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公开(公告)号：US10832450B2
公开(公告)日：2020-11-10
申请号：US16366393
申请日：2019-03-27
Applicant: GM Global Technology Operations LLC
Inventor： Wei Tong , Chengqi Bian , Farui Peng , Shuqing Zeng
Abstract: A method for image style transfer using a Semantic Preserved Generative Adversarial Network (SPGAN) includes: receiving a source image; inputting the source image into the SPGAN; extracting a source-semantic feature data from the source image; generating, by the first decoder, a first synthetic image including the source semantic content of the source image in a target style of a target image using the source-semantic feature data extracted by the first encoder of the first generator network, wherein the first synthetic image includes first-synthetic feature data; determining a first encoder loss using the source-semantic feature data and the first-synthetic feature data; discriminating the first synthetic image against the target image to determine a GAN loss; determining a total loss as a function of the first encoder loss and the first GAN loss; and training the first generator network and the first discriminator network.
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公开(公告)号：US10824943B2
公开(公告)日：2020-11-03
申请号：US16107315
申请日：2018-08-21
Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC
Inventor： Wei Tong , Shuqing Zeng , Upali P. Mudalige , Shige Wang
Abstract: Described herein are systems, methods, and computer-readable media for generating and training a high precision low bit convolutional neural network (CNN). A filter of each convolutional layer of the CNN is approximated using one or more binary filters and a real-valued activation function is approximated using a linear combination of binary activations. More specifically, a non-1×1 filter (e.g., a k×k filter, where k>1) is approximated using a scaled binary filter and a 1×1 filter is approximated using a linear combination of binary filters. Thus, a different strategy is employed for approximating different weights (e.g., 1×1 filter vs. a non-1×1 filter). In this manner, convolutions performed in convolutional layer(s) of the high precision low bit CNN become binary convolutions that yield a lower computational cost while still maintaining a high performance (e.g., a high accuracy).
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13.发明授权公开(公告)号：US10745006B2
公开(公告)日：2020-08-18
申请号：US15886492
申请日：2018-02-01
Applicant: GM Global Technology Operations LLC
Inventor： Donald K. Grimm , Wei Tong , Shuqing Zeng , Upali P. Mudalige
Abstract: Technical solutions are described for controlling an automated driving system of a vehicle. An example method includes computing a complexity metric of an upcoming region along a route that the vehicle is traveling along. The method further includes, in response to the complexity metric being below a predetermined low-complexity threshold, determining a trajectory for the vehicle to travel in the upcoming region using a computing system of the vehicle. Further, the method includes in response to the complexity metric being above a predetermined high-complexity threshold, instructing an external computing system to determine the trajectory for the vehicle to travel in the upcoming region. If the trajectory cannot be determined by the external computing system a minimal risk condition maneuver of the vehicle is performed.
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公开(公告)号：US20190325670A1
公开(公告)日：2019-10-24
申请号：US15960781
申请日：2018-04-24
Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC
Inventor： Wei Tong , Hojjat Izadi , Fahim Javid
Abstract: An apparatus and method that detect a wheel alignment condition are provided. The method includes receiving a dataset comprising one or more from among a steering wheel angle parameter, a speed parameter, a lateral acceleration parameter, a self-aligning torque parameter and a power steering torque parameter, normalizing the received dataset, analyzing the normalized dataset according to a model for determining a wheel alignment condition, and outputting a value indicating whether the wheel alignment is within a predetermined value based on the model.
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公开(公告)号：US20190325290A1
公开(公告)日：2019-10-24
申请号：US15960948
申请日：2018-04-24
Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC
Inventor： Wei Tong , Hojjat Izadi , Fahim Javid
Abstract: A method and apparatus that detect wheel misalignment are provided. The method includes predicting a self-aligning torque parameter based on a regression model determined from a dataset including one or more from among a steering wheel angle parameter, a speed parameter, a torsion bar torque parameter, a lateral acceleration parameter, and a power steering torque parameter, comparing a measured self-aligning torque parameter and the predicted self-aligning torque parameter, and outputting a wheel alignment condition indicating whether the wheel alignment is proper if the self-aligning torque parameter and the predicted self-aligning torque parameter are within a predetermined value based on the comparing.
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Patent Agency Ranking
公开(公告)号：US20190244136A1
公开(公告)日：2019-08-08
申请号：US15888322
申请日：2018-02-05
Applicant: GM Global Technology Operations LLC
Inventor： Yasen Hu , Shuqing Zeng , Wei Tong , Mohannad Murad , Gregg R. Kittinger , David R. Petrucci
CPC classification number: G06N20/00 , G05D1/0088 , G05D1/0238 , G05D1/0255
Abstract: A system and method to perform inter-sensor learning obtains a detection of a target based on a first sensor. The method also includes determining whether a second sensor with an overlapping detection range with the first sensor also detects the target, and performing learning to update a detection algorithm used with the second sensor based on the second sensor failing to detect the target.
