Negotiating cars through bends relies on a combination of speed, steering and position in the lane, with speed and steering being the controllable actions and the position on the lane the resultant variable. For the control of steering, two major currently discussed strategies are orientation to the tangent point and gaze sampling.
The tangent point method for negotiating bends relies on the simple geometrical fact that the bend radius (and hence the required steering angle) relates in a simple fashion to the visible angle between the momentary heading direction of the car and the tangent point (Land & Lee,
1994). The tangent point is the point of the inner lane marking (or the boundary between the asphalted road and the adjacent green) bearing the highest curvature in the 2D retinal image, or in other terms, the innermost point of this boundary (
Figure 1A). Drivers can easily use this strategy by looking at the tangent point and inferring the to-be-adopted steering angle from the rotation angle of their gaze and head in an open-loop fashion. If the driver steers correctly, the tangent point will stay in its retinal position. Additionally, the driver may then further use the tangent point in the manner of a closed-loop controller. If he under or oversteers the car, the tangent point will deviate from the fovea. The driver can then adjust the steering until the tangent point is again in the desired position relative to his gaze direction.
Driving by the tangent point has been observed from both normal and racing drivers in real-world scenarios by Land and Lee (
1994), Land and Tatler (
2001), Chattington, Wilson, Ashford, and Marple-Horvat (
2007), as well as in simulated races (Wilson, Chattington, & Marple-Horvat,
2008). The tangent point has further been noted as one of three possible attractors (‘far points’) in a recent model of car driving (Salvucci & Gray,
2004).
The alternative gaze-sampling method relies on retinal flow information (Wann & Land,
2000; Wann & Swapp,
2000). As an observer moves through an environment of visual objects, the representation of these objects on the retina changes with the movement, resulting in the retinal flow. The exact flow of each object depends on a number of parameters like the momentary heading direction and speed of the driver (i.e. his car), the depth structure of the environment and whether objects are static or move themselves. Heading, depth structure and independently moving objects can be derived from the optic flow by computational algorithms (Lee,
1980; Longuet-Higgins & Prazdny,
1980; Pauwels & van Hulle,
2004) and by human observers (Lappe, Bremmer, & van den Berg,
1999; Rogers & Graham,
1979; Rushton, Bradshaw, & Warren,
2007; Warren & Hannon,
1988). Thus, from a combination of the momentary heading direction obtained from the flow and a high level representation of the street layout, it would be possible to decide whether one's car is on the track (Warren,
1998). But gaze sampling relies on much more basal information and thus avoids the more extensive computation of heading and scene structure as well as the balancing between them.
The cardinal idea in gaze sampling is that the observer's movement through the environment produces retinal flow lines and that these and especially their straightness or curvature can be determined by higher-order detectors (Wann & Land,
2000; Wann & Swapp,
2000; Wilkie & Wann,
2003b).
Using these flow lines for bend driving requires that the scene points before the driver lie in a plane, that observer first fixates a point on his intended path on that plane (cf.
Figure 1B and insets), and then tracks that point for a short time interval. If he steers correctly, straight retinal flow lines will emerge. In contrast, if he understeers then flow lines will be curved out of the bend (away from the fixation point), whereas flow lines curving into the bend will result if he oversteers (Wann & Land,
2000). Thus, the curvature of the flow lines provides a visual signal of steering correctness.
In order to use the gaze sampling method on winding roads, drivers have to fixate a spot on their future path (i.e. optimally in the middle of the lane) and track it for some time as they approach it. When it comes too near to the front end of their car to be comfortably fixated any further, drivers will look for a new point to track. For the periods of tracking the curvature of the flow lines has then to be assessed.
Wilkie and Wann (
2003a) proposed that the visual system is able to distinguish between straight, left and right-curved flow lines and that observers are able to use the strength of the curvature to correct steering maneuvers accordingly. They placed subjects in a virtual environment and instructed them to negotiate a car through winding roads by using the gaze-sampling method. In that virtual environment and with a moderate speed, subjects succeeded in driving that course safely (Robertshaw & Wilkie,
2008; Wilkie & Wann,
2003a).
In a study with real driving, we directly compared driving quality when using the gaze-sampling and when using the tangent point technique (Kandil, Rotter, & Lappe,
2009). We argued that flow lines obtained during driving on real streets are not as robust as in virtual reality because the car on the street, the driver in the car and the driver's head on his body are all moving irregularly due to the vibrations of the car and the unevenness of the street. Since these factors may well add substantial errors to the estimation of both position and vector of the motion information and may hence confound the mechanisms responsible for the distinction between straight and curved flow lines, we considered it worthwhile to investigate whether gaze sampling is used at all in successful bend negotiation, and if so with what precision. We conducted our experiment on the inner loops of cloverleaf motorway junctions, that is on fairly tight right-hand bends (radius of curvature ≈60 m).
1 These are closed bends, meaning that drivers can neither see their end point nor cut them short. We found that our subjects did not use gaze-sampling on their own and, if instructed to do so, drove less stably and not as smoothly as when using the tangent point. Our results thus contradict Robertshaw and Wilkie's (
2008) results, who found that reliance on the tangent point was neither extensive nor significantly beneficial for the smoothness of the driving when they tested a similar strategy with subjects driving in a car-simulator.
It has been argued (Kandil et al.,
2009; Robertshaw & Wilkie,
2008; Wilkie & Wann,
2003a) that next to the obvious difference of real-road vs. simulated driving, the parameters of the roads may play a role, that is whether the curvature is high or low and whether the bend is open or closed.
In the present study we take the idea of regarding open vs. closed bends and curvature a step further by defining openness as an entity. Imagine a driver approaching a curve. In order to negotiate the bend, he has primarily to steer the car according to the needs of the curve and to check the road surface for possible obstacles. Thus the most important part of the road is the section between his car and the point at which in his view the road surface of his own lane ends. (One may argue that he will be able to also see more distant parts of the road if he looks further off to the left or right. But in this case, he will lose sight contact to the primarily interesting gaze area. In correspondence with this view, we (Kandil et al.,
2009) observed that these glances to the future path of the road are performed from time to time but that the driver's eye then quickly returns to the road section before him). We now define openness as the sight distance between the driver's eye and the subjective end point of his road lane. From geometrical considerations, it is clear that openness increases with increasing curve radius (i.e. with decreasing curvature). Further (as illustrated in
Figures 1C and
1D) in right-hand traffic, openness is larger in left than for right-hand curves (L1 and L2 are longer than R1 and R2) and for leaving as compared to entering sections (L2 and R2 are larger than L1 and R1) of otherwise identical roads. Additionally, it is striking that for the number of bends tested here, R2 is of approximately the same length as L1.
In this study, we test whether and to what extent openness predicts the driver's eye-related behavior. To this aim, we selected three test courses, each with a series of bends of differing curvature, and analyzed the eye-movements of a set of drivers separately for entering and leaving segments of left and right-hand bends of different curvature.