System and method for federated learning of self-supervised networks in automated driving systems

US12198063B2 · kind B2 · utility

Assignee

• ZENSEACT AB · SE

Inventors

• Magnus GYLLENHAMMAR · Mölnlycke, SE
• Adam TONDERSKI · Mölndal, SE

Key dates

• Filing date: Mar 13, 2023
• Grant date: Jan 14, 2025
• Expiry date: Jul 6, 2043

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N3/0455
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

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.