarily from within the 1-3s time window 9 , 16 , 27 , 29 – 31 . The implication is that gaze fixations that land within the this time window serve an important role in obtaining the requisite information for controlling one’s trajectory, earning these fixations the label “Guiding Fixations” (GF; Fig.…”

Identification of Driving Characteristics Using Markov Process Model

“…Wireless sensing to collect biometrics of drivers to classify driver behavior is currently studied and machine learning methods are performed [7]. Frequency response function (FRF) estimates reveal how drivers use visual preview, lateral position feedback, and heading feedback for control [8].…”