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    AUTOMATED DETECTION OF SPECULAR REFLECTING ROAD SURFACES USING POLARIMETRIC IMAGE DATA
    5.
    发明公开
    AUTOMATED DETECTION OF SPECULAR REFLECTING ROAD SURFACES USING POLARIMETRIC IMAGE DATA 审中-公开

    公开(公告)号：US20240300518A1

    公开(公告)日：2024-09-12

    申请号：US18178748

    申请日：2023-03-06

    Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

    Inventor： Michael Baltaxe ,  Tzvi Philipp ,  Dan Levi

    IPC: B60W60/00 ,  B60K35/00 ,  G06V20/56

    CPC classification number: B60W60/001 ,  B60K35/00 ,  G06V20/588 ,  B60K35/23 ,  B60K35/28 ,  B60K2360/175

    Abstract: A detection system for a host vehicle includes a camera, global positioning system (“GPS”) receiver, compass, and electronic control unit (“ECU”). The camera collects polarimetric image data forming an imaged drive scene inclusive of a road surface illuminated by the Sun. The GPS receiver outputs a present location of the vehicle as a date-and-time-stamped coordinate set. The compass provides a directional heading of the vehicle. The ECU determines the Sun's location relative to the vehicle and camera using an input data set, including the present location and directional heading. The ECU also detects a specular reflecting area or areas on the road surface using the polarimetric image data and Sun's location, with the specular reflecting area(s) forming an output data set. The ECU then executes a control action aboard the host vehicle in response to the output data set.

    METHODS, SYSTEMS, AND APPARATUSES FOR USER-UNDERSTANDABLE EXPLAINABLE LEARNING MODELS
    6.
    发明申请
    METHODS, SYSTEMS, AND APPARATUSES FOR USER-UNDERSTANDABLE EXPLAINABLE LEARNING MODELS 有权

    公开(公告)号：US20220067511A1

    公开(公告)日：2022-03-03

    申请号：US17023515

    申请日：2020-09-17

    Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

    Inventor： Claudia V. Goldman-Shenhar ,  Michael Baltaxe

    IPC: G06N3/08 ,  G05D1/02 ,  G06N3/04 ,  G06N3/063 ,  G06K9/62 ,  G06K9/72

    Abstract: Methods, systems, and apparatuses to build an explainable user output to receive input feature data by a neural network of multiple layers of an original classifier; determine a semantic function to label data samples with semantic categories; determine a semantic accuracy for each layer of the original classifier within the neural network; compare each layer based on results from the comparison of the semantic accuracy; designate a layer based on an amount of computed semantic accuracy; extend the designated layer by a category branch to the neural network to extract semantic data samples from the semantic content to train a set of new connections of an explainable classifier to compute a set of output explanations with an accuracy measure associated each output explanation for each semantic category of the plurality of semantic categories, and compare the accuracy measure for each output explanation to generate the output explanation in a user understandable format.

    Seat belt status determining system and method
    8.
    发明授权
    Seat belt status determining system and method 有权

    公开(公告)号：US11046273B2

    公开(公告)日：2021-06-29

    申请号：US16253312

    申请日：2019-01-22

    Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

    Inventor： Michael Baltaxe ,  Ruben Mergui ,  Kobi Nistel ,  Gila Kamhi

    IPC: B60R21/015 ,  B60R22/48 ,  G06N3/08 ,  G05D1/00 ,  B60Q9/00 ,  G06N3/04 ,  G06K9/00 ,  G06K9/62

    Abstract: A seat belt status determining system and method for a vehicle that evaluate a seat belt and determine if it is buckled and if it is properly routed on a passenger. In order to evaluate the seat belt status, the system and method use seat belt sensors and cameras inside the vehicle cabin to generate data that is then analyzed in conjunction with machine learning techniques (e.g., supervised machine learning techniques implemented through the use of neural networks) to classify the seat belt status into one or more categories, such as: passenger not present, passenger present/seat belt not being worn, passenger present/seat belt being worn improperly, passenger present/seat belt being worn properly, and blocked view.

    VEHICLE WITH POLARIMETRIC IMAGE NORMALIZATION LOGIC
    9.
    发明公开
    VEHICLE WITH POLARIMETRIC IMAGE NORMALIZATION LOGIC 审中-公开

    公开(公告)号：US20240300517A1

    公开(公告)日：2024-09-12

    申请号：US18178733

    申请日：2023-03-06

    Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

    Inventor： Michael Baltaxe ,  Tzvi Philipp ,  Tomer Pe'er ,  Dan Levi

    IPC: B60W60/00 ,  G06T7/70 ,  H04N23/54

    CPC classification number: B60W60/001 ,  G06T7/70 ,  H04N23/54 ,  B60W2556/50 ,  G06T2207/10024 ,  G06T2207/30256

    Abstract: A system for a host vehicle operating on a road surface includes a polarimetric camera, a global positioning system (“GPS”) receiver, a compass, and an electronic control unit (“ECU”). The camera collects polarimetric image data of a drive scene, including a potential driving path on the road surface. The ECU receives the polarimetric image data, estimates the Sun location using the GPS receiver and compass, and computes an ideal representation of the road surface using the Sun location. The ECU normalizes the polarimetric image data such that the road surface has a normalized representation in the drive scene, i.e., an angle of linear polarization (“AoLP”) and degree of linear polarization (“DoLP”) equal predetermined fixed values. The ECU executes a control action using the normalized representation.

    Methods, systems, and apparatuses for user-understandable explainable learning models
    10.
    发明授权
    Methods, systems, and apparatuses for user-understandable explainable learning models 有权

    公开(公告)号：US11934957B2

    公开(公告)日：2024-03-19

    申请号：US17023515

    申请日：2020-09-17

    Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

    Inventor： Claudia V. Goldman-Shenhar ,  Michael Baltaxe

    IPC: G06N3/08 ,  G06N3/0464 ,  G06N3/082 ,  G06V30/262

    CPC classification number: G06N3/082 ,  G06N3/0464 ,  G06V30/274

    Abstract: Methods, systems, and apparatuses to build an explainable user output to receive input feature data by a neural network of multiple layers of an original classifier; determine a semantic function to label data samples with semantic categories; determine a semantic accuracy for each layer of the original classifier within the neural network; compare each layer based on results from the comparison of the semantic accuracy; designate a layer based on an amount of computed semantic accuracy; extend the designated layer by a category branch to the neural network to extract semantic data samples from the semantic content to train a set of new connections of an explainable classifier to compute a set of output explanations with an accuracy measure associated each output explanation for each semantic category of the plurality of semantic categories, and compare the accuracy measure for each output explanation to generate the output explanation in a user understandable format.

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