Judgments of path, not heading, guide locomotion

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

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.

The Relationship between Saccades and Locomotion

Human locomotion involves a complex interplay among multiple brain regions and depends on constant feedback from the visual system. We summarize here the current understanding of the relationship among fixations, saccades, and gait as observed in studies sampling eye movements during locomotion, through a review of the literature and a synthesis of the relevant knowledge on the topic. A significant overlap in locomotor and saccadic neural circuitry exists that may support this relationship. Several animal studies have identified potential integration nodes between these overlapping circuitries. Behavioral studies that explored the relationship of saccadic and gait-related impairments in normal conditions and in various disease states are also discussed. Eye movements and locomotion share many underlying neural circuits, and further studies can leverage this interplay for diagnostic and therapeutic purposes.

When flow is not enough: evidence from a lane changing task

Humans are able to estimate their heading on the basis of optic flow information and it has been argued that we use flow in this way to guide navigation. Consistent with this idea, several studies have reported good navigation performance in flow fields. However, one criticism of these studies is that they have generally focused on the task of walking or steering towards a target, offering an additional, salient directional cue. Hence, it remains a matter of debate as to whether humans are truly able to control steering in the presence of optic flow alone. In this study, we report a set of maneuvers carried out in flow fields in the absence of a physical target. To do this, we studied the everyday task of lane changing, a commonplace multiphase steering maneuver which can be conceptualized without the need for a target. What is more (and here is the crucial quirk), previous literature has found that in the absence of visual feedback, drivers show a systematic, asymmetric steering response, resulting in a systematic final heading error. If optic flow is sufficient for controlling navigation through our environment, we would expect this asymmetry to disappear whenever optic flow is provided. However, our results show that this asymmetry persisted, even in the presence of a flow field, implying that drivers are unable to use flow to guide normal steering responses in this task.

The need for speed: global optic flow speed influences steering

How do animals follow demarcated paths? Different species are sensitive to optic flow and one control solution is to maintain the balance of flow symmetry across visual fields; however, it is unclear whether animals are sensitive to changes in asymmetries when steering along curved paths. Flow asymmetries can alter the global properties of flow (i.e. flow speed) which may also influence steering control. We tested humans steering curved paths in a virtual environment. The scene was manipulated so that the ground plane to either side of the demarcated path produced larger or smaller asymmetries in optic flow. Independent of asymmetries and the locomotor speed, the scene properties were altered to produce either faster or slower globally averaged flow speeds. Results showed that rather than being influenced by changes in flow asymmetry, steering responded to global flow speed. We conclude that the human brain performs global averaging of flow speed from across the scene and uses this signal as an input for steering control. This finding is surprising since the demarcated path provided sufficient information to steer, whereas global flow speed (by itself) did not. To explain these findings, existing models of steering must be modified to include a new perceptual variable: namely global optic flow speed.

Development of Automatic Steering System by Modeling Human Behavior Based on Optical Flow

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270002/03.jpg"" width=""300"" /> Manipulated optical flow field</div> Recently, various driving support systems have been developed to improve safety. However, because drivers occasionally feel that something is wrong, systems need to be designed based on information that drivers perceive. Therefore, we focused on optical flow, which is one of the visual information used by humans to improve driving feel. Humans are said to perceive the direction of self-motion from optical flow and also utilize it during driving. Applying the optical flow model to automatic steering systems, a human-oriented system might be able to be developed. In this paper, we derive the focus of expansion (FOE) in the frame of a camera that is the direction of self-motion in optical flow and propose a nonlinear control method based on the FOE. The effectiveness of the proposed method was verified through a vehicle simulation, and the results showed that the proposed method simulates human behavior. Based on these results, this approach may serve as a foundation of human-oriented system designs. </span>

