Patents by Inventor Wenyuan Zeng

Wenyuan Zeng 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: 20210278852
    Abstract: Systems and methods for generating attention masks are provided. In particular, a computing system can access sensor data and map data for an area around an autonomous vehicle. The computing system can generate a voxel grid representation of the sensor data and map data. The computing system can generate an attention mask based on the voxel grid representation. The computing system can generate, by using the voxel grid representation and the attention mask as input to a machine-learned model, an attention weighted feature map. The computing system can determine using the attention weighted feature map, a planning cost volume for an area around the autonomous vehicle. The computing system can select a trajectory for the autonomous vehicle based, at least in part, on the planning cost volume.
    Type: Application
    Filed: January 15, 2021
    Publication date: September 9, 2021
    Inventors: Raquel Urtasun, Bob Qingyuan Wei, Mengye Ren, Wenyuan Zeng, Ming Liang, Bin Yang
  • Publication number: 20210149404
    Abstract: The present disclosure is directed to generating trajectories using a structured machine-learned model. In particular, a computing system can obtain sensor data for an area around an autonomous vehicle. The computing system can detect one or more objects based on the sensor data. The computing system can determine a plurality of candidate object trajectories for each object in the one or more objects. The computing system can generate, using the plurality of candidate object trajectories as input to one or more machine-learned models, likelihood data for the plurality of candidate object trajectories. The computing system can update the likelihood values for each of the plurality of candidate object trajectories for each respective object in the one or more objects based on the likelihood values associated with candidate object trajectories for other objects in the one or more objects. The computing system can determine a motion plan for the autonomous vehicle.
    Type: Application
    Filed: September 16, 2020
    Publication date: May 20, 2021
    Inventors: Wenyuan Zeng, Shenlong Wang, Renjie Liao, Yun Chen, Bin Yang, Raquel Urtasun
  • 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: 20210146963
    Abstract: A computing system can input first relative location embedding data into an interaction transformer model and receive, as an output of the interaction transformer model, motion forecast data for actors relative to a vehicle. The computing system can input the motion forecast data into a prediction model to receive respective trajectories for the actors for a current time step and respective projected trajectories for the actors for a subsequent time step. The computing system can generate second relative location embedding data based on the respective projected trajectories from the second time step. The computing system can produce second motion forecast data using the interaction transformer model based on the second relative location embedding. The computing system can determine second respective trajectories for the actors using the prediction model based on the second forecast data.
    Type: Application
    Filed: September 2, 2020
    Publication date: May 20, 2021
    Inventors: Lingyun Li, Bin Yang, Wenyuan Zeng, Ming Liang, Mengye Ren, Sean Segal, Raquel Urtasun
  • 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: 20210009166
    Abstract: A computing system can be configured to input data that describes sensor data into an object detection model and receive, as an output of the object detection model, object detection data describing features of the plurality of the actors relative to the autonomous vehicle. The computing system can generate an input sequence that describes the object detection data. The computing system can analyze the input sequence using an interaction model to produce, as an output of the interaction model, an attention embedding with respect to the plurality of actors. The computing system can be configured to input the attention embedding into a recurrent model and determine respective trajectories for the plurality of actors based on motion forecast data received as an output of the recurrent model.
    Type: Application
    Filed: February 26, 2020
    Publication date: January 14, 2021
    Inventors: Lingyun Li, Bin Yang, Ming Liang, Wenyuan Zeng, Mengye Ren, Sean Segal, Raquel Urtasun
  • 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: 20200159225
    Abstract: Systems and methods for generating motion plans including target trajectories for autonomous vehicles are provided. An autonomous vehicle may include or access a machine-learned motion planning model including a backbone network configured to generate a cost volume including data indicative of a cost associated with future locations of the autonomous vehicle. The cost volume can be generated from raw sensor data as part of motion planning for the autonomous vehicle. The backbone network can generate intermediate representations associated with object detections and objection predictions. The motion planning model can include a trajectory generator configured to evaluate one or more potential trajectories for the autonomous vehicle and to select a target trajectory based at least in part on the cost volume generate by the backbone network.
    Type: Application
    Filed: August 15, 2019
    Publication date: May 21, 2020
    Inventors: Wenyuan Zeng, Wenjie Luo, Abbas Sadat, Bin Yang, Raquel Urtasun