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公开(公告)号：US12051247B2
公开(公告)日：2024-07-30
申请号：US16585667
申请日：2019-09-27
Applicant: DISH NETWORK L.L.C.
Inventor： Nathan McBeth , John Huynh
CPC classification number: G06V20/58 , B60W30/09 , B60W40/06 , G06N20/00 , G08G1/0112 , G08G1/0141 , H04W4/44 , B60W2554/00 , B60W2556/45
Abstract: Examples of the present disclosure describe systems and methods for detecting and remediating roadway hazards. In example aspects, a machine learning model is trained on a dataset related to roadway items. Input data may then be collected by a data collection engine and provided to a pattern recognizer. The pattern recognizer may extract roadway features (physical and non-physical) and recognized patterns from the input data and provide the extracted features to a trained machine learning model. The trained machine learning model may compare the extracted features to the model, and a risk value may be generated. The risk value may be compared to a risk value threshold. If the risk value is equal to or exceeds the risk threshold, then the input data may be classified as a roadway hazard. Remedial action may subsequently be triggered, e.g., notifying other vehicles on the roadway, notifying other devices of drivers on the roadway, and/or notifying interested third parties (e.g., government agencies and/or private entities responsible for maintaining a roadway), to decrease the frequency of vehicles colliding with roadway hazards and to increase the efficiency of remediating the identified roadway hazards.
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公开(公告)号：US20240371176A1
公开(公告)日：2024-11-07
申请号：US18776234
申请日：2024-07-17
Applicant: DISH Network L.L.C.
Inventor： Nathan McBeth , John Huynh
Abstract: Systems and methods for detecting and remediating roadway hazards are disclosed. A machine learning model is trained on a dataset related to roadway items. Input data is collected by a data collection engine and provided to a pattern recognizer. The pattern recognizer extracts roadway features and recognized patterns from the input data and provide the extracted features to a trained machine learning model. The trained model compares the extracted features to the model, and a risk value is generated. The risk value is compared to a risk value threshold. If the risk value is equal to or exceeds the risk threshold, then the input data may be classified as a roadway hazard. Remedial action is subsequently be triggered.
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公开(公告)号：US20210097311A1
公开(公告)日：2021-04-01
申请号：US16585667
申请日：2019-09-27
Applicant: DISH NETWORK L.L.C.
Inventor： Nathan McBeth , John Huynh
Abstract: Examples of the present disclosure describe systems and methods for detecting and remediating roadway hazards. In example aspects, a machine learning model is trained on a dataset related to roadway items. Input data may then be collected by a data collection engine and provided to a pattern recognizer. The pattern recognizer may extract roadway features (physical and non-physical) and recognized patterns from the input data and provide the extracted features to a trained machine learning model. The trained machine learning model may compare the extracted features to the model, and a risk value may be generated. The risk value may be compared to a risk value threshold. If the risk value is equal to or exceeds the risk threshold, then the input data may be classified as a roadway hazard. Remedial action may subsequently be triggered, e.g., notifying other vehicles on the roadway, notifying other devices of drivers on the roadway, and/or notifying interested third parties (e.g., government agencies and/or private entities responsible for maintaining a roadway), to decrease the frequency of vehicles colliding with roadway hazards and to increase the efficiency of remediating the identified roadway hazards.
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