Displaying optic flow to simulate locomotion: Comparing heading and steering

Optic flow can be used by humans to determine their direction of heading as well as controlling steering. Dot-flow displays have been widely used to investigate these abilities but it is unclear whether photorealistic textures would provide better information for controlling high-speed steering. Here, we examine the accuracy of heading judgements from dot-flow displays of different densities and luminance and then compare to a scene containing a textured ground. We then examine steering behaviour using these same displays to determine whether accurate heading conditions necessarily equate to successful steering. Our findings suggest that the bright dense dot-flow displays led to equivalent performance as the ground texture when judging heading, and this was also true when steering. The intermediate dot-flow conditions (with fewer and faded dots) revealed that some conditions that led to accurate heading judgements were insufficient for accurate steering. It seems, therefore, that heading perception should not be considered synonymous with successful steering control, and displays that support one ability will not necessarily support the other.

A representation of changing heading direction in human cortical areas pVIP and CSv

When we move around in the environment, we continually change direction. Much work has examined how the brain extracts instantaneous direction of heading from optic flow but how changes in heading are encoded is unknown. Change could simply be inferred cognitively from successive instantaneous heading values, but we hypothesize that heading change is represented as a low-level signal that feeds into motor control with minimal need for attention or cognition. To test this, we first used functional MRI to measure activity in several predefined visual areas previously associated with processing optic flow (hMST, hV6, pVIP, and CSv) while participants viewed flow that simulated either constant heading or changing heading. We then trained a support vector machine (SVM) to distinguish the multivoxel activity pattern elicited by rightward versus leftward changes in heading direction. Some motion-sensitive visual cortical areas, including hMST, responded well to flow but did not appear to encode heading change. However, visual areas pVIP and, particularly, CSv responded with strong selectivity to changing flow and also allowed direction of heading change to be decoded. This suggests that these areas may construct a representation of heading change from instantaneous heading directions, permitting rapid and accurate preattentive detection and response to change.

Path curvature discrimination: dependence on gaze direction and optical flow speed

Many experimental approaches to the control of steering rely on the tangent point (TP) as major source of information. The TP is a good candidate to control self-motion. It corresponds to a singular and salient point in the subject's visual field, and its location depends on the road geometry, the direction of self-motion relative to the road and the position of the driver on the road. However, the particular status of the TP in the optical flow, as a local minimum of flow speed, has often been left aside. We therefore assume that the TP is actually an optimal location in the dynamic optical array to perceive a change in the trajectory curvature. In this study, we evaluated the ability of human observers to detect variations in their path curvature from optical flow patterns, as a function of their gaze direction in a virtual environment. We simulated curvilinear self-motion parallel to a ground plane. Using random-dot optic flow stimuli of brief duration and a two-alternative forced-choice adaptive procedure, we determined path curvature discrimination thresholds, as a function of gaze direction. The discrimination thresholds are minimal for a gaze directed toward a local minimum of optical flow speed. A model based on Weber fraction of the foveal velocities () correctly predicts the relationship between experimental thresholds and local flow velocities. This model was also tested for an optical flow computation integrating larger circular areas in central vision. Averaging the flow over five degrees leads to an even better fit of the model to experimental thresholds. We also found that the minimal optical flow speed direction corresponds to a maximal sensitivity of the visual system, as predicted by our model. The spontaneous gazing strategies observed during driving might thus correspond to an optimal selection of relevant information in the optical flow field.

Visuospatial perception and navigation in Parkinson's disease

A shifted field of view, an altered perception of optic flow speed, and gait asymmetries may influence heading direction in Parkinson's disease (PD). PD participants (left body-side onset, LPD, n=14; right body-side onset, RPD, n=9) and Healthy Control participants (n=17) walked a virtual hallway in which the optic flow speeds of the walls varied. Three-dimensional kinematics showed participants veered away from the faster moving wall. Although veering normally occurs toward the side with smaller step length, in both LPD and RPD this bias was overridden by a shifted field of view, which caused veering in the opposite direction, toward the side of the brain with more basal ganglia damage.

