Method and system for in-vehicle self-supervised training of perception functions for an automated driving system

A computer-implemented method for updating a perception function of a vehicle having an Automated Driving System (ADS) having a self-supervised machine-learning algorithm configured to generate an output based on one or more ingested images and a machine-learning algorithm for an in-vehicle perception module trained to provide one or more in-vehicle perception functions based on one or more ingested images, the method comprising:

• obtaining one or more images of a scene in a surrounding environment of the vehicle;
• updating one or more model parameters of the self-supervised machine-learning algorithm in accordance with a self-supervised machine learning process based on the obtained one or more images;
• generating a first output by processing the obtained one or more images by using the self-supervised machine-learning algorithm;
• generating a supervisory signal for a supervised learning process based on the first output;
• and
• updating one or more model parameters of the machine-learning algorithm for the in-vehicle perception module based on the obtained one or more images and the generated supervisory signal in accordance with the supervised learning process.