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US Patent 11886199 Multi-scale driving environment prediction with hierarchical spatial temporal attention

Patent 11886199 was granted and assigned to Toyota Motor Engineering & Manufacturing North America on January, 2024 by the United States Patent and Trademark Office.

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Contents

Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent
1

Patent attributes

Patent Applicant
Toyota Motor Engineering & Manufacturing North America
Toyota Motor Engineering & Manufacturing North America
1
Current Assignee
Toyota Motor Engineering & Manufacturing North America
Toyota Motor Engineering & Manufacturing North America
1
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
1
Patent Number
118861991
Patent Inventor Names
Kentaro Oguchi1
Yongkang Liu1
Xuewei Qi1
Date of Patent
January 30, 2024
1
Patent Application Number
175004471
Date Filed
October 13, 2021
1
Patent Citations
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US Patent 9165477 Systems and methods for building road models, driver models, and vehicle models and making predictions therefrom
1
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US Patent 9360328 Apparatus and method for recognizing driving environment for autonomous vehicle
1
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US Patent 10496098 Road segment-based routing guidance system for autonomous driving vehicles
1
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US Patent 10611379 Integrative cognition of driver behavior
1
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US Patent 10759446 Information processing system, information processing method, and program
1
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US Patent 11073831 Autonomous driving using a standard navigation map and lane configuration determined based on prior trajectories of vehicles
1
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US Patent 6879969 System and method for real-time recognition of driving patterns
1
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US Patent 8903588 Method and system for predicting movement behavior of a target traffic object
1
Patent Primary Examiner
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Rodney A Butler
1
CPC Code
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G08G 1/167
1
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H04W 4/44
1
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H04W 4/46
1
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G06V 10/806
1
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G06V 10/82
1
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G06V 20/56
1
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G05D 1/0251
1
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G05D 1/0214
1
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Patent abstract

In accordance with one embodiment of the present disclosure, method includes obtaining multi-level environment data corresponding to a plurality of driving environment levels, encoding the multi-level environment data at each level, extracting features from the multi-level environment data at each encoded level, fusing the extracted features from each encoded level with a spatial-temporal attention framework to generate a fused information embedding, and decoding the fused information embedding to predict driving environment information at one or more driving environment levels.

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