Clustering track pairs for multi-sensor track association

    公开(公告)号:US12093048B2

    公开(公告)日:2024-09-17

    申请号:US17524667

    申请日:2021-11-11

    发明人: Syed Asif Imran

    摘要: This document describes systems and techniques for clustering track pairs for multi-sensor track association. Many track-association algorithms use pattern-matching processes that can be computationally complex. Clustering tracks derived from different sensors present on a vehicle may reduce the computational complexity by reducing the pattern-matching problem into groups of subproblems. The weakest connection between two sets of tracks is identified based on both the perspective from each track derived from a first sensor and the perspective of each track derived from a second sensor. By identifying and pruning the weakest connection between two sets of tracks, a large cluster of tracks may be split into smaller clusters. The smaller clusters may require fewer computations by limiting the quantity of candidate track pairs to be evaluated. Fewer computations result in processing the sensor information more efficiently that, in turn, may increase the safety and reliability of an automobile.

    Method and apparatus for enhancing effectivity of machine learning solutions

    公开(公告)号:US12056580B2

    公开(公告)日:2024-08-06

    申请号:US16662090

    申请日:2019-10-24

    摘要: A method, system and computer program product, the method comprising: creating a model representing underperforming cases; from a case collection having a total performance, and which comprises for each of a multiplicity of records: a value for each feature from a collection of features, a ground truth label and a prediction of a machine learning (ML) engine, obtaining one or more features; dividing the records into groups, based on values of the features in each record; for one group of the groups, calculating a performance parameter of the ML engine over the portion of the records associated with the group; subject to the performance parameter of the group being below the total performance in at least a predetermined threshold: determining a characteristic for the group; adding the characteristic of the group to the model; and providing the model to a user, thus indicating under-performing parts of the test collection.