Abstract: The present invention relates to a method for training artificial neural network configured for 3D lane detection based on unlabelled image data from camera. The method includes generating a first set of 3D lane boundaries in first coordinate system based on first image, generating a second set of 3D lane boundaries in second coordinate system based on second image, transforming at least one of the second set of 3D lane boundaries and first set of 3D lane boundaries based on positional data associated with first image and second image, evaluating the first set of 3D lane boundaries against second set of 3D lane boundaries in common coordinate system in order to find matching lane pairs of first set of 3D lane boundaries and second set of 3D lane boundaries, and updating one or more model parameters of an artificial neural network based on a spatio-temporal consistency loss.

Abstract: A method for generating an annotated training data set for training a perception algorithm of an ADS of a vehicle is disclosed. The method includes obtaining a sequence of frames captured by a LiDAR sensors, predicting, using a road reference object (RRO) prediction neural network, an RRO position data set for each of a sub-set of the frames, wherein each RRO position data set includes RRO position data sub-sets for one or more RROs. Each RRO position data sub-set is related to spatial information of one RRO found in the frames, matching the PRO position data sub-sets of one frame with the PRO position data sub-sets of other frame to populate a global RRO position data set, wherein the global RRO position data set includes global RRO position data sub-sets, and forming the annotated training data set based on the sequence and the global RRO position data set.

Abstract: The present invention relates to a method for training artificial neural network configured for 3D lane detection based on unlabelled image data from camera. The method includes generating a first set of 3D lane boundaries in first coordinate system based on first image, generating a second set of 3D lane boundaries in second coordinate system based on second image, transforming at least one of the second set of 3D lane boundaries and first set of 3D lane boundaries based on positional data associated with first image and second image, evaluating the first set of 3D lane boundaries against second set of 3D lane boundaries in common coordinate system in order to find matching lane pairs of first set of 3D lane boundaries and second set of 3D lane boundaries, and updating one or more model parameters of an artificial neural network based on a spatio-temporal consistency loss.