Weight sharing between deep learning models used in autonomous vehicles
AssigneeGM Cruise Holdings LLC
InventorBurkay Donderici
Life of this patent
Estimating the term…
Abstract
Some autonomous vehicles rely on deep learning models to generate outputs that can be used to control the autonomous vehicle. Deep learning models can be trained to fit datasets collected in a particular environment. These deep learning models may not perform as well in a different environment, and new deep learning models may need to be created and trained. Training new models can be computationally expensive, and the amount of datasets collected in a new environment for training the new models may be limited. Various techniques involving sharing parameters (e.g., weights) between deep learning models can alleviate some of these challenges.
Claims20
A method for perceiving an environment of a vehicle and controlling the vehicle, the method comprising:
- receiving, signals of the environment of the vehicle collected by sensors of the vehicle;
- providing the signals as input to a deep learning model, wherein the deep learning model comprises:
- agnostic nodes whose parameters are trained based on a first dataset;
- and dependent nodes whose parameters are trained based on a second dataset, the second dataset being different from the first dataset;
- and
- outputting, by the deep learning model, information which is used in part to control the vehicle.
A computing system for a vehicle, the vehicle having one or more sensors communicably coupled to the computing system, the one or more sensors to sense an environment of the vehicle, the computing system comprising:
- one or more processing units;
- and
- one or more non-transitory, computer-readable media having instructions and parameters of a deep learning model stored thereon;
- wherein the deep learning model comprises:
- agnostic nodes whose parameters are trained based on a first dataset;
- and dependent nodes whose parameters are trained based on a second dataset, the second dataset being different from the first dataset;
- and
- wherein the instructions when executed by the one or more processing units, causes the one or more processing units to perform the following:
- receive, signals of the environment of the vehicle collected by the one or more sensors;
- provide the signals as input to the deep learning model;
- and
- output, by the deep learning model, information which is used in part to control the vehicle.
A method for sharing parameters between deep learning models, the method comprising:
- receiving a deep learning model, wherein the deep learning model has agnostic nodes and dependent nodes, and the agnostic nodes are trained based on a first dataset representing a first environment;
- receiving a second dataset, wherein the second dataset represents a second environment different from the first environment;
- freezing parameters of the agnostic nodes of the deep learning model;
- updating parameters of the dependent nodes using the second dataset;
- and
- causing the deep learning model having updated parameters of the dependent nodes to output information which is used in part to control a vehicle in the second environment.
Dependencies are inferred from the claim text.