Patents by Inventor Derek Xiang Ma

Derek Xiang Ma 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).

  • Publication number: 20250259455
    Abstract: A system for faster object attribute and/or intent classification may include an machine-learned (ML) architecture that processes temporal sensor data (e.g., multiple instances of sensor data received at different times) and includes a cache in an intermediate layer of the ML architecture. The ML architecture may be capable of classifying an object's intent to enter a roadway, idling near a roadway, or active crossing of a roadway. The ML architecture may additionally or alternatively classify indicator states, such as indications to turn, stop, or the like. Other attributes and/or intentions are discussed herein.
    Type: Application
    Filed: February 12, 2025
    Publication date: August 14, 2025
    Inventors: Subhasis Das, Oytun Ulutan, Yi-Ting Lin, Derek Xiang Ma
  • Publication number: 20250259454
    Abstract: Techniques for detecting, locating, and/or classifying objects based on multiple sensor data inputs received from different sensor modalities. The techniques may include receiving sensor data generated by different sensor modalities of a vehicle, the sensor data including at least first sensor data generated by a first sensor modality and second sensor data generated by a second sensor modality. In some examples, the sensor data may be input into a machine-learning pipeline. The machine-learning pipeline may be configured to determine locations of objects in an environment surrounding the vehicle based at least in part on a correlation, by the multi-attention component, of the first sensor data and the second sensor data. The techniques may also include receiving, from the machine-learning pipeline, an output indicating a location of an object in the environment.
    Type: Application
    Filed: April 30, 2025
    Publication date: August 14, 2025
    Inventors: Amir Ghaderi, Jonathan Tyler Dowdall, Derek Xiang Ma
  • Patent number: 12387500
    Abstract: Techniques for adjusting vehicle models based on environmental conditions are discussed herein. The techniques may include receiving image data representing a portion of an environment in which a vehicle is operating and inputting the image data into a machine learned model. Additionally, data representing an environmental condition associated with the environment may be received from a sensor of the vehicle to detect changes in the environmental conditions such that one or more actions associated with the machine learned model or an output of the machine learned model may be performed. Some of the techniques may also include running multiple machine learned models or multiple configurations of a machine learned model in parallel and selecting different outputs of the machine learned model(s) to adjust for changes in the environmental conditions. For instance, individual outputs may be selected based on environmental conditions, confidence scores, thresholds, etc.
    Type: Grant
    Filed: December 18, 2020
    Date of Patent: August 12, 2025
    Assignee: Zoox, Inc.
    Inventors: Kratarth Goel, Jesse Sol Levinson, Derek Xiang Ma, Justin Nordgreen, Adam Pollack, Ekaterina Hristova Taralova, Sarah Tariq
  • Patent number: 12360213
    Abstract: Techniques for segmenting and classifying a representation of aggregated sensor data from a scene are discussed herein. Sensor data may be collected during multiple traversals of a same scene, and the sensor data may be filtered to remove portions of the sensor data not relevant to road network maps. In some examples, the filtered data may be aggregated and represented in voxels of a three-dimensional voxel space, from which an image representing a top-down view of the scene may be generated, though other views are also contemplated. Operations may include segmenting and/or classifying the image e.g., by a trained machine-learned model, to associate class labels indicative of map elements (e.g., driving lane, stop line, turn lane, and the like) with segments identified in the image. Additionally, techniques may create or update road network maps based on segmented and semantically labeled image(s) of various portions of an environment.
    Type: Grant
    Filed: October 13, 2022
    Date of Patent: July 15, 2025
    Assignee: Zoox, Inc.
    Inventors: Soroush Dean Khadem, Derek Xiang Ma
  • Publication number: 20250171050
    Abstract: There is provided methods, systems, and computer-readable media for determining intention of bicycles and other person-wide vehicles. A method comprises receiving, from a first sensor of an autonomous vehicle, first sensor data relating to an external environment of the autonomous vehicle; and receiving, from a second sensor of the autonomous vehicle, second sensor data the second sensor comprising a different sensor type to the first sensor. A person-wide vehicle in proximate to the autonomous vehicle is identified. First object data associated with the person-wide vehicle is determined. Based on the first object data, a future intention of the person-wide vehicle is received from a machine-learned model. The autonomous vehicle is controlled based at least in part on the future intention of the person-wide vehicle.
