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US Patent 11257371 Early warning and collision avoidance

Patent 11257371 was granted and assigned to Derq on February, 2022 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
Derq
Derq
0
Current Assignee
Derq
Derq
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
112573710
Patent Inventor Names
Rishabh Choudhary0
Amer Abufadel0
Ankit Sharma0
Georges Aoude0
Karl Jeanbart0
Date of Patent
February 22, 2022
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Patent Application Number
168794420
Date Filed
May 20, 2020
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Patent Citations
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US Patent 10565880 Early warning and collision avoidance
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US Patent 10332395 System and method for translating roadside device position data according to differential position data
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US Patent 10854079 Early warning and collision avoidance
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US Patent 10007269 Collision-avoidance system for autonomous-capable vehicle
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US Patent 10163350 Vehicular driver warnings
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US Patent 10235882 Early warning and collision avoidance
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US Patent 10252721 System and method for providing a vehicle convoy status indication
Patent Citations Received
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US Patent 11915593 Systems and methods for machine learning based collision avoidance
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US Patent 11749111 Early warning and collision avoidance
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US Patent 11763678 Early warning and collision avoidance
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US Patent 11443631 Enhanced onboard equipment
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US Patent 11475774 Systems and methods for machine learning based collision avoidance
Patent Primary Examiner
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Ramsey Refai
0
Patent abstract

Among other things, equipment is located at an intersection of a transportation network. The equipment includes an input to receive data from a sensor oriented to monitor ground transportation entities at or near the intersection. A wireless communication device sends to a device of one of the ground transportation entities, a warning about a dangerous situation at or near the intersection, there is a processor and a storage for instructions executable by the processor to perform actions including the following. A machine learning model is stored that can predict behavior of ground transportation entities at or near the intersection at a current time. The machine learning model is based on training data about previous motion and related behavior of ground transportation entities at or near the intersection. Current motion data received from the sensor about ground transportation entities at or near the intersection is applied to the machine learning model to predict imminent behaviors of the ground transportation entities. An imminent dangerous situation for one or more of the ground transportation entities at or near the intersection is inferred from the predicted imminent behaviors. The wireless communication device sends the warning about the dangerous situation to the device of one of the ground transportation entities.

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