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公开(公告)号：US10268204B2
公开(公告)日：2019-04-23
申请号：US15690966
申请日：2017-08-30
Applicant: GM Global Technology Operations LLC
Inventor： Wei Tong , Shuqing Zeng , Upali P. Mudalige
Abstract: A vehicle, system and method of driving of an autonomous vehicle. The vehicle includes a camera for obtaining an image of a surrounding region of the vehicle, an actuation device for controlling a parameter of motion of the vehicle, and a processor. The processor selects a context region within the image, wherein the context region including a detection region therein. The processor further estimates a confidence level indicative of the presence of at least a portion of the target object in the detection region and a bounding box associated with the target object, determines a proposal region from the bounding box when the confidence level is greater than a selected threshold, determines a parameter of the target object within the proposal region, and controls the actuation device to alter a parameter of motion of the vehicle based on the parameter of the target object.
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公开(公告)号：US20190026597A1
公开(公告)日：2019-01-24
申请号：US15658138
申请日：2017-07-24
Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC
Inventor： Shuqing Zeng , Wei Tong , Upali P. Mudalige
CPC classification number: G06K9/629 , G05D1/0088 , G05D1/0231 , G05D1/0246 , G05D1/0274 , G05D1/0278 , G05D2201/0213 , G06K9/00791 , G06K9/00805 , G06K9/6256 , G06K9/6277 , G06N3/04 , G06N3/0454 , G06N3/08 , G06N3/084 , H04L67/12
Abstract: Methods and apparatus are provided for controlling an autonomous vehicle. A sensor fusion system with a sensor system for providing environment condition information and a convolutional neural network (CNN) is provided. The CNN includes a receiving interface configured to receive the environment condition information from the sensor system, a common convolutional layer configured to extract traffic information from the received environment condition information, and a plurality of fully connected layers configured to detect objects belonging to different object classes based on the extracted traffic information, wherein the object classes include at least one of a road feature class, a static object class, and a dynamic object class.
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公开(公告)号：US20180072269A1
公开(公告)日：2018-03-15
申请号：US15261048
申请日：2016-09-09
Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC
Inventor： Wei Tong , Jinsong Wang , Donald K. Grimm , Thomas R. Brown , Mary E. Decaluwe , Carl W. Wellborn
IPC: B60R25/30
CPC classification number: B60R25/305 , B60R2300/8073
Abstract: A method of detecting an intrusion includes sending an activation command to an intrusion detection system. In response to the activation command, at least one camera is activated. At least one image is obtained from the at least one camera representative of a surrounding area of the at least one camera. The at least one image is analyzed to determine if the intrusion is detected. An operator is then notified of the presence or absence of the intrusion.
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公开(公告)号：US12115995B2
公开(公告)日：2024-10-15
申请号：US17829643
申请日：2022-06-01
Applicant: GM Global Technology Operations LLC
Inventor： Alaa M. Khamis , Wei Tong , Sai Vishnu Aluru , Upali P. Mudalige , Sravan Daruri
IPC: B60W40/08 , A61B5/00 , A61B5/0205 , A61B5/16 , B60W30/18 , G06V10/12 , G06V20/59 , G06V20/70 , G10L15/22 , G10L25/63
CPC classification number: B60W40/08 , B60W30/18109 , G06V10/12 , G06V20/59 , G06V20/70 , G10L15/22 , G10L25/63 , A61B5/0205 , A61B5/165 , A61B5/6893 , B60W2040/089
Abstract: An in-vehicle violence detection system includes a speech and non-speech audio recognition module capturing occupant threat words and non-speech audio events. In-vehicle accelerometers generate data analyzed in a shaking movement recognition module. A heart rate and breathing rate detection module measures physiological changes in occupant heart rates and breathing rhythms. An in-vehicle semantic scene recognition module analyzes occupant non-verbal interactions. Occupant threat indicators including an audible threat indicator are generated by the speech and non-speech audio event recognition module. Visual threat indicators are generated by the in-vehicle semantic scene recognition module. Physiological threat indicators are generated by the heart rate and breathing rate detection module. Vibration-based threat indicators are generated by the shaking movement recognition module. The four threat indicators are fused to estimate a threat level, consolidated by incorporating contextual information including a vehicle location and time. Different actions are taken based on the consolidated threat level.
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