In recent years, devices which determine the distance and size of objects in front of a vehicle, and which appropriately control the vehicle in accordance with this judgment, have been proposed for the purpose of improving the safety of vehicle operation.
Japanese Patent Application Kokai No. Hei 9-79821 describes one example of a device in which an optical distance measuring device consisting of two light-receiving elements is used to determine whether an object whose distance has been detected is a physical object or a road area (including characters or white lines on the road surface). The device calculates differences for respective calculation areas, and recognizes areas in which obstructions are present by clustering calculation areas whose mutual distances are within a fixed range and are proximate to each other in the horizontal direction. In the case of this clustering, calculation areas whose distances have not yet been measured are also clustered.
However, when clustering (that corresponds to the abovementioned block formation) is performed with larger number of windows (that corresponds to the abovementioned calculation areas) so that measured distance values can be obtained with a greater degree of fineness, an extremely large amount of time is required for determining whether or not distances are within a specified range according to the scheme as described in Patent Application Kokai No. Hei 9-79821.
Conventionally, furthermore, when the distance ranges are determined beforehand and windows are clustered within these distance ranges, it was necessary to first determine the distance range in which clustering is to be performed based on such factors as a frequency distribution of the distances. Furthermore, when clustering is performed, it was necessary to determine which distance ranges the distance values calculated for the windows belong to and to determine whether or not respective distance ranges are equal to each other. Such process requires a large amount of time.
Furthermore, when the distance ranges in which clustering is performed are fixed as in the device described in Japanese Patent Application Kokai No. Hei 9-79821, precision of the distance value drops as the distance becomes larger. Generally, the calculation of a distance “d” is expressed by the formula “d=C/s (C is a constant)”, where “s” is a parallax. While the parallax resolution is constant, the distance resolution drops as the distance becomes larger. As a result, when a physical object is imaged over a plurality of windows, larger error is generated in distance values for respective windows as the distance becomes larger.
For example, referring to FIG. 14(a), according to the prior art, clustering is performed with a fixed distance range as shown by a pair of arrows. In an area closer to the vehicle, a window with a distance value 101 and a window with a distance value 102, which fall within the fixed distance range, are clustered together. In contrast, a window with a distance value 103 and a window with a distance value 104 are clustered into different clusters in an area further from the vehicle even if the two windows belong to a same object. This is so because they are not within the fixed distance range.
Conversely, when the distance range is set to a larger value so that two windows belonging to the same physical object are clustered into the same cluster in an area further from the vehicle as shown in FIG. 14(b), the window with a distance value 103 and the window with a distance value 104 may be clustered into a same cluster. However, in an area closer to the vehicle, not only the windows with distance values of 101 and 102, but also the window with a distance value 105 that belongs to a different physical object, are clustered into a same cluster. Thus, if clustering is performed with a fixed distance range, instances arise in which a physical object cannot be formed into a same cluster, and in which different physical objects are included in a same cluster.
Accordingly, one object of the present invention is to provide a system, which makes it possible to recognize objects by clustering windows at a high speed. This is accomplished by performing clustering based on labels corresponding to distance values calculated for the windows. Furthermore, another object of the present invention is to provide an object recognition system, which makes it possible to recognize physical objects by the accurate clustering of windows. This is accomplished by setting distance ranges in accordance with tolerances in measured distances.