Machine-learning model for processing radar datasets in multiple domains

A processing device comprising:

• one or more processors;
• and
• memory coupled to the one or more processors with instructions stored thereon, wherein the instructions, when executed by the one or more processors, enable the processing device to:
• obtain a sequence of radar datasets depicting a scene including one or more objects, each radar dataset of the sequence comprising a multi-dimensional representation of radar reflections at the one or more objects,
• determine, for each radar dataset of the sequence, a respective embedding using a pre-trained embedding module to obtain a sequence of embeddings, each embedding of the sequence of embeddings being a numerical representation of the respective radar dataset in a machine-learned latent space,
• process the sequence of embeddings in a pre-trained neural network comprising one or more attention layers, to obtain a sequence of contextualized embeddings, each contextualized embedding depending on a relationship between the respective embedding and further embeddings of the sequence,
• process the sequence of contextualized embeddings to obtain at least one prediction, and
• provide a control signal based on the at least one prediction.

A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause an apparatus to:

• obtain a sequence of radar datasets depicting a scene including one or more objects, each radar dataset of the sequence comprising a multi-dimensional representation of radar reflections at the one or more objects;
• determine, for each radar dataset of the sequence, a respective embedding using a pre-trained embedding module, to obtain a sequence of embeddings, each embedding of the sequence of embeddings being a numerical representation of the respective radar dataset in a machine-learned latent space;
• process the sequence of embeddings in a pre-trained neural network comprising one or more attention layers, to obtain a sequence of contextualized embeddings, each contextualized embedding depending on a relationship between the respective embedding and further embeddings of the sequence;
• process the sequence of contextualized embeddings to obtain at least one prediction;
• and
• provide a control signal based on the at least one prediction.