    Type: Application
    Filed: January 29, 2025
    Publication date: May 29, 2025
    Inventors: Yi-Ting LIN, Derek Xiang MA, Kenneth Michael SIEBERT, Oytun ULUTAN
  • Patent number: 12299997
    Abstract: Techniques for detecting, locating, and/or classifying objects based on multiple sensor data inputs received from different sensor modalities. The techniques may include receiving sensor data generated by different sensor modalities of a vehicle, the sensor data including at least first sensor data generated by a first sensor modality and second sensor data generated by a second sensor modality. In some examples, the sensor data may be input into a machine-learning pipeline. The machine-learning pipeline may be configured to determine locations of objects in an environment surrounding the vehicle based at least in part on a correlation, by the multi-attention component, of the first sensor data and the second sensor data. The techniques may also include receiving, from the machine-learning pipeline, an output indicating a location of an object in the environment.
    Type: Grant
    Filed: September 26, 2022
    Date of Patent: May 13, 2025
    Assignee: Zoox, Inc.
    Inventors: Amir Ghaderi, Jonathan Tyler Dowdall, Derek Xiang Ma
  • Patent number: 12260651
    Abstract: A system for faster object attribute and/or intent classification may include an machine-learned (ML) architecture that processes temporal sensor data (e.g., multiple instances of sensor data received at different times) and includes a cache in an intermediate layer of the ML architecture. The ML architecture may be capable of classifying an object's intent to enter a roadway, idling near a roadway, or active crossing of a roadway. The ML architecture may additionally or alternatively classify indicator states, such as indications to turn, stop, or the like. Other attributes and/or intentions are discussed herein.
    Type: Grant
    Filed: March 29, 2024
    Date of Patent: March 25, 2025
    Assignee: Zoox, Inc.
    Inventors: Subhasis Das, Oytun Ulutan, Yi-Ting Lin, Derek Xiang Ma
  • Patent number: 12240497
    Abstract: There is provided methods, systems, and computer-readable media for determining intention of bicycles and other person-wide vehicles. A method comprises: receiving, from one or more sensors of an autonomous vehicle, sensor data relating to an external environment of the autonomous vehicle; detecting, based at least in part on the sensor data, a person-wide vehicle in the external environment proximate to the autonomous vehicle; determining, based at least in part on the sensor data and the person-wide vehicle, image data including the person-wide vehicle; inputting the image data to a machine-learned model; receiving, from the machine-learned model based on the image data, a future intention of the person-wide vehicle; and controlling the autonomous vehicle based at least in part on the future intention of the person-wide vehicle.
    Type: Grant
    Filed: September 21, 2022
    Date of Patent: March 4, 2025
    Assignee: Zoox, Inc.
    Inventors: Yi-Ting Lin, Derek Xiang Ma, Kenneth Michael Siebert, Oytun Ulutan
  • Publication number: 20250022284
    Abstract: Techniques are discussed herein for controlling autonomous vehicles within a driving environment, including generating and using bounding contours associated with objects detected in the environment. Image data may be captured and analyzed to identify and/or classify objects within the environment. Image-based and/or lidar-based techniques may be used to determine depth data associated with the objects, and a bounding contour may be determined based on the object boundaries and associated depth data. An autonomous vehicle may use the bounding contours of objects within the environment to classify the objects, predict the positions, poses, and trajectories of the objects, and determine trajectories and perform other vehicle control actions while safely navigating the environment.
    Type: Application
    Filed: June 21, 2024
    Publication date: January 16, 2025
    Inventors: Scott M. Purdy, Derek Xiang Ma, Subhasis Das, Zeng Wang
  • Publication number: 20240320985
    Abstract: A system for faster object attribute and/or intent classification may include an machine-learned (ML) architecture that processes temporal sensor data (e.g., multiple instances of sensor data received at different times) and includes a cache in an intermediate layer of the ML architecture. The ML architecture may be capable of classifying an object's intent to enter a roadway, idling near a roadway, or active crossing of a roadway. The ML architecture may additionally or alternatively classify indicator states, such as indications to turn, stop, or the like. Other attributes and/or intentions are discussed herein.
