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US Patent 10311312 System and method for vehicle occlusion detection

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10311312
Patent Inventor Names
Panqu Wang38
Pengfei Chen38
Zhipeng Yan38
Hongkai Yu38
Date of Patent
June 4, 2019
Patent Application Number
15796769
Date Filed
October 28, 2017
Patent Citations Received
‌
US Patent 12136030 System and method for adapting a neural network model on a hardware platform
1
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US Patent 11983630 Neural networks for embedded devices
2
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US Patent 11989847 Photorealistic image simulation with geometry-aware composition
3
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US Patent 12014553 Predicting three-dimensional features for autonomous driving
4
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US Patent 12020476 Data synthesis for autonomous control systems
5
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US Patent 12079723 Optimizing neural network structures for embedded systems
6
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US Patent 12086097 Vector computational unit
7
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US Patent 11487288 Data synthesis for autonomous control systems
8
...
Patent Primary Examiner
‌
Frederick M Brushaber
Patent abstract

A system and method for vehicle occlusion detection is disclosed. A particular embodiment includes: receiving training image data from a training image data collection system; obtaining ground truth data corresponding to the training image data; performing a training phase to train a plurality of classifiers, a first classifier being trained for processing static images of the training image data, a second classifier being trained for processing image sequences of the training image data; receiving image data from an image data collection system associated with an autonomous vehicle; and performing an operational phase including performing feature extraction on the image data, determining a presence of an extracted feature instance in multiple image frames of the image data by tracing the extracted feature instance back to a previous plurality of N frames relative to a current frame, applying the first trained classifier to the extracted feature instance if the extracted feature instance cannot be determined to be present in multiple image frames of the image data, and applying the second trained classifier to the extracted feature instance if the extracted feature instance can be determined to be present in multiple image frames of the image data.

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