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公开(公告)号：US09836660B2
公开(公告)日：2017-12-05
申请号：US14957983
申请日：2015-12-03
Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC
Inventor： Qingrong Zhao , Qi Zhang , Jinsong Wang , Bakhtiar B. Litkouhi
CPC classification number: G06K9/00798 , B60W40/06 , B60W2420/42 , B60W2550/14 , B60W2710/06 , B60W2710/18 , B60W2720/40 , B60W2750/40 , G06K9/00791 , G06K9/3233 , G06K9/4604 , G06T7/30 , G06T2207/20024 , G06T2207/30256 , H04N7/183
Abstract: A method of determining a wet surface condition of a road. An image of a road surface is captured by an image capture device of the host vehicle. The image capture device is mounted on a side of the host vehicle and an image is captured in a downward direction. A region of interest is identified in the captured image by a processor. The region of interest is in a region rearward of a tire of a host vehicle. The region of interest is representative of where a tire track as generated by the tire rotating on the road when the road surface is wet. A determination is made whether water is present in the region of interest as a function of identifying the tire track. A wet road surface signal is generated in response to the identification of water in the region of interest.
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22.发明授权Road surface condition detection with recursive adaptive learning and validation 有权路面状态检测与递归自适应学习和验证公开(公告)号：US09139204B1
公开(公告)日：2015-09-22
申请号：US14302642
申请日：2014-06-12
Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC
Inventor： Qingrong Zhao , Jinsong Wang , Wende Zhang , Bakhtiar Brian Litkouhi
IPC: G01C21/00 , B60W40/064 , G06K9/00 , G06K9/62
CPC classification number: G01C21/00 , B60W40/06 , B60W40/068 , B60W50/0098 , B60W2050/0088 , B60W2420/42 , B60W2550/12 , B60W2550/141 , B60W2550/20 , Y02T10/6286
Abstract: A method of determining a road surface condition for a vehicle driving on a road. Probabilities associated with a plurality of road surface conditions based on an image of a capture scene are determined by a first probability module. Probabilities associated with the plurality of road surface conditions based on vehicle operating data are determined by a second probability module. The probabilities determined by the first and second probability modules are input to a data fusion unit for fusing the probabilities and determining a road surface condition. A refined probability is output from the data fusion unit that is a function of the fused first and second probabilities. The refined probability from the data fusion unit is provided to an adaptive learning unit. The adaptive learning unit generates output commands that refine tunable parameters of at least the first probability and second probability modules for determining the respective probabilities.
Abstract translation: 一种确定用于在道路上行驶的车辆的路面状况的方法。 基于捕获场景的图像与多个路面条件相关联的概率由第一概率模块确定。 基于车辆操作数据与多个路面条件相关联的概率由第二概率模块确定。 将由第一和第二概率模块确定的概率输入到数据融合单元，用于融合概率并确定路面状况。 从作为融合的第一和第二概率的函数的数据融合单元输出精确概率。 将来自数据融合单元的精确概率提供给自适应学习单元。 自适应学习单元产生输出命令，其精细化至少第一概率和第二概率模块的可调参数，以确定相应的概率。
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公开(公告)号：US12246700B2
公开(公告)日：2025-03-11
申请号：US18057281
申请日：2022-11-21
Applicant: GM Global Technology Operations LLC
Inventor： Yubiao Zhang , Qingrong Zhao , Edward Joseph Ecclestone
Abstract: A method of estimating a performance characteristic of a wheel of a vehicle, includes selecting relevant input features based on wheel dynamics and tire behavior, and collecting experimental data for each of the relevant input features at each of a plurality of vehicle operating conditions. The method further includes manually identifying and labeling wheel stability status over time from the experimental data and calculating tractive limit over time from the experimental data. The method also includes training a tractive limit model and training a wheel stability status model. The method further includes receiving a plurality of testing inputs, wherein each of the plurality of testing inputs is received from a sensor on-board the vehicle or from a controller on-board the vehicle and, processing the received testing inputs in a predetermined machine learning process to calculate in one or more data processors a prediction of the performance characteristic.
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公开(公告)号：US20240092371A1
公开(公告)日：2024-03-21
申请号：US17933554
申请日：2022-09-20
Inventor： Amir Khajepour , Amin Habibnejad Korayem , Ehsan Hashemi , Qingrong Zhao , SeyedAlireza Kasaiezadeh Mahabadi , Yechen Qin
CPC classification number: B60W40/10 , B60W40/13 , G07C5/085 , B60W2040/1315
Abstract: A universal machine learning based system for estimating a vehicle state of a vehicle includes one or more controllers executing instructions to receive a plurality of dynamic variables and corresponding historical data. The controllers execute a sensitivity analysis algorithm to determine a sensitivity level for each dynamic variable and corresponding historical data and select two or more pertinent dynamic variables based on the sensitivity level of each dynamic variable and the corresponding historical data. The controllers standardize the two or more pertinent dynamic variables into a plurality of generic dynamic variables, wherein the plurality of generic dynamic variables are in a standardized format that is applicable to any configuration of vehicle, and estimate the vehicle state based on the plurality of generic dynamic variables by one or more machine learning algorithms.
