The present invention relates to a method and a device for driver support, e.g., for support when changing lanes.
Driver assistance systems help the driver react quickly and correctly in critical situations. Examples include, e.g., lane departure warning systems (LDW), which support the driver during lateral guidance of the vehicle. In such assistance systems, a change of lane is recognized, and a reaction takes place at least in the case of an unintentional change of lane on the part of the driver. German Published Patent Application No. 102 38 215, for example, describes procedures with the aid of which unintentional and intentional lane changes are distinguished from one another. Such a distinction also plays a part in connection with other driver assistance systems, e.g., adaptive vehicle speed regulating systems (Adaptive Cruise Control: ACC).
Through the turning on of a signal indicating the change of lane, for example, a blinker, in the case of intentional lane changes, other drivers in traffic are alerted of the intended lane change, thus increasing overall traffic safety.
In addition, the driver assistance system may achieve an increase in driving comfort, because the driver no longer has to indicate his desire to change lanes by turning on the blinker. This task is taken over by the driver assistance system.
The turning on of the vehicle's directional indicator by the driver assistance system may take place only if the directional indicator has not already been turned on by the driver.
After the change of lane has been completed, the directional indicator may be deactivated, if this has not been done by the driver himself.
Further features and aspects of example embodiments of the present invention are described in more detail below in the following description with reference to the appended Figures.
In
Using image analysis methods, in the exemplary embodiment of the lane departure warning system image data supplied by the image sensor system concerning the scene in front of the vehicle are used to determine lane data that represent the course and the size of the lane. Thus, for example, the lane edge markings (left and/or right lane edge) are acquired, and the course of the respective lane edge is approximated, for example, as a polynomial (third-order power function). In addition, the course of the vehicle's lane, e.g., for the right and/or left wheel, is calculated from vehicle geometric quantities, the current and possibly past quantities of vehicle speed, steering angle or yaw rate, etc., and is also represented as a polynomial. From these data, additional lane data are calculated, for example, the lateral distance between the lane marking and the lane of the vehicle (right side to right edge, left side to left edge), the curvature of the lane, and/or the angle between the lane and the lane marking (right lane to right edge, left lane to left edge), on the basis of tangent comparisons. From these data, in an example embodiment, the expected time until line crossing may also be calculated. In an exemplary embodiment, the warning takes place upon exceeding of a predetermined lateral distance, or upon undershooting of a predetermined time value.
In such a function, the driver is warned only if he does not intend to cross over the lane marking.
Various realizations of the classification are conventional. The basic procedure for classification is based on the evaluation of at least two vehicle operating quantities, on the basis of which the behavior of the driver can be inferred. Operating quantities that are suitable for this purpose include, for example, the steering angle (alternatively, the yaw rate), the speed of the vehicle or its acceleration or retardation, the lateral offset between the vehicle lane and the edge of the lane, e.g., changes therein, and/or the angle of the vehicle lane to the edge of the roadway. With regard to the steering angle, the steering behavior is checked, which is clearly recognizable if there is an intention to change lanes. A steering angle greater than a predetermined value, e.g., a corresponding temporal change in the steering angle, indicates an intention to change lanes. During curved travel, the determined roadway curvature is to be taken into account. In addition, when there is an intention to change lanes, e.g., to the left, there is usually an acceleration of the vehicle, so that given an acceleration of the vehicle, or a desired acceleration of the part of the driver, that is greater than a predetermined threshold value, an intentional lane change is to be assumed. Another suitable quantity is the lateral distance of the vehicle from the lane marking, e.g., the temporal change thereof. This represents a measure of the magnitude with which a vehicle is approaching the lane edge marking. In the case of intentional lane changes, this measure is significantly greater than in the case of unintentional lane changes. The same holds correspondingly for the angle to the lane marking, which is significantly greater for intentional lane changes than for unintentional ones.
In sum, it is to be noted that a classification of the lane change process into unintentional and intentional lane changes takes place on the basis of vehicle operating quantities, e.g., if the steering angle exceeds a threshold value and/or the driver's desired acceleration exceeds a threshold value and/or the temporal curve of the lateral distance to the edge marking exceeds a threshold value and/or the angle to the edge marking exceeds a threshold value. These criteria are used in weighted fashion for the classification of the lane change process into intentional and unintentional changes of lane. In general, an intentional lane change is recognized when at least one of the described situations is present, and if none of them is present, an unintentional lane change is recognized.
Through the classification, it is therefore recognized with high probability whether a change of lane is taking place intentionally or unintentionally.
In an exemplary embodiment, neural networks are suitable for the realization of the classifier in accordance with the procedure described above. In an exemplary embodiment, an MLP network (multilayer perceptron) may be suitable. The quantities described above for the left and for the right side are supplied to this neural network. According to the weights (threshold values) assigned to the individual neurons, the neural network forms an output quantity that indicates an intentional or unintentional change of lane.
A second possible realization consists in the specification of concrete conditions for the individual quantities from whose presence an intentional or an unintentional change of lane is derived. In order to make the decision more reliable, a combination of the criteria is to be used at least in unclear cases. If, for example, the angle to the lane upon lane contact is greater than 4°, inattentiveness of the driver can be ruled out as long as no significant lane curvature is present. Correspondingly, for each quantity used a corresponding decision rule can be formed. For the remaining situations, which do not yield an unambiguous result with any decision rule, on the basis of a remaining feature (for example the temporal change of the distance to the lane boundary) a definite decision is made as to whether an intentional or an unintentional change of lane is taking place. The decision criteria, or at least their weighting, are as a rule different for the left side and the right side.
In addition, for example, from the document mentioned above, other procedures are also conventional for distinguishing an intentional change of lane from an unintentional one. In these other procedures, on the basis of the attentiveness of the driver the lane-change intention is determined, using the assumption that if the driver is attentive an intentional change of lane is taking place.
If a lane-change intention has been recognized in one of the manners described above, the program depicted in
Subsequently, in step 402, it is checked whether the change of lane has been concluded or not. A concluded change of lane is recognized in that the vehicle is situated between two edge markings of a lane, or, in an example embodiment, after the elapsing of a predetermined time period. This is determined through analysis of the image from the video camera of the lane departure warning system, from which the position of the vehicle within the lane can be calculated. If the change of lane has not concluded, the controlling of the blinker is maintained according to step 400. If the change of lane has concluded, according to step 404, the controlling of the blinker is terminated, i.e., the blinker is deactivated and the program depicted in the Figure is terminated.
Number | Date | Country | Kind |
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10 2004 048 009.5 | Oct 2004 | DE | national |
Filing Document | Filing Date | Country | Kind | 371c Date |
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PCT/EP05/54048 | 8/17/2005 | WO | 00 | 12/14/2007 |