    Type: Application
    Filed: March 29, 2024
    Publication date: September 26, 2024
    Inventors: Subhasis Das, Oytun Ulutan, Yi-Ting Lin, Derek Xiang Ma
  • Patent number: 12026956
    Abstract: Techniques are discussed herein for controlling autonomous vehicles within a driving environment, including generating and using bounding contours associated with objects detected in the environment. Image data may be captured and analyzed to identify and/or classify objects within the environment. Image-based and/or lidar-based techniques may be used to determine depth data associated with the objects, and a bounding contour may be determined based on the object boundaries and associated depth data. An autonomous vehicle may use the bounding contours of objects within the environment to classify the objects, predict the positions, poses, and trajectories of the objects, and determine trajectories and perform other vehicle control actions while safely navigating the environment.
    Type: Grant
    Filed: October 28, 2021
    Date of Patent: July 2, 2024
    Assignee: Zoox, Inc.
    Inventors: Scott M. Purdy, Subhasis Das, Derek Xiang Ma, Zeng Wang
  • Patent number: 11972614
    Abstract: A system for faster object attribute and/or intent classification may include an machine-learned (ML) architecture that processes temporal sensor data (e.g., multiple instances of sensor data received at different times) and includes a cache in an intermediate layer of the ML architecture. The ML architecture may be capable of classifying an object's intent to enter a roadway, idling near a roadway, or active crossing of a roadway. The ML architecture may additionally or alternatively classify indicator states, such as indications to turn, stop, or the like. Other attributes and/or intentions are discussed herein.
    Type: Grant
    Filed: November 9, 2021
    Date of Patent: April 30, 2024
    Assignee: Zoox, Inc.
    Inventors: Oytun Ulutan, Subhasis Das, Yi-Ting Lin, Derek Xiang Ma
  • Publication number: 20240125899
    Abstract: Techniques for segmenting and classifying a representation of aggregated sensor data from a scene are discussed herein. Sensor data may be collected during multiple traversals of a same scene, and the sensor data may be filtered to remove portions of the sensor data not relevant to road network maps. In some examples, the filtered data may be aggregated and represented in voxels of a three-dimensional voxel space, from which an image representing a top-down view of the scene may be generated, though other views are also contemplated. Operations may include segmenting and/or classifying the image e.g., by a trained machine-learned model, to associate class labels indicative of map elements (e.g., driving lane, stop line, turn lane, and the like) with segments identified in the image. Additionally, techniques may create or update road network maps based on segmented and semantically labeled image(s) of various portions of an environment.
    Type: Application
    Filed: October 13, 2022
    Publication date: April 18, 2024
    Inventors: Soroush Dean Khadem, Derek Xiang Ma
  • Patent number: 11694447
    Abstract: Techniques are described for detecting whether a lane of a roadway is open or closed. Detecting a lane as being closed may include detecting an object in or near the lane, which may comprise determining a size, location, and/or classification associated with the object, and dilating the size associated with the object. The lane may be indicated as being closed if a distance between a dilated object detection and another object detection, dilated object detection, or lane extent is less than a threshold distance. The techniques may additionally or alternatively comprise determining an alternative lane shape based at least in part on one or more object detections and/or determining that one or more lanes are closed and/or uploading a lane closure and/or alternative lane shape to a central database for retrieval by/dissemination to other computing devices.
    Type: Grant
    Filed: August 23, 2021
    Date of Patent: July 4, 2023
    Assignee: Zoox, Inc.
    Inventors: Derek Xiang Ma, Zejia Zheng
  • Publication number: 20230144745
    Abstract: A system for faster object attribute and/or intent classification may include an machine-learned (ML) architecture that processes temporal sensor data (e.g., multiple instances of sensor data received at different times) and includes a cache in an intermediate layer of the ML architecture. The ML architecture may be capable of classifying an object's intent to enter a roadway, idling near a roadway, or active crossing of a roadway. The ML architecture may additionally or alternatively classify indicator states, such as indications to turn, stop, or the like. Other attributes and/or intentions are discussed herein.
