Patents by Inventor Tzu-Kuo Huang
Tzu-Kuo Huang has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11934027Abstract: An optical system affixed to an electronic apparatus is provided, including a first optical module, a second optical module, and a third optical module. The first optical module is configured to adjust the moving direction of a first light from a first moving direction to a second moving direction, wherein the first moving direction is not parallel to the second moving direction. The second optical module is configured to receive the first light moving in the second moving direction. The first light reaches the third optical module via the first optical module and the second optical module in sequence. The third optical module includes a first photoelectric converter configured to transform the first light into a first image signal.Type: GrantFiled: June 21, 2022Date of Patent: March 19, 2024Assignee: TDK TAIWAN CORP.Inventors: Chao-Chang Hu, Chih-Wei Weng, Chia-Che Wu, Chien-Yu Kao, Hsiao-Hsin Hu, He-Ling Chang, Chao-Hsi Wang, Chen-Hsien Fan, Che-Wei Chang, Mao-Gen Jian, Sung-Mao Tsai, Wei-Jhe Shen, Yung-Ping Yang, Sin-Hong Lin, Tzu-Yu Chang, Sin-Jhong Song, Shang-Yu Hsu, Meng-Ting Lin, Shih-Wei Hung, Yu-Huai Liao, Mao-Kuo Hsu, Hsueh-Ju Lu, Ching-Chieh Huang, Chih-Wen Chiang, Yu-Chiao Lo, Ying-Jen Wang, Shu-Shan Chen, Che-Hsiang Chiu
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Patent number: 11851087Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices associated with trajectory prediction are provided. For example, trajectory data and goal path data can be accessed. The trajectory data can be associated with an object's predicted trajectory. The predicted trajectory can include waypoints associated with waypoint position uncertainty distributions that can be based on an expectation maximization technique. The goal path data can be associated with a goal path and include locations the object is predicted to travel. Solution waypoints for the object can be determined based on application of optimization techniques to the waypoints and waypoint position uncertainty distributions. The optimization techniques can include operations to maximize the probability of each of the solution waypoints. Stitched trajectory data can be generated based on the solution waypoints. The stitched trajectory data can be associated with portions of the solution waypoints and the goal path.Type: GrantFiled: July 26, 2022Date of Patent: December 26, 2023Assignee: UATC, LLCInventors: Nemanja Djuric, Sai Bhargav Yalamanchi, Galen Clark Haynes, Tzu-Kuo Huang
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Patent number: 11835951Abstract: Systems and methods for predicting object motion and controlling autonomous vehicles are provided. In one example embodiment, a computer implemented method includes obtaining state data indicative of at least a current or a past state of an object that is within a surrounding environment of an autonomous vehicle. The method includes obtaining data associated with a geographic area in which the object is located. The method includes generating a combined data set associated with the object based at least in part on a fusion of the state data and the data associated with the geographic area in which the object is located. The method includes obtaining data indicative of a machine-learned model. The method includes inputting the combined data set into the machine-learned model. The method includes receiving an output from the machine-learned model. The output can be indicative of a plurality of predicted trajectories of the object.Type: GrantFiled: September 3, 2021Date of Patent: December 5, 2023Assignee: UATC, LLCInventors: Nemanja Djuric, Vladan Radosavljevic, Thi Duong Nguyen, Tsung-Han Lin, Jeff Schneider, Henggang Cui, Fang-Chieh Chou, Tzu-Kuo Huang
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Publication number: 20220388537Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices associated with trajectory prediction are provided. For example, trajectory data and goal path data can be accessed. The trajectory data can be associated with an object's predicted trajectory. The predicted trajectory can include waypoints associated with waypoint position uncertainty distributions that can be based on an expectation maximization technique. The goal path data can be associated with a goal path and include locations the object is predicted to travel. Solution waypoints for the object can be determined based on application of optimization techniques to the waypoints and waypoint position uncertainty distributions. The optimization techniques can include operations to maximize the probability of each of the solution waypoints. Stitched trajectory data can be generated based on the solution waypoints. The stitched trajectory data can be associated with portions of the solution waypoints and the goal path.Type: ApplicationFiled: July 26, 2022Publication date: December 8, 2022Inventors: Nemanja Djuric, Sai Bhargav Yalamanchi, Galen Clark Haynes, Tzu-Kuo Huang
