Patents by Inventor Sivabalan Manivasagam

Sivabalan Manivasagam 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: 20210279640
    Abstract: Systems and methods for vehicle-to-vehicle communications are provided. An adverse system can obtain sensor data representative of an environment proximate to a targeted system. The adverse system can generate an intermediate representation of the environment and a representation deviation for the intermediate representation. The representation deviation can be designed to disrupt a machine-learned model associated with the target system. The adverse system can communicate the intermediate representation modified by the representation deviation to the target system. The target system can train the machine-learned model associated with the target system to detect the modified intermediate representation. Detected modified intermediate representations can be discarded before disrupting the machine-learned model.
    Type: Application
    Filed: January 15, 2021
    Publication date: September 9, 2021
    Inventors: Xuanyuan Tu, Raquel Urtasun, Tsu-shuan Wang, Sivabalan Manivasagam, Jingkang Wang, Mengye Ren
  • Publication number: 20210152996
    Abstract: Systems and methods for vehicle-to-vehicle communications are provided. An example computer-implemented method includes obtaining, by a computing system onboard a first autonomous vehicle, sensor data associated with an environment of the first autonomous vehicle. The method includes determining, by the computing system, an intermediate environmental representation of at least a portion of the environment of the first autonomous vehicle based at least in part on the sensor data. The method includes generating, by the computing system, a compressed intermediate environmental representation by compressing the intermediate environmental representation of at least the portion of the environment of the first autonomous vehicle. The method includes communicating, by the computing system, the compressed intermediate environmental representation to a second autonomous vehicle.
    Type: Application
    Filed: October 8, 2020
    Publication date: May 20, 2021
    Inventors: Sivabalan Manivasagam, Ming Liang, Bin Yang, Wenyuan Zeng, Raquel Urtasun, Tsu-shuan Wang
  • Publication number: 20210146959
    Abstract: Systems and methods for vehicle-to-vehicle communications are provided. An example computer-implemented method includes obtaining from a first autonomous vehicle, by a computing system onboard a second autonomous vehicle, a first compressed intermediate environmental representation. The first compressed intermediate environmental representation is indicative of at least a portion of an environment of the second autonomous vehicle and is based at least in part on sensor data acquired by the first autonomous vehicle at a first time. The method includes generating, by the computing system, a first decompressed intermediate environmental representation by decompressing the first compressed intermediate environmental representation. The method includes determining, by the computing system, a first time-corrected intermediate environmental representation based at least in part on the first decompressed intermediate environmental representation.
    Type: Application
    Filed: October 8, 2020
    Publication date: May 20, 2021
    Inventors: Sivabalan Manivasagam, Ming Liang, Bin Yang, Wenyuan Zeng, Raquel Urtasun, Tsu-shuan Wang
  • Publication number: 20210152997
    Abstract: Systems and methods for vehicle-to-vehicle communications are provided. An example computer-implemented method includes obtaining from a first autonomous vehicle, by a second autonomous vehicle, a first compressed intermediate environmental representation. The first compressed intermediate environmental representation is indicative of at least a portion of an environment of the second autonomous vehicle. The method includes generating a first decompressed intermediate environmental representation by decompressing the first compressed intermediate environmental representation. The method includes determining, using one or more machine-learned models, an updated intermediate environmental representation based at least in part on the first decompressed intermediate environmental representation and a second intermediate environmental representation generated by the second autonomous vehicle.
    Type: Application
    Filed: October 8, 2020
    Publication date: May 20, 2021
    Inventors: Sivabalan Manivasagam, Ming Liang, Bin Yang, Wenyuan Zeng, Raquel Urtasun, Tsu-shuan Wang
  • Publication number: 20200301799
    Abstract: The present disclosure provides systems and methods that combine physics-based systems with machine learning to generate synthetic LiDAR data that accurately mimics a real-world LiDAR sensor system. In particular, aspects of the present disclosure combine physics-based rendering with machine-learned models such as deep neural networks to simulate both the geometry and intensity of the LiDAR sensor. As one example, a physics-based ray casting approach can be used on a three-dimensional map of an environment to generate an initial three-dimensional point cloud that mimics LiDAR data. According to an aspect of the present disclosure, a machine-learned model can predict one or more dropout probabilities for one or more of the points in the initial three-dimensional point cloud, thereby generating an adjusted three-dimensional point cloud which more realistically simulates real-world LiDAR data.
    Type: Application
    Filed: March 23, 2020
    Publication date: September 24, 2020
    Inventors: Sivabalan Manivasagam, Shenlong Wang, Wei-Chiu Ma, Kelvin Ka Wing Wong, Wenyuan Zeng, Raquel Urtasun
  • Publication number: 20200160598
    Abstract: The present disclosure provides systems and methods that combine physics-based systems with machine learning to generate synthetic LiDAR data that accurately mimics a real-world LiDAR sensor system. In particular, aspects of the present disclosure combine physics-based rendering with machine-learned models such as deep neural networks to simulate both the geometry and intensity of the LiDAR sensor. As one example, a physics-based ray casting approach can be used on a three-dimensional map of an environment to generate an initial three-dimensional point cloud that mimics LiDAR data. According to an aspect of the present disclosure, a machine-learned geometry model can predict one or more adjusted depths for one or more of the points in the initial three-dimensional point cloud, thereby generating an adjusted three-dimensional point cloud which more realistically simulates real-world LiDAR data.
    Type: Application
    Filed: September 11, 2019
    Publication date: May 21, 2020
    Inventors: Sivabalan Manivasagam, Shenlong Wang, Wei-Chiu Ma, Raquel Urtasun