Lane detection is an important component of driver assistance systems that are used, e.g., for longitudinal control or for lane keeping.
Known lane detection systems are based on assigning the detected structures to the side of the lane and the distance thereof from the middle of the lane. The difficulty of said assignment increases with an increasing distance of the measuring system from the structures. Furthermore, the description of the detected lane usually refers to the position of the vehicle and does not provide any explicit information about the far range. The usual lane model uses the curvature and the curvature change of a clothoid model to describe the further course of the road, wherein the estimated values are an averaging relative to the ego-position of the vehicle. Therefore, the known methods are not capable of determining the exact point of transition between two clothoid segments since roads are created by stringing clothoid segments together. For example, the transition from a bend to a straight line cannot be determined precisely, which results in an unnatural control behavior of a vehicle lateral control system of a driver assistance system at these points of transition.
According to U.S. Pat. No. 6,718,259 B1, which is incorporated by reference, this problem is solved by supplying the surroundings data generated by a sensor system to a filter bank made up of several Kalman filters, wherein the lane model is based on a clothoid model in which the road region in front of the vehicle is subdivided into a near range (up to a distance d1) and a far range (distance from d1 to d2) having different clothoid parameters, wherein a continuous transition between these two ranges is assumed. Thus, a point of transition between two clothoid segments of the road is not estimated, but the transition point is assumed to be at distance d1. Each of the individual Kalman filters of the filter bank is adapted to a lane model, which lane models differ from each other with respect to the position of the transition point relative to distance d1. Each Kalman filter of this filter bank provides an estimate of the lane model parameters of the respective model, wherein each of these estimated values is subsequently weighted with a weighting value that corresponds to the probability of the occurrence of the respective model. The weighted output data are merged.