Patents by Inventor KUAN X. LIU

KUAN X. LIU 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).

  • Vehicle velocity predictor using neural networks based on V2X data augmentation to enable predictive optimal control of connected and automated vehicles
    Patent number: 10814881
    Abstract: Some implementations of the disclosure are directed to reducing or removing time lag in vehicle velocity prediction by training a model for vehicle velocity prediction using labeled features that provide indication of a feature associated with a vehicle acceleration or deacceleration event. In one implementation, a method includes: receiving multiple time series datasets, each of the time series datasets including sensor data, GPS data, and vehicle state data collected over time; extracting features from each of the time series datasets that are indicative of a future velocity of a vehicle; labeling the extracted features of each of the time series datasets to indicate vehicle acceleration or deacceleration events; and after labeling the extracted features of each of the time series datasets, using at least a subset of the extracted and labeled time series datasets to train a machine learning model that predicts vehicle velocity some time into the future.
    Type: Grant
    Filed: October 16, 2018
    Date of Patent: October 27, 2020
    Assignees: TOYOTA MOTOR ENGINEERING & MANUFACTURING NORTH AMERICA, INC., THE REGENTS OF THE UNIVERSITY OF MICHIGAN
    Inventors: Kuan X. Liu, Mike X. Huang, Ilya V. Kolmanovsky
  • VEHICLE VELOCITY PREDICTOR USING NEURAL NETWORKS BASED ON V2X DATA AUGMENTATION TO ENABLE PREDICTIVE OPTIMAL CONTROL OF CONNECTED AND AUTOMATED VEHICLES
    Publication number: 20200114926
    Abstract: Some implementations of the disclosure are directed to reducing or removing time lag in vehicle velocity prediction by training a model for vehicle velocity prediction using labeled features that provide indication of a feature associated with a vehicle acceleration or deacceleration event. In one implementation, a method includes: receiving multiple time series datasets, each of the time series datasets including sensor data, GPS data, and vehicle state data collected over time; extracting features from each of the time series datasets that are indicative of a future velocity of a vehicle; labeling the extracted features of each of the time series datasets to indicate vehicle acceleration or deacceleration events; and after labeling the extracted features of each of the time series datasets, using at least a subset of the extracted and labeled time series datasets to train a machine learning model that predicts vehicle velocity some time into the future.
    Type: Application
    Filed: October 16, 2018
    Publication date: April 16, 2020
    Inventors: KUAN X. LIU, Mike X. HUANG, ILYA V. KOLMANOVSKY