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Patent number: 11420648Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices associated with trajectory prediction are provided. For example, trajectory data and goal path data can be accessed. The trajectory data can be associated with an object's predicted trajectory. The predicted trajectory can include waypoints associated with waypoint position uncertainty distributions that can be based on an expectation maximization technique. The goal path data can be associated with a goal path and include locations the object is predicted to travel. Solution waypoints for the object can be determined based on application of optimization techniques to the waypoints and waypoint position uncertainty distributions. The optimization techniques can include operations to maximize the probability of each of the solution waypoints. Stitched trajectory data can be generated based on the solution waypoints. The stitched trajectory data can be associated with portions of the solution waypoints and the goal path.Type: GrantFiled: February 29, 2020Date of Patent: August 23, 2022Assignee: UATC, LLCInventors: Nemanja Djuric, Sai Bhargav Yalamanchi, Galen Clark Haynes, Tzu-Kuo Huang
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Publication number: 20210397185Abstract: Systems and methods for predicting object motion and controlling autonomous vehicles are provided. In one example embodiment, a computer implemented method includes obtaining state data indicative of at least a current or a past state of an object that is within a surrounding environment of an autonomous vehicle. The method includes obtaining data associated with a geographic area in which the object is located. The method includes generating a combined data set associated with the object based at least in part on a fusion of the state data and the data associated with the geographic area in which the object is located. The method includes obtaining data indicative of a machine-learned model. The method includes inputting the combined data set into the machine-learned model. The method includes receiving an output from the machine-learned model. The output can be indicative of a plurality of predicted trajectories of the object.Type: ApplicationFiled: September 3, 2021Publication date: December 23, 2021Inventors: Nemanja Djuric, Vladan Radosavljevic, Thi Duong Nguyen, Tsung-Han Lin, Jeff Schneider, Henggang Cui, Fang-Chieh Chou, Tzu-Kuo Huang
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Patent number: 11112796Abstract: Systems and methods for predicting object motion and controlling autonomous vehicles are provided. In one example embodiment, a computer implemented method includes obtaining state data indicative of at least a current or a past state of an object that is within a surrounding environment of an autonomous vehicle. The method includes obtaining data associated with a geographic area in which the object is located. The method includes generating a combined data set associated with the object based at least in part on a fusion of the state data and the data associated with the geographic area in which the object is located. The method includes obtaining data indicative of a machine-learned model. The method includes inputting the combined data set into the machine-learned model. The method includes receiving an output from the machine-learned model. The output can be indicative of a plurality of predicted trajectories of the object.Type: GrantFiled: September 5, 2018Date of Patent: September 7, 2021Assignee: UATC, LLCInventors: Nemanja Djuric, Vladan Radosavljevic, Thi Duong Nguyen, Tsung-Han Lin, Jeff Schneider, Henggang Cui, Fang-Chieh Chou, Tzu-Kuo Huang
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Publication number: 20210269059Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices associated with trajectory prediction are provided. For example, trajectory data and goal path data can be accessed. The trajectory data can be associated with an object's predicted trajectory. The predicted trajectory can include waypoints associated with waypoint position uncertainty distributions that can be based on an expectation maximization technique. The goal path data can be associated with a goal path and include locations the object is predicted to travel. Solution waypoints for the object can be determined based on application of optimization techniques to the waypoints and waypoint position uncertainty distributions. The optimization techniques can include operations to maximize the probability of each of the solution waypoints. Stitched trajectory data can be generated based on the solution waypoints. The stitched trajectory data can be associated with portions of the solution waypoints and the goal path.Type: ApplicationFiled: February 29, 2020Publication date: September 2, 2021Inventors: Nemanja Djuric, Sai Bhargav Yalamanchi, Galen Clark Haynes, Tzu-Kuo Huang
