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CV for Lateral Guidance: Lane Tracking

It is by now established practice to place between 3 and 10 small windows on the expected image plane locations of bright lines marking the left and right delimitation of a lane. Edge elements within each window are associated with the projection of the model lane border into the image plane (sometimes called a `model segment'). (Weighted) deviations between edge elements and associated model segments are usually fed into an Extended Kalman Filter (EKF) in order to update the parameters characterizing the current camera position and orientation - and thus, for a camera fixed to the vehicle, the vehicle coordinate system - with respect to the lane.

Automatic lateral control of road vehicles on highways based on such a CV approach is considered State-of-the-Art, both for continuous as well as for discontinuous lane markings. Essentially similar approaches have been used in order to detect lines delimiting adjacent highway lanes on either side of the one used by the camera-carrying vehicle itself. Up until recently, special purpose computers, configurations of Digital Signal Processors, or a network of standard processors have been required in order to achieve this in real-time. A standard, 1996 vintage VLSI CPU can roughly cope with the computations required for real-time tracking of a well-marked, reasonably illuminated highway lane.

Innercity roads frequently turn much more sharply than highways or rural roads: clothoid or extended parabolic arc models used for the latter can not be easily adapted to the more complicated conditions of innercity roads and intersections, in particular since significant parts of the road border may be occluded by, e. g., parking vehicles. As soon as more computing power can be made available within vehicles, model-based approaches are likely to be investigated for innercity lane detection and tracking. Similarly, the lane width is not yet routinely estimated from recorded video images as a variable parameter of a generic lane model. Roads are not restricted to planes, although for highways and larger roads outside cities and mountainous areas, these vertical curvatures are so small that they usually have been neglected.

Whether additional computing power will be used in order to refine road models as discussed above or rather to increase robustness of lane detection and tracking under more adverse operating conditions such as driving by night, during fog, heavy rain, or snow, will have to be seen.


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