Monkey steering responses reveal rapid visual-motor feedback

The neural mechanisms underlying primate locomotion are largely unknown. While behavioral and theoretical work has provided a number of ideas of how navigation is controlled, progress will require direct physiolgical tests of the underlying mechanisms. In turn, this will require development of appropriate animal models. We trained three monkeys to track a moving visual target in a simple virtual environment, using a joystick to control their direction. The monkeys learned to quickly and accurately turn to the target, and their steering behavior was quite stereotyped and reliable. Monkeys typically responded to abrupt steps of target direction with a biphasic steering movement, exhibiting modest but transient overshoot. Response latencies averaged approximately 300 ms, and monkeys were typically back on target after about 1 s. We also exploited the variability of responses about the mean to explore the time-course of correlation between target direction and steering response. This analysis revealed a broad peak of correlation spanning approximately 400 ms in the recent past, during which steering errors provoke a compensatory response. This suggests a continuous, visual-motor loop controls steering behavior, even during the epoch surrounding transient inputs. Many results from the human literature also suggest that steering is controlled by such a closed loop. The similarity of our results to those in humans suggests the monkey is a very good animal model for human visually guided steering.

Using vision to control locomotion: looking where you want to go

Looking at the inside edge of the road when steering a bend seems to be a well-established strategy linked to using a feature called the tangent point. An alternative proposal suggests that the gaze patterns observed when steering result from looking at the points in the world through which one wishes to pass. In this explanation fixation on or near the tangent point results from trying to take a trajectory that cuts the corner. To test these accounts, we recorded gaze and steering when taking different paths along curved roadways. Participants could gauge and maintain their lateral distance, but crucially, gaze was predominantly directed to the region proximal to the desired path rather than toward the tangent point per se. These results show that successful control of high-speed locomotion requires fixations in the direction you want to steer rather than using a single road feature like the tangent point.

Cortical dynamics of navigation and steering in natural scenes: Motion-based object segmentation, heading, and obstacle avoidance

Visually guided navigation through a cluttered natural scene is a challenging problem that animals and humans accomplish with ease. The ViSTARS neural model proposes how primates use motion information to segment objects and determine heading for purposes of goal approach and obstacle avoidance in response to video inputs from real and virtual environments. The model produces trajectories similar to those of human navigators. It does so by predicting how computationally complementary processes in cortical areas MT(-)/MSTv and MT(+)/MSTd compute object motion for tracking and self-motion for navigation, respectively. The model's retina responds to transients in the input stream. Model V1 generates a local speed and direction estimate. This local motion estimate is ambiguous due to the neural aperture problem. Model MT(+) interacts with MSTd via an attentive feedback loop to compute accurate heading estimates in MSTd that quantitatively simulate properties of human heading estimation data. Model MT(-) interacts with MSTv via an attentive feedback loop to compute accurate estimates of speed, direction and position of moving objects. This object information is combined with heading information to produce steering decisions wherein goals behave like attractors and obstacles behave like repellers. These steering decisions lead to navigational trajectories that closely match human performance.

Neural systems in the visual control of steering

Visual control of locomotion is essential for most mammals and requires coordination between perceptual processes and action systems. Previous research on the neural systems engaged by self-motion has focused on heading perception, which is only one perceptual subcomponent. For effective steering, it is necessary to perceive an appropriate future path and then bring about the required change to heading. Using function magnetic resonance imaging in humans, we reveal a role for the parietal eye fields (PEFs) in directing spatially selective processes relating to future path information. A parietal area close to PEFs appears to be specialized for processing the future path information itself. Furthermore, a separate parietal area responds to visual position error signals, which occur when steering adjustments are imprecise. A network of three areas, the cerebellum, the supplementary eye fields, and dorsal premotor cortex, was found to be involved in generating appropriate motor responses for steering adjustments. This may reflect the demands of integrating visual inputs with the output response for the control device.