-
公开(公告)号：US20220153306A1
公开(公告)日：2022-05-19
申请号：US17129775
申请日：2020-12-21
发明人： Syed Asif Imran , Jan K. Schiffmann , Nianxia Cao
IPC分类号： B60W60/00 , G06T7/20 , B60W30/095 , B60W50/02 , G01S13/931
摘要： This document describes Kurtosis based pruning for sensor-fusion systems. Kurtosis based pruning minimizes a total quantity of comparisons performed when fusing together large sets of data. Multiple candidate radar tracks may possibly align with one of multiple candidate visual tracks. For each candidate vision track, a weight or other evidence of matching is assigned to each candidate radar track. An inverse of matching errors between each candidate vision and each candidate radar track contributes to this evidence, which may be normalized to produce, for each candidate vision track, a distribution associated with all candidate radar tracks. A Kurtosis or shape of this distribution is calculated. Based on the Kurtosis values, some candidate radar tracks are selected for matching and other remaining candidate radar tracks are pruned. The Kurtosis aids in determining how many candidates to retain and how many to prune. In this way, Kurtosis based pruning can prevent combinatorial explosions due to large-scale matching.
-
公开(公告)号：US20200349718A1
公开(公告)日：2020-11-05
申请号：US16398745
申请日：2019-04-30
发明人： Wenbing Dang , Jan K. Schiffmann , Kumar Vishwajeet , Susan Chen
摘要： An illustrative example method of tracking an object includes detecting one or more points on the object over time to obtain a plurality of detections, determining a position of each of the detections, determining a relationship between the determined positions, and determining an estimated heading angle of the object based on the relationship.
-
公开(公告)号：US11618480B2
公开(公告)日：2023-04-04
申请号：US17129775
申请日：2020-12-21
发明人： Syed Asif Imran , Jan K. Schiffmann , Nianxia Cao
IPC分类号： G06T7/00 , B60W60/00 , B60W30/095 , B60W50/02 , G01S13/931 , G06T7/20
摘要： This document describes Kurtosis based pruning for sensor-fusion systems. Kurtosis based pruning minimizes a total quantity of comparisons performed when fusing together large sets of data. Multiple candidate radar tracks may possibly align with one of multiple candidate visual tracks. For each candidate vision track, a weight or other evidence of matching is assigned to each candidate radar track. An inverse of matching errors between each candidate vision and each candidate radar track contributes to this evidence, which may be normalized to produce, for each candidate vision track, a distribution associated with all candidate radar tracks. A Kurtosis or shape of this distribution is calculated. Based on the Kurtosis values, some candidate radar tracks are selected for matching and other remaining candidate radar tracks are pruned. The Kurtosis aids in determining how many candidates to retain and how many to prune. In this way, Kurtosis based pruning can prevent combinatorial explosions due to large-scale matching.
-
公开(公告)号：US20220308205A1
公开(公告)日：2022-09-29
申请号：US17644464
申请日：2021-12-15
发明人： Simon Roesler , Adrian Becker , Jan K. Schiffmann
IPC分类号： G01S13/931 , G01S13/89 , G01S13/58 , G06N3/02
摘要： This document describes techniques and systems for a partially-learned model for speed estimates in radar tracking. A radar system is described that determines radial-velocity maps of potential detections in an environment of a vehicle. The model uses a data cube to determine predicted boxes for the potential detections. Using the predicted boxes, the radar system determines Doppler measurements associated with the potential detections that correspond to the predicted boxes. The Doppler measurements are used to determine speed estimates for the predicted boxes based on the corresponding potential detections. These speed estimates may be more accurate than a speed estimate derived from the data cube and the model. Driving decisions supported by the speed estimates may result in safer and more comfortable vehicle behavior.