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Patent number: 10579063Abstract: The present disclosure provides systems and methods for predicting the future locations of objects that are perceived by autonomous vehicles. An autonomous vehicle can include a prediction system that, for each object perceived by the autonomous vehicle, generates one or more potential goals, selects one or more of the potential goals, and develops one or more trajectories by which the object can achieve the one or more selected goals. The prediction systems and methods described herein can include or leverage one or more machine-learned models that assist in predicting the future locations of the objects. As an example, in some implementations, the prediction system can include a machine-learned static object classifier, a machine-learned goal scoring model, a machine-learned trajectory development model, a machine-learned ballistic quality classifier, and/or other machine-learned models. The use of machine-learned models can improve the speed, quality, and/or accuracy of the generated predictions.Type: GrantFiled: August 23, 2017Date of Patent: March 3, 2020Assignee: UATC, LLCInventors: Galen Clark Haynes, Ian Dewancker, Nemanja Djuric, Tzu-Kuo Huang, Tian Lan, Tsung-Han Lin, Micol Marchetti-Bowick, Vladan Radosavljevic, Jeff Schneider, Alexander David Styler, Neil Traft, Huahua Wang, Anthony Joseph Stentz
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Publication number: 20190049970Abstract: Systems and methods for predicting object motion and controlling autonomous vehicles are provided. In one example embodiment, a computer implemented method includes obtaining state data indicative of at least a current or a past state of an object that is within a surrounding environment of an autonomous vehicle. The method includes obtaining data associated with a geographic area in which the object is located. The method includes generating a combined data set associated with the object based at least in part on a fusion of the state data and the data associated with the geographic area in which the object is located. The method includes obtaining data indicative of a machine-learned model. The method includes inputting the combined data set into the machine-learned model. The method includes receiving an output from the machine-learned model. The output can be indicative of a plurality of predicted trajectories of the object.Type: ApplicationFiled: September 5, 2018Publication date: February 14, 2019Inventors: Nemanja Djuric, Vladan Radosavljevic, Thi Duong Nguyen, Tsung-Han Lin, Jeff Schneider, Henggang Cui, Fang-Chieh Chou, Tzu-Kuo Huang
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Publication number: 20190025841Abstract: The present disclosure provides systems and methods for predicting the future locations of objects that are perceived by autonomous vehicles. An autonomous vehicle can include a prediction system that, for each object perceived by the autonomous vehicle, generates one or more potential goals, selects one or more of the potential goals, and develops one or more trajectories by which the object can achieve the one or more selected goals. The prediction systems and methods described herein can include or leverage one or more machine-learned models that assist in predicting the future locations of the objects. As an example, in some implementations, the prediction system can include a machine-learned static object classifier, a machine-learned goal scoring model, a machine-learned trajectory development model, a machine-learned ballistic quality classifier, and/or other machine-learned models. The use of machine-learned models can improve the speed, quality, and/or accuracy of the generated predictions.Type: ApplicationFiled: August 23, 2017Publication date: January 24, 2019Inventors: Clark Haynes, Ian Dewancker, Nemanja Djuric, Tzu-Kuo Huang, Tian Lan, Hank Lin, Micol Marchetti-Bowick, Vladan Radosavljevic, Jeff Schneider, Alex Styler, Neil Traft, Huahua Wang, Tony Stentz
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Patent number: 7769561Abstract: A method for monitoring machine conditions is based on machine learning through the use of a statistical model. A correlation coefficient is calculated using weights assigned to each sample that indicate the likelihood that that sample is an outlier. The resulting correlation coefficient is more robust against outliers. The calculation of the weight is based on the Mahalanobis distance from the sample to the sample mean. Additionally, hierarchical clustering is applied to intuitively reveal group information among sensors. By specifying a similarity threshold, the user can easily obtain desired clustering results.Type: GrantFiled: November 27, 2006Date of Patent: August 3, 2010Assignee: Siemens CorporationInventors: Chao Yuan, Christian Balderer, Tzu-Kuo Huang, Claus Neubauer
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Publication number: 20070162241Abstract: A method for monitoring machine conditions is based on machine learning through the use of a statistical model. A correlation coefficient is calculated using weights assigned to each sample that indicate the likelihood that that sample is an outlier. The resulting correlation coefficient is more robust against outliers. The calculation of the weight is based on the Mahalanobis distance from the sample to the sample mean. Additionally, hierarchical clustering is applied to intuitively reveal group information among sensors. By specifying a similarity threshold, the user can easily obtain desired clustering results.Type: ApplicationFiled: November 27, 2006Publication date: July 12, 2007Applicant: SIEMENS CORPORATE RESEARCH, INC.Inventors: Chao Yuan, Christian Balderer, Tzu-Kuo Huang, Claus Neubauer