# US 12,768,663 B2 · Real-time adaptive geospatial mapping with autonomous vehicles > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,768,663 B2 - Document ID: US12768663B2 - Status: Granted - Kind: B2 - Application number: 18/816,802 - Filed: 2024-08-27 - Published: 2026-03-05 - Granted: 2026-10-20 - Earliest claimed: 2024-08-27 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Li Bo Zhang; Hamid Majdabadi; Su Liu; Yang Liang - Web page: https://patentgrep.com/patent/US12768663B2 ## CPC - G01C 21/3461: {Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types or segments such as motorways, toll roads or ferries} - G01C 21/3415: {Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents} - G01C 21/3446: {Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags or using precalculated routes} - G01S 17/89: for mapping or imaging - G06V 10/80: Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level (multimodal speaker identification or verification G10L17/10) - G06V 10/82: using neural networks - G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S) - G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract A computer-implemented method may include receiving, by a processor set, sensor suite data from an autonomous vehicle (AV); determining, by the processor set, an AV location based on the sensor suite data; determining, by the processor set, a non-AV location based on global positioning system data; mapping, by the processor set, a digital environment based on the sensor suite data and the non-AV location; generating, by the processor set, a route in the digital environment based on the sensor suite data, the AV location, and the non-AV location, wherein the route in the digital environment is configured to avoid the AV location; and communicating, by the processor set, the route to a device of the non-AV. ## Claims ### Claim 1 A computer-implemented method, comprising: receiving, by a processor set, sensor suite data from an autonomous vehicle (AV), wherein the sensor suite data indicates that the AV has switched from operating in an autonomous driving mode to operating in a non-autonomous driving mode, or that the AV has switched from operating in a non-autonomous driving mode to operating in an autonomous driving mode; determining, by the processor set, an AV location based on the sensor suite data using a neural network trained on the sensor suite data fused with global positioning system data; determining, by the processor set, a non-AV location of a non-AV based on the global positioning system data; mapping, by the processor set, a digital environment based on the sensor suite data and the non-AV location, wherein the mapping includes extracting features from the sensor suite data to identify one or more objects within a real-world environment, and wherein the mapping further includes dynamically updating the digital environment based on changes in the extracted features; generating, by the processor set, a route for the non-AV in the digital environment based on the sensor suite data, the AV location, and the non-AV location, wherein the route is configured to avoid the AV location in response to the sensor suite data indicating that the AV has switched to operating in the autonomous driving mode, and wherein the route is dynamically modified in response to the sensor suite data indicating that the AV has switched to operating in the non-autonomous driving mode; and communicating, by the processor set, the route to a device of the non-AV. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the sensor suite data comprises a status of a sensor indicating that the AV is operating in the autonomous driving mode. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the determining the AV location comprises identifying the location of the AV based on a global positioning system and the sensor suite data. ### Claim 4 (depends on 3) The computer-implemented method of claim 3, further