TARGET GROUPING TECHNIQUES FOR OBJECT FUSION
First Claim
1. A method for grouping object sensor measurements with target objects in an object detection system, said method comprising:
- providing a list of target objects being tracked by the object detection system, where the list of target objects includes known targets identified by the object detection system in an area ahead of a host vehicle;
computing hypothesis locations and orientations for each known target in the list of target objects, where the hypothesis locations and orientations include a prediction of each known target'"'"'s movement since the list of target objects was previously computed;
providing sensor measurement points from at least one object sensing system, where the sensor measurement points designate points at which an object has been detected in the area ahead of the host vehicle;
grouping, using a microprocessor, the sensor measurement points with the known targets at the hypothesis locations and orientations;
validating the hypothesis locations and orientations based on the grouping;
identifying new targets based on any clusters of sensor measurement points which do not correlate to one of the known targets; and
updating the list of target objects to include the known targets at the hypothesis locations and orientations, and any new targets identified.
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Abstract
A method is disclosed for improved target grouping of sensor measurements in an object detection system. The method uses road curvature information to improve grouping accuracy by better predicting a new location of a known target object and matching it to sensor measurements. Additional target attributes are also used for improved grouping accuracy, where the attributes includes range rate, target cross-section and others. Distance compression is also employed for improved grouping accuracy, where range is compressed in a log scale calculation in order to diminish errors in measurement of distant objects. Grid-based techniques include the use of hash tables and a flood fill algorithm for improved computational performance of target object identification, where the number of computations can be reduced by an order of magnitude.
31 Citations
20 Claims
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1. A method for grouping object sensor measurements with target objects in an object detection system, said method comprising:
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providing a list of target objects being tracked by the object detection system, where the list of target objects includes known targets identified by the object detection system in an area ahead of a host vehicle; computing hypothesis locations and orientations for each known target in the list of target objects, where the hypothesis locations and orientations include a prediction of each known target'"'"'s movement since the list of target objects was previously computed; providing sensor measurement points from at least one object sensing system, where the sensor measurement points designate points at which an object has been detected in the area ahead of the host vehicle; grouping, using a microprocessor, the sensor measurement points with the known targets at the hypothesis locations and orientations; validating the hypothesis locations and orientations based on the grouping; identifying new targets based on any clusters of sensor measurement points which do not correlate to one of the known targets; and updating the list of target objects to include the known targets at the hypothesis locations and orientations, and any new targets identified. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A method for grouping object sensor measurements with target objects in an object detection system, said method comprising:
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providing a list of target objects being tracked by the object detection system, where the list of target objects includes known targets identified by the object detection system in an area ahead of a host vehicle; computing hypothesis locations and orientations for each known target in the list of target objects, where the hypothesis locations and orientations include a prediction of each known target'"'"'s movement since the list of target objects was previously computed based on road curvature data and target velocity; providing sensor measurement points from at least one object sensing system, where the sensor measurement points designate points at which an object has been detected in the area ahead of the host vehicle; grouping, using a microprocessor, the sensor measurement points with the known targets at the hypothesis locations and orientations, including comparing both a range and a range rate of the points and the targets to establish correlations, and further including using a mapped range value for the measurement points and the known targets, where the mapped range value is computed from an actual range value using a logarithmic scale; validating the hypothesis locations and orientations based on the grouping; identifying new targets based on any clusters of sensor measurement points which do not correlate to one of the known targets; and updating the list of target objects to include the known targets at the hypothesis locations and orientations, and any new targets identified. - View Dependent Claims (13)
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14. An object detection system comprising:
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at least one object sensing system onboard a host vehicle, said object sensing system providing sensor measurement points which designate points at which an object has been detected in an area ahead of the host vehicle; a memory module for storing a list of target objects being tracked by the object detection system, where the list of target objects includes known targets identified by the object detection system in the area ahead of the host vehicle; and an object detection processor in communication with the memory module and the at least one object sensing system, said object detection processor being configured to; compute hypothesis locations and orientations for each known target in the list of target objects, where the hypothesis locations and orientations include a prediction of each known target'"'"'s movement since the list of target objects was previously computed; group the sensor measurement points with the known targets at the hypothesis locations and orientations; validate the hypothesis locations and orientations based on the grouping; identify new targets based on any clusters of sensor measurement points which do not correlate to one of the known targets; and update the list of target objects to include the known targets at the hypothesis locations and orientations, and any new targets identified. - View Dependent Claims (15, 16, 17, 18, 19, 20)
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Specification