Patents by Inventor Ollin Boer Bohan

Ollin Boer Bohan 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: 20220138568
    Abstract: In various examples, reinforcement learning is used to train at least one machine learning model (MLM) to control a vehicle by leveraging a deep neural network (DNN) trained on real-world data by using imitation learning to predict movements of one or more actors to define a world model. The DNN may be trained from real-world data to predict attributes of actors, such as locations and/or movements, from input attributes. The predictions may define states of the environment in a simulator, and one or more attributes of one or more actors input into the DNN may be modified or controlled by the simulator to simulate conditions that may otherwise be unfeasible. The MLM(s) may leverage predictions made by the DNN to predict one or more actions for the vehicle.
    Type: Application
    Filed: November 1, 2021
    Publication date: May 5, 2022
    Inventors: Nikolai Smolyanskiy, Alexey Kamenev, Lirui Wang, David Nister, Ollin Boer Bohan, Ishwar Kulkarni, Fangkai Yang, Julia Ng, Alperen Degirmenci, Ruchi Bhargava, Rotem Aviv
  • Publication number: 20210295171
    Abstract: In various examples, past location information corresponding to actors in an environment and map information may be applied to a deep neural network (DNN)—such as a recurrent neural network (RNN)—trained to compute information corresponding to future trajectories of the actors. The output of the DNN may include, for each future time slice the DNN is trained to predict, a confidence map representing a confidence for each pixel that an actor is present and a vector field representing locations of actors in confidence maps for prior time slices. The vector fields may thus be used to track an object through confidence maps for each future time slice to generate a predicted future trajectory for each actor. The predicted future trajectories, in addition to tracked past trajectories, may be used to generate full trajectories for the actors that may aid an ego-vehicle in navigating the environment.
    Type: Application
    Filed: March 19, 2020
    Publication date: September 23, 2021
    Inventors: Alexey Kamenev, Nikolai Smolyanskiy, Ishwar Kulkarni, Ollin Boer Bohan, Fangkai Yang, Alperen Degirmenci, Ruchi Bhargava, Urs Muller, David Nister, Rotem Aviv
  • Publication number: 20210253128
    Abstract: Embodiments of the present disclosure relate to behavior planning for autonomous vehicles. The technology described herein selects a preferred trajectory for an autonomous vehicle based on an evaluation of multiple hypothetical trajectories by different components within a planning system. The various components provide an optimization score for each trajectory according to the priorities of the component and scores from multiple components may form a final optimization score. This scoring system allows the competing priorities (e.g., comfort, minimal travel time, fuel economy) of different components to be considered together. In examples, the trajectory with the best combined score may be selected for implementation. As such, an iterative approach that evaluates various factors may be used to identify an optimal or preferred trajectory for an autonomous vehicle when navigating an environment.
    Type: Application
    Filed: February 18, 2021
    Publication date: August 19, 2021
    Inventors: David Nister, Yizhou Wang, Julia Ng, Rotem Aviv, Seungho Lee, Joshua John Bialkowski, Hon Leung Lee, Hermes Lanker, Raul Correal Tezanos, Zhenyi Zhang, Nikolai Smolyanskiy, Alexey Kamenev, Ollin Boer Bohan, Anton Vorontsov, Miguel Sainz Serra, Birgit Henke