    Type: Application
    Filed: November 9, 2021
    Publication date: May 11, 2023
    Inventors: Oytun Ulutan, Subhasis Das, Yi-Ting Lin, Derek Xiang Ma
  • Publication number: 20220101020
    Abstract: Techniques are disclosed for tracking objects in sensor data, such as multiple images or multiple LIDAR clouds. The techniques may include comparing segmentations of sensor data such as by, for example, determining a similarity of a first segmentation of first sensor data and a second segmentation of second sensor data. Comparing the similarity may comprise determining a first embedding associated with the first segmentation and a second embedding associated with the second segmentation and determining a distance between the first embedding and the second embedding. The techniques may improve the accuracy and/or safety of systems integrating the techniques discussed herein.
    Type: Application
    Filed: December 13, 2021
    Publication date: March 31, 2022
    Inventors: Bryce A. Evans, Derek Xiang Ma, Sarah Tariq
  • Patent number: 11200429
    Abstract: Techniques are disclosed for tracking objects in sensor data, such as multiple images or multiple LIDAR clouds. The techniques may include comparing segmentations of sensor data such as by, for example, determining a similarity of a first segmentation of first sensor data and a second segmentation of second sensor data. Comparing the similarity may comprise determining a first embedding associated with the first segmentation and a second embedding associated with the second segmentation and determining a distance between the first embedding and the second embedding. The techniques may improve the accuracy and/or safety of systems integrating the techniques discussed herein.
    Type: Grant
    Filed: December 28, 2018
    Date of Patent: December 14, 2021
    Assignee: Zoox, Inc.
    Inventors: Bryce A. Evans, Derek Xiang Ma, Sarah Tariq
  • Publication number: 20210383138
    Abstract: Techniques are described for detecting whether a lane of a roadway is open or closed. Detecting a lane as being closed may include detecting an object in or near the lane, which may comprise determining a size, location, and/or classification associated with the object, and dilating the size associated with the object. The lane may be indicated as being closed if a distance between a dilated object detection and another object detection, dilated object detection, or lane extent is less than a threshold distance. The techniques may additionally or alternatively comprise determining an alternative lane shape based at least in part on one or more object detections and/or determining that one or more lanes are closed and/or uploading a lane closure and/or alternative lane shape to a central database for retrieval by/dissemination to other computing devices.
    Type: Application
    Filed: August 23, 2021
    Publication date: December 9, 2021
    Inventors: Derek Xiang Ma, Zejia Zheng
  • Patent number: 11100339
    Abstract: Techniques are described for detecting whether a lane of a roadway is open or closed. Detecting a lane as being closed may include detecting an object in or near the lane, which may comprise determining a size, location, and/or classification associated with the object, and dilating the size associated with the object. The lane may be indicated as being closed if a distance between a dilated object detection and another object detection, dilated object detection, or lane extent is less than a threshold distance. The techniques may additionally or alternatively comprise determining an alternative lane shape based at least in part on one or more object detections and/or determining that one or more lanes are closed and/or uploading a lane closure and/or alternative lane shape to a central database for retrieval by/dissemination to other computing devices.
    Type: Grant
    Filed: May 20, 2019
    Date of Patent: August 24, 2021
    Assignee: Zoox, Inc.
    Inventors: Derek Xiang Ma, Zejia Zheng
  • Publication number: 20200372262
    Abstract: Techniques are described for detecting whether a lane of a roadway is open or closed. Detecting a lane as being closed may include detecting an object in or near the lane, which may comprise determining a size, location, and/or classification associated with the object, and dilating the size associated with the object. The lane may be indicated as being closed if a distance between a dilated object detection and another object detection, dilated object detection, or lane extent is less than a threshold distance. The techniques may additionally or alternatively comprise determining an alternative lane shape based at least in part on one or more object detections and/or determining that one or more lanes are closed and/or uploading a lane closure and/or alternative lane shape to a central database for retrieval by/dissemination to other computing devices.
    Type: Application
    Filed: May 20, 2019
    Publication date: November 26, 2020
    Inventors: Derek Xiang Ma, Zejia Zheng