# US 2025/0095293 A1 · Systems and methods for generating maps using sliced data and transformers > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0095293 A1 - Document ID: US20250095293A1 - Status: Published application - Kind: A1 - Application number: 18/467,012 - Filed: 2023-09-14 - Published: 2025-03-20 - Earliest claimed: 2023-09-14 - Assignees: Woven By Toyota, Inc. - Applicants: Woven by Toyota, Inc. - Inventors: Jeffrey M. Walls; Ryan M. Wiesenberg - Web page: https://patentgrep.com/patent/US20250095293A1 ## CPC - G06T 17/05: Geographic models - G06V 10/762: using clustering, e.g. of similar faces in social networks - G06V 10/7715: {Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods} - G06V 20/588: {Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road} - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Systems, methods, and other embodiments described herein relate to generating maps using transformer encoding that infers lane information from sliced data. In one embodiment, a method includes generating a sequence of lateral slices for a road graph using discrete three-dimensional (3D) representations, and the sequence forms a road edge connected in the road graph that topologically describes a mapped area. The method also includes identifying parameters by channelizing the sequence individually for estimating lane boundaries along the road edge. The method also includes encoding features across the sequence by a transformer correlating context and the parameters about the lateral slices. The method also includes decoding the features across the sequence using a learning model for computing a lane structure along the road graph. ## Claims ### Claim 1 An estimation system comprising: a memory storing 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 connected in the road graph that 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 decode the features across the sequence using a learning model to compute a lane structure along the road graph. ### Claim 2 (depends on 1) The estimation system of claim 1 further including instructions to: select by the transformer a cluster of the lateral slices from the sequence near a target slice having an information gap using the context about the road edge, and the context describes visual details about the lane boundaries; estimate by the transformer a lane characteristic to fill the information gap using a structural relationship about the road edge between the target slice and the cluster; and assemble a map by organizing the target slice within the lateral slices according to the context. ### Claim 3 (depends on 2) The estimation system of claim 2, wherein the information gap is a center line of the mapped area that is missing and the cluster includes the lateral slices having the center line. ### Claim 4 (depends on 1) The estimation system of claim 1, wherein the instructions to identify the parameters further include instructions to count detection points in a bin for the lateral slices individually associated with the lane structure. ### Claim 5 (depends on 1) The estimation system of claim 1, wherein decoding the features further includes instructions to: output a characteristic that is one of a boundary shape, a boundary position, a line type, a line color, a speed limit, and a lane direction for a target slice from the sequence. ### Claim 6 (depends on 5) The estimation system of claim 5 further including instructions to: estimate a lane group by organizing the lateral slices according to the characteristic; infer missing data for the lane structure according to the lane group; and locate a start, a continuation, and an end segment of the lane group according to the missing data. ### Claim 7 (depends on 1) The estimation system of claim 1, wherein the lane structure includes locations of lane boundaries, center lines, and colors associated with the road edge. ### Claim 8 (depends on 1) The estimation system of claim 1, wherein the lateral slices are subsections of the road edge having detection points from the discrete 3D representations that are sparse. ### Claim 9 (depends on 1) The estimation system of claim 1, wherein the discrete 3D representations includes one of lane boundaries, lane lines, lane colors, and lane types having detection points that are sparse and coarse. ### Claim 10 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 connected in the road graph that 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 decode the features across the sequence using a learning model to compute a lane structure along the road graph. ### Claim 11 (depends on 10) The non-transitory computer-readable medium of claim 10 further including instructions to: select by the transformer a cluster of the lateral slices from the sequence near a target slice having an information gap using the context about the road edge, and the context describes visual details about the lane boundaries; estimate by the transformer a lane characteristic to fill the information gap using a structural relationship about the road edge between the target slice and the cluster; and assemble a map by organizing the target slice within the lateral slices according to the context. ### Claim 12 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 connected in the road graph that 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 decoding the features across the sequence using a learning model for computing a lane structure along the road graph. ### Claim 13 (depends on 12) The method of claim 12 further comprising: selecting by the transformer a cluster of the lateral slices from the sequence near a target slice having an information gap using the context about the road edge, and the context describes visual details about the lane boundaries; estimating by the transformer a lane characteristic to fill the information gap using a structural relationship about the road edge between the target slice and the cluster; and assembling a map by organizing the target slice within the lateral slices according to the context. ### Claim 14 (depends on 13) The method of claim 13, wherein the information gap is a center line of the mapped area that is missing and the cluster includes the lateral slices having the center line. ### Claim 15 (depends on 12) The method of claim 12, wherein identifying the parameters further includes counting detection points in a bin for the lateral slices individually associated with the lane structure. ### Claim 16 (depends on 12) The method of claim 12, wherein decoding the features further includes: outputting a characteristic that is one of a boundary shape, a boundary position, a line type, a line color, a speed limit, and a lane direction for a target slice from the sequence. ### Claim 17 (depends on 16) The method of claim 16 further comprising: estimating a lane group by organizing the lateral slices according to the characteristic; inferring missing data for the lane structure according to the lane group; and locating a start, a continuation, and an end segment of the lane group according to the missing data. ### Claim 18 (depends on 12) The method of claim 12, wherein the lane structure includes locations of lane boundaries, center lines, and colors associated with the road edge. ### Claim 19 (depends on 12) The method of claim 12, wherein the lateral slices are subsections of the road edge having detection points from the discrete 3D representations that are sparse. ### Claim 20 (depends on 12) The method of claim 12, wherein the discrete 3D representations include one of lane boundaries, lane lines, lane colors, and lane types having detection points that are sparse and coarse.