Systems and methods for generating maps using sliced data and transformers

A non-transitory computer-readable medium comprising:

• instructions that when executed by a processor cause the processor to:
• generate a sequence of lateral slices for a road graph using discrete three-dimensional (3D) representations, and the sequence forms a road edge that is a road segment connected in the road graph with another road edge at a node and the road graph topologically describes a mapped area;
• identify parameters by channelizing the sequence individually to estimate lane boundaries along the road edge;
• encode features across the sequence by a transformer that correlates context and the parameters about the lateral slices, and the transformer is a network having a feed-forward architecture, and select by the transformer a cluster of the lateral slices from the sequence near a target slice having an information gap using the context and the context has visual lateral details about the road edge and the lane boundaries;
• and
• decode the features across the sequence using a learning model to compute a lane structure along the road graph.

A method comprising:

• generating a sequence of lateral slices for a road graph using discrete three-dimensional (3D) representations, and the sequence forms a road edge that is a road segment connected in the road graph with another road edge at a node and the road graph topologically describes a mapped area;
• identifying parameters by channelizing the sequence individually for estimating lane boundaries along the road edge;
• encoding features across the sequence by a transformer correlating context and the parameters about the lateral slices, and the transformer is a network having a feed-forward architecture, and selecting by the transformer a cluster of the lateral slices from the sequence near a target slice having an information gap using the context and the context has visual lateral details about the road edge and the lane boundaries;
• and
• decoding the features across the sequence using a learning model for computing a lane structure along the road graph.