Invention Publication
- Patent Title: SYSTEM AND METHOD FOR FEDERATED LEARNING OF SELF-SUPERVISED NETWORKS IN AUTOMATED DRIVING SYSTEMS
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Application No.: US18182629Application Date: 2023-03-13
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Publication No.: US20230297845A1Publication Date: 2023-09-21
- Inventor: Magnus GYLLENHAMMAR , Adam TONDERSKI
- Applicant: ZENSEACT AB
- Applicant Address: SE GOTHENBURG
- Assignee: ZENSEACT AB
- Current Assignee: ZENSEACT AB
- Current Assignee Address: SE GOTHENBURG
- Priority: EP 162202.0 2022.03.15
- Main IPC: G06N3/098
- IPC: G06N3/098 ; G06N3/0895 ; G05B13/04 ; G05B13/02 ; G06F8/65

Abstract:
A computer implemented method and related aspects for updating a perception function of a plurality of vehicles having an Automated Driving System (ADS) are disclosed. The method includes obtaining one or more locally updated model parameters of a self-supervised machine-learning algorithm from a plurality of remote vehicles, and updating one or more model parameters of a global self-supervised machine-learning algorithm based on the obtained one or more locally updated model parameters. Further, the method includes fine-tuning the global self-supervised machine-learning algorithm based on an annotated dataset in order to generate a fine-tuned global machine-learning algorithm comprising one or more fine-tuned model parameters. The method further includes forming a machine-learning algorithm for an in-vehicle perception module based on the fine-tuned global machine-learning algorithm, and transmitting one or more model parameters of the formed machine-learning algorithm for the in-vehicle perception module to the plurality of remote vehicles.
Public/Granted literature
- US12198063B2 System and method for federated learning of self-supervised networks in automated driving systems Public/Granted day:2025-01-14
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