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), wherein the ADS comprises a self-supervised machine-learning algorithm configured to reconstruct an ingested image and a machine-learning algorithm for an in-vehicle perception module configured to detect one or more objects or free-space areas depicted in an ingested image, the method comprising:

• obtaining, by a processing unit, an image of a scene in a surrounding environment of the vehicle;
• processing, by the processing unit, the obtained image, by the machine-learning algorithm for the in-vehicle perception module, in order to obtain an output image with one or more detected objects or free-space areas, each detected object or free-space area is indicated in the output image;
• forming, by the processing unit, an evaluation dataset based on the obtained output image, wherein the evaluation dataset comprises a plurality of evaluation images corresponding to the scene comprised in the obtained image with masked permutations of the indications of the one or more detected objects or free-space areas in the output image;
• processing, by the processing unit, the evaluation dataset and the obtained image, by means of the self-supervised machine-learning algorithm, in order to obtain a reconstruction error value for each evaluation image;
• selecting, by the processing unit, an evaluation image out of the plurality of evaluation images, wherein the selected evaluation image has the highest reconstruction error value under a regularization restriction defined based on at least a size of the masked and permuted indications;
• and
• updating, by the processing unit, the machine-learning algorithm for the in-vehicle perception module based on the obtained image and the selected evaluation image, wherein the permuted indication of the selected evaluation image defines a ground truth.