Abstract: A method for generating predictive output for an Automated Driving System of a vehicle is disclosed. The method includes encoding, by processors of a first hardware platform of the vehicle, a first sensor dataset generated by a first cluster of sensors of the vehicle using first encoder layers to form an encoded first sensor dataset. The method further includes encoding, by processors of a second hardware platform, a second sensor dataset generated by a second cluster of sensors using second encoder layers to form an encoded second sensor dataset. The first and second hardware platforms are separate and sensors in each cluster is different from one another. The method further includes fusing the encoded first and second sensor dataset to form a set of fused encoded sensor data features, and generating a predictive output thereafter using decoder layers. All encoder, fusion, and decoder layers form an end-to-end trained neural network.

Abstract: A method for generating predictive output for an ADS of a vehicle is disclosed. The method includes, by a first hardware platform, encoding a first sensor dataset from a first cluster of sensors using first encoder networks and fusing the encoded data using a first sensor fusion network to form first fused sensor data features. The method further includes, by a second hardware platform, encoding a second sensor dataset from a second cluster of sensors using second encoder networks and fusing the encoded data using a second sensor fusion network to form second fused sensor data features. The first and second hardware platforms are separate, and each sensor in the first cluster differs from those in the second cluster. Thereafter, the first and second fused feature sets are fused using a third sensor fusion network to form third fused features, and generate a predictive output using decoder networks.