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 whilst 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. Whilst two-point control models can capture many aspects of driver behaviour, 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 et al., 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 behaviours changed when steering along roads where the utility of far-road information varied. Whilst steering behaviours 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 Problem with Big Data: Operating on Smaller Datasets to Bridge the Implementation Gap

Big datasets have the potential to revolutionize public health. However, there is a mismatch between the political and scientific optimism surrounding big data and the public’s perception of its benefit. We suggest a systematic and concerted emphasis on developing models derived from smaller datasets to illustrate to the public how big data can produce tangible benefits in the long term. In order to highlight the immediate value of a small data approach, we produced a proof-of-concept model predicting hospital length of stay. The results demonstrate that existing small datasets can be used to create models that generate a reasonable prediction, facilitating health-care delivery. We propose that greater attention (and funding) needs to be directed toward the utilization of existing information resources in parallel with current efforts to create and exploit “big data.

Predicting the Effect of Surface Texture on the Qualitative Form of Prehension

Reach-to-grasp movements change quantitatively in a lawful (i.e. predictable) manner with changes in object properties. We explored whether altering object texture would produce qualitative changes in the form of the precontact movement patterns. Twelve participants reached to lift objects from a tabletop. Nine objects were produced, each with one of three grip surface textures (high-friction, medium-friction and low-friction) and one of three widths (50 mm, 70 mm and 90 mm). Each object was placed at three distances (100 mm, 300 mm and 500 mm), representing a total of 27 trial conditions. We observed two distinct movement patterns across all trials—participants either: (i) brought their arm to a stop, secured the object and lifted it from the tabletop; or (ii) grasped the object ‘on-the-fly’, so it was secured in the hand while the arm was moving. A majority of grasps were on-the-fly when the texture was high-friction and none when the object was low-friction, with medium-friction producing an intermediate proportion. Previous research has shown that the probability of on-the-fly behaviour is a function of grasp surface accuracy constraints. A finger friction rig was used to calculate the coefficients of friction for the objects and these calculations showed that the area available for a stable grasp (the ‘functional grasp surface size’) increased with surface friction coefficient. Thus, knowledge of functional grasp surface size is required to predict the probability of observing a given qualitative form of grasping in human prehensile behaviour

Object geometric properties friction-dependant functional grip area.

<p><i>Upper</i> Geometric variation in stimulus sizes: Grip surface width ‘A’, the distance between the spherical surface centre-points ‘B’ and support base width ‘C’ were varied as discussed in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032770#s2" target="_blank">Method</a> section. <i>Lower a)</i> Manually securing an object requires the frictional force to be greater than the tangential component of object weight at the interface between fingertip and object. A curved surface results in a normal reaction force direction (R_N) unique to the point at which the object is grasped. Fearing <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032770#pone.0032770-Fearing1" target="_blank">[14]</a> demonstrated that, for a stable grasp, the grip conditions should satisfy: tan⁻¹|F_t|/F_n−1μ or μF_n>|F_t|. For a stable lift, fingertip force should be applied within an angle of φ_s relative to the normal reaction force (R_N), where: φ_s = tan⁻¹μ_s. Extending this relationship in the direction of all tangential friction force directions generates a cone of friction of half-angle φ_s and cone angle ψ where: ψ = 2 φ_s. <i>b)</i> As force is applied to the curved surface at a distance d_LIM from the centreline of the radius, then the force is at an angle α to the surface normal. When α = φ_s the force lies at the limit of the cone of friction. An increase in d results in the force lying outside the cone of friction and unstable grasp. Thus φ_s, and d_LIM are linked to the coefficient of static friction μ_s such that an increase in μ_s extends the functional area which can be grasped to achieve a stable grasp.</p

Kinematic profiles for stop and ‘on-the-fly’ prehension movements.

<p><i>Upper</i> A velocity profile typical of a stop movement: 1, the hand is in the transport phase with the wrist IRED reaching peak velocity. 2, as the hand and fingers approach the object the hand velocity drops below the threshold velocity (Vth) and remains below threshold velocity or stops for a period (T_DW). 3, upon successful application of the grip, both the wrist and object markers move in unison as part of a second distinct movement. 4, movement complete – hand and object velocity tends to zero. Time to Peak Speed (tPS) is defined as the time between the wrist marker moving above Vth and achieving peak speed. Movement time is defined as the time elapsed between the wrist marker achieving Vth and the object marker achieving Vth, here represented in the stop movement scenario. <i>Lower</i> A velocity profile typical of a ‘fly-through’ movement: 1, the hand is in transport phase toward the object. 2, as the fingers contact the object, the wrist IRED velocity is maintained above the threshold velocity (Vth) as the object is gripped. 3, the hand and object continue to move in unison while the wrist IRED velocity remains above the threshold velocity. 4, movement complete, hand and object velocity tends to zero. Movement time is defined as the time elapsed between the wrist marker achieving Vth and the object marker achieving Vth, here represented in the fly-through movement scenario.</p

Proportion of ‘on-the-fly’ movements as a function of surface texture.

<p>The mean coefficient of static friction was 1.31, 0.76 and 0.44 for the high, medium and low friction object surface textures respectively (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032770#s2" target="_blank">Methods</a>).</p