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公开(公告)号：US11460544B2
公开(公告)日：2022-10-04
申请号：US16194461
申请日：2018-11-19
申请人： Dalong Li , Andrew Chen , Ivan Roman
发明人： Dalong Li , Andrew Chen , Ivan Roman
IPC分类号： G06F11/30 , G01S7/48 , G01S13/931 , G01S13/42
摘要： An advanced driver assistance system (ADAS) and method for a vehicle utilize a light detection and ranging (LIDAR) system configured to emit laser light pulses and capture reflected laser light pulses collectively forming three-dimensional (3D) LIDAR point cloud data and a controller configured to receive the 3D LIDAR point cloud data, convert the 3D LIDAR point cloud data to a two-dimensional (2D) birdview projection, detect a set of lines in the 2D birdview projection, filter the detected set of lines to remove lines having features that are not indicative of traffic signs to obtain a filtered set of lines, and detect one or more traffic signs using the filtered set of lines.
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公开(公告)号：US20140146882A1
公开(公告)日：2014-05-29
申请号：US14126573
申请日：2011-08-08
申请人： Steven J. Simske , Dalong Li
发明人： Steven J. Simske , Dalong Li
CPC分类号： H04N19/18 , G08B13/1961 , G08B13/19667 , H04N7/18 , H04N19/12 , H04N19/14 , H04N19/60 , H04N19/87
摘要： An embodiment provides a method for compression of a real-time surveillance signal. This method includes receiving a signal from a monitoring device and analyzing the signal to be monitored to compute spectral content of the signal. This method also includes computing the information content of the signal and determining a count of a number of coefficients to be used to monitor the signal. This method includes deploying a strategy for computing a plurality of coefficients based on the spectral content of the signal and the count of the number of coefficients to be used for monitoring the signal. This method further includes monitoring the signal and resetting the system in the case of above-threshold changes in a selected portion of the plurality of coefficients.
摘要翻译： 实施例提供了一种压缩实时监视信号的方法。 该方法包括从监视设备接收信号并分析待监视的信号以计算信号的频谱内容。 该方法还包括计算信号的信息内容并确定要用于监视信号的系数数量的计数。 该方法包括基于信号的频谱内容和用于监视信号的系数数量的计数来部署用于计算多个系数的策略。 该方法还包括在多个系数的选定部分中的阈值上变化的情况下监视信号和重置系统。
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公开(公告)号：US20210133947A1
公开(公告)日：2021-05-06
申请号：US16670575
申请日：2019-10-31
申请人： Dalong Li , Stephen Horton , Neil R Garbacik
发明人： Dalong Li , Stephen Horton , Neil R Garbacik
摘要： An autonomous driving technique comprises determining an image quality metric for each image frame of a series of image frames of a scene outside of a vehicle captured by a camera system and determining an image quality threshold based on the image quality metrics for the series of image frames. The technique then determines whether the image quality metric for a current image frame satisfies the image quality threshold. When the image quality metric for the current image frame satisfies the image quality threshold, object detection is performed by at least utilizing a first deep neural network (DNN) with the current image frame. When the image quality metric for the current image frame fails to satisfy the image quality threshold, object detection is performed by utilizing a second, different DNN with the information captured by another sensor system and without utilizing the first DNN or the current image frame.
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公开(公告)号：US10983215B2
公开(公告)日：2021-04-20
申请号：US16224978
申请日：2018-12-19
申请人： Dalong Li , Alex M Smith , Stephen Horton
发明人： Dalong Li , Alex M Smith , Stephen Horton
IPC分类号： G01S17/66 , G01S7/48 , B60W30/09 , B60W30/14 , G01S17/931
摘要： An advanced driver assistance system (ADAS) and method for a vehicle utilize a light detection and ranging (LIDAR) system configured to emit laser light pulses and capture reflected laser light pulses collectively forming three-dimensional (3D) LIDAR point cloud data and a controller configured to receive the 3D LIDAR point cloud data, convert the 3D LIDAR point cloud data to a two-dimensional (2D) birdview projection, obtain a template image for object detection, the template image being representative of a specific object, blur the 2D birdview projection and the template image to obtain a blurred 2D birdview projection and a blurred template image, and detect the specific object by matching a portion of the blurred 2D birdview projection to the blurred template image.
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公开(公告)号：US10823855B2
公开(公告)日：2020-11-03
申请号：US16194465
申请日：2018-11-19
申请人： Dalong Li , Andrew Chen
发明人： Dalong Li , Andrew Chen
摘要： An advanced driver assistance system (ADAS) and method for a vehicle utilize a light detection and ranging (LIDAR) system configured to emit laser light pulses and capture reflected laser light pulses collectively forming three-dimensional (3D) LIDAR point cloud data and a controller configured to receive the 3D LIDAR point cloud data divide the 3D LIDAR point cloud data into a plurality of cells corresponding to distinct regions surrounding the vehicle, generate a histogram comprising a calculated height difference between a maximum height and a minimum height in the 3D LIDAR point cloud data for each cell of the plurality of cells, and using the histogram, perform at least one of adaptive ground removal from the 3D LIDAR point cloud data and traffic level recognition.