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公开(公告)号：US20220379881A1
公开(公告)日：2022-12-01
申请号：US17303304
申请日：2021-05-26
Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC
Inventor： Nikolai Moshchuk , Bakhtiar Litkouhi , Qingrong Zhao
Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: receiving a first surface value associated with a first road surface area in an upcoming environment of the vehicle; receiving a second surface value associated with a second road surface area in the upcoming environment of the vehicle; determining a change in surface value based on the first surface value and the second surface value; and in response to the change in surface value being greater than a threshold, adapting at least one of vehicle collision warning messages, vehicle braking control, vehicle steering control, and path planning based on the second surface value.
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Patent Agency Ranking
公开(公告)号：US20200074639A1
公开(公告)日：2020-03-05
申请号：US16120616
申请日：2018-09-04
Applicant: GM Global Technology Operations LLC
Inventor： Qingrong Zhao , Mohsen Khalili , Bakhtiar B. Litkouhi , Jingfu Jin , Jinsong Wang
Abstract: A vehicle subsystem includes an on-vehicle camera that is disposed to monitor a field of view (FOV) that includes a travel surface for the vehicle. A controller captures, via the on-vehicle camera, an image file associated with the FOV and segments the image file into a first set of regions associated with the travel surface and a second set of regions associated with an above-horizon portion. Image features on each of the first set of regions and the second set of regions are extracted and classified. A surface condition for the travel surface for the vehicle is identified based upon the classified extracted image features from each of the first set of regions and the second set of regions. Operation of the vehicle is controlled based upon the identified surface condition.
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公开(公告)号：US10558868B2
公开(公告)日：2020-02-11
申请号：US15845381
申请日：2017-12-18
Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC
Inventor： Qingrong Zhao , Jinsong Wang , Bakhtiar B. Litkouhi , Jingfu Jin , Tianyu Wang
Abstract: A vehicle includes a plurality of on-vehicle cameras, and a controller executes a method to evaluate a travel surface by capturing images for fields of view of the respective cameras. Corresponding regions of interest for the images are identified, wherein each of the regions of interest is associated with the portion of the field of view of the respective camera that includes the travel surface. Portions of the images are extracted, wherein each extracted portion is associated with the region of interest in the portion of the field of view of the respective camera that includes the travel surface and wherein one extracted portion of the respective image includes the sky. The extracted portions of the images are compiled into a composite image datafile, and an image analysis of the composite image datafile is executed to determine a travel surface state. The travel surface state is communicated to another controller.
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公开(公告)号：US10373000B2
公开(公告)日：2019-08-06
申请号：US15677649
申请日：2017-08-15
Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC
Inventor： Wei Tong , Qingrong Zhao , Shuqing Zeng , Bakhtiar B. Litkouhi
Abstract: A method of identifying a condition of a road surface includes capturing at least a first image of the road surface with a first camera, and a second image of the road surface with a second camera. The first image and the second image are tiled together to form a combined tile image. A feature vector is extracted from the combined tile image using a convolutional neural network, and a condition of the road surface is determined from the feature vector using a classifier.
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29.发明授权公开(公告)号：US10272919B2
公开(公告)日：2019-04-30
申请号：US14510862
申请日：2014-10-09
Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC
Inventor： Youssef A. Ghoneim , Qingrong Zhao , Qi Zhang , Bakhtiar B. Litkouhi , Yingmei Si , David M. Sidlosky , Valarie V. Boatman , Sami Ahmed
IPC: B60W40/06 , B60W40/068
Abstract: Methods and systems for determining road surface information in a vehicle. In one embodiment, the method includes: determining at least one condition assessment value based on steering data; determining a feature set to include at least one of self-aligning torque (SAT), slip angle, SAT variance, steering rate, and lateral acceleration based on the condition assessment value; processing steering data obtained during a steering maneuver and associated with the feature set using a pattern classification technique; and determining a surface type based on the processing.
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公开(公告)号：US20190057272A1
公开(公告)日：2019-02-21
申请号：US15681008
申请日：2017-08-18
Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC
Inventor： Qingrong Zhao , Bakhtiar B. Litkouhi , Qi Zhang , Jinsong Wang , Wende Zhang , Jingfu Jin
Abstract: A method of identifying a snow covered road includes creating a forward image of a road surface. The forward image is analyzed to detect a tire track in the forward image. When a tire track is detected in the forward image, a message indicating a snow covered road surface is signaled. When a tire track is not detected in the forward image, a rearward image, a left side image, and a right side image are created. The rearward image, the left side image, and the right side image are analyzed to detect a tire track in at least one of the rearward image, the right side image, and the left side image. A message indicating a snow covered road surface is signaled when a tire track is detected in one of the rearward image, the left side image, or the right side image.
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