-
公开(公告)号：US20220308198A1
公开(公告)日：2022-09-29
申请号：US17649794
申请日：2022-02-02
IPC分类号： G01S13/66 , G01S13/931 , G01S13/87
摘要： This document describes radar tracking with model estimates augmented by radar detections. An example tracker analyzes information derived using radar detections to enhance radar tracks having object measurements estimated from directly analyzing data cubes with a model (e.g., a machine-learning model). High-quality tracks with measurements to objects of importance can be quickly produced with the model. However, the model only estimates measurements for classes of objects its training or programming can recognize. To improve estimated measurements from the model, or even in some cases, to convey additional classes of objects, the tracker separately analyzes detections. Detections that consistently align to objects recognized by the model can update model-derived measurements conveyed initially in the tracks. Consistently observed detections that do not align to existing tracks may be used to establish new tracks for conveying more classes of objects than the model can recognize.
-
公开(公告)号：US20210374974A1
公开(公告)日：2021-12-02
申请号：US17403690
申请日：2021-08-16
发明人： Wenbing Dang , Jan K. Schiffmann , Kumar Vishwajeet , Susan Chen
摘要： An illustrative example method of tracking an object includes detecting one or more points on the object over time to obtain a plurality of detections, determining a position of each of the detections, determining a relationship between the determined positions, and determining an estimated heading angle of the object based on the relationship.
-
公开(公告)号：US11035943B2
公开(公告)日：2021-06-15
申请号：US16039890
申请日：2018-07-19
发明人： Kumar Vishwajeet , Jan K. Schiffmann , Wenbing Dang , Keerthi Raj Nagaraja , Franz P. Schiffmann
IPC分类号： G01S13/536 , G01S13/931
摘要： An illustrative example method of classifying a detected object includes detecting an object, determining that an estimated velocity of the object is below a preselected threshold velocity requiring classification, determining a time during which the object has been detected, determining a first distance the object moves during the time determining a speed of the object from the first distance and the time, determining a second distance that a centroid of the detected object moves during the time, and classifying the detected object as a slow moving object or a stationary object based on a relationship between the first and second distances and a relationship between the estimated velocity and the speed.
-
公开(公告)号：US10565468B2
公开(公告)日：2020-02-18
申请号：US15000730
申请日：2016-01-19
发明人： Jan K. Schiffmann
摘要： An object tracking system suitable for use on an automated vehicle includes a camera, a radar-sensor and a controller. The controller is configured to assign a vision-identification to each vision-track associated with an instance of an object detected using the camera, and assign a radar-identification to each radar-glob associated with an instance of grouped-tracklets indicated detected using the radar-sensor. The controller is further configured to determine probabilities that a vision-track and a radar-glob indicate the same object. If the combination has a reasonable chance of matching it is includes in a further screening of the data to determine a combination of pairings of each vision-track to a radar-track that has the greatest probability of being the correct combination.
-
公开(公告)号：US11615538B2
公开(公告)日：2023-03-28
申请号：US17403690
申请日：2021-08-16
发明人： Wenbing Dang , Jan K. Schiffmann , Kumar Vishwajeet , Susan Chen
摘要： An illustrative example method of tracking an object includes detecting one or more points on the object over time to obtain a plurality of detections, determining a position of each of the detections, determining a relationship between the determined positions, and determining an estimated heading angle of the object based on the relationship.
-
公开(公告)号：US20220300743A1
公开(公告)日：2022-09-22
申请号：US17308908
申请日：2021-05-05
发明人： Syed Asif Imran , Jan K. Schiffmann , Nianxia Cao
摘要： This document describes methods and systems directed at history-based identification of incompatible tracks. The historical trajectory of tracks can be advantageous to accurately determine whether tracks originating from different sensors identify the same object or different objects. However, recording historical data of several tracks may consume vast amounts of memory or computing resources, and related computations may become complex. The methods and systems described herein enable a sensor fusion system of an automobile or other vehicle to consider historical data when associating and pairing tracks, without requiring large amounts of memory and without tying up other computing resources.
-
-
-
-
-
-
-
-
-
搜索结果
14 条记录 (耗时 0.056 秒)