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6.发明授权公开(公告)号：US11820393B2
公开(公告)日：2023-11-21
申请号：US17082180
申请日：2020-10-28
申请人： Neil Garbacik , Dalong Li
发明人： Neil Garbacik , Dalong Li
CPC分类号： B60W50/06 , B60W60/001 , G06F18/2193 , G06N3/08 , G06N20/00 , G06V10/22 , G06V20/56 , B60W2420/42 , B60W2420/52
摘要： Systems and methods for testing a machine learning algorithm or technique of an autonomous driving feature of a vehicle utilize a sensor system configured to capture input data representative of an environment external to the vehicle and a controller configured to receive the input data from the sensor system and perform a testing procedure for the autonomous driving feature that includes inserting known input data into a target portion of the input data to obtain modified input data, processing the modified input data according to the autonomous driving feature to obtain output data, and determining an accuracy of the autonomous driving features based on a comparison between the output data and the known input data.
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公开(公告)号：US11594040B2
公开(公告)日：2023-02-28
申请号：US16985460
申请日：2020-08-05
申请人： Dalong Li , Stephen Horton , Neil R Garbacik
发明人： Dalong Li , Stephen Horton , Neil R Garbacik
摘要： Techniques for training multiple resolution deep neural networks (DNNs) for vehicle autonomous driving comprise obtaining a training dataset for training a plurality of DNNs for an autonomous driving feature of the vehicle, sub-sampling the training dataset to obtain a plurality of training datasets comprising the training dataset and one or more sub-sampled datasets each having a different resolution than a remainder of the plurality of training datasets, training the plurality of DNNs using the plurality of training datasets, respectively, determining a plurality of outputs for the autonomous driving feature using the plurality of trained DNNs and the input data, receiving input data for the autonomous driving feature captured by a sensor device, and determining a best output for the autonomous driving feature using the plurality of outputs.
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公开(公告)号：US20220261582A1
公开(公告)日：2022-08-18
申请号：US17178865
申请日：2021-02-18
申请人： Dalong Li , Neil Garbacik
发明人： Dalong Li , Neil Garbacik
摘要： An object detection and classification verification system for a vehicle includes a projection system configured to project a three-dimensional (3D) scene pre-captured at a known distance and comprising at least one known object onto a surface in front of the vehicle a controller configured to verify a performance of an object detection and classification routine by performing the object detection and classification routine on the projected 3D scene to generate a set of results, comparing the set of results to a set of expected results associated with the projected 3D scene, and based on the comparing, determining whether the performance of the object detection and classification routine satisfies a predetermined threshold metric.
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9.发明申请公开(公告)号：US20220126849A1
公开(公告)日：2022-04-28
申请号：US17082180
申请日：2020-10-28
申请人： Neil Garbacik , Dalong Li
发明人： Neil Garbacik , Dalong Li
摘要： Systems and methods for testing a machine learning algorithm or technique of an autonomous driving feature of a vehicle utilize a sensor system configured to capture input data representative of an environment external to the vehicle and a controller configured to receive the input data from the sensor system and perform a testing procedure for the autonomous driving feature that includes inserting known input data into a target portion of the input data to obtain modified input data, processing the modified input data according to the autonomous driving feature to obtain output data, and determining an accuracy of the autonomous driving features based on a comparison between the output data and the known input data.
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公开(公告)号：US20100158373A1
公开(公告)日：2010-06-24
申请号：US12642643
申请日：2009-12-18
申请人： Dalong Li , John Elton , Sebastian Steger
发明人： Dalong Li , John Elton , Sebastian Steger
IPC分类号： G06K9/00
CPC分类号： G06K9/38
摘要： A threshold determination method is selected from among a plurality of alternative global thresholding determination methods and, optionally, a local thresholding determination method based on characteristics of a histogram of grayscales values representing an image. When it is determined to use a global thresholding method, a single global binarization threshold value is determined using the selected global thresholding method. Various alternative global binarization threshold values include a predetermined constant, an average value of the two grayscale values, an Otsu method based threshold value, a Newton method based threshold value, and an Otsu method based threshold value based on a truncated version of the histogram. When it is determined to use local thresholding, a plurality of local binarization threshold values are determined corresponding to different non-overlapping blocks of the image. The determined binarization threshold(s) are applied to the gray scale pixel values to obtain a set of binary pixel values.
摘要翻译： 从多个备选全局阈值确定方法中选择阈值确定方法，并且可选地，基于表示图像的灰度值的直方图的特征来选择局部阈值确定方法。 当确定使用全局阈值方法时，使用所选择的全局阈值方法来确定单个全局二值化阈值。 各种替代的全局二值化阈值包括基于直方图的截断版本的预定常数，两个灰度值的平均值，基于Otsu方法的阈值，基于牛顿法的阈值和基于Otsu方法的阈值。 当确定使用本地阈值时，对应于图像的不同非重叠块来确定多个局部二值化阈值。 将确定的二值化阈值应用于灰度像素值以获得一组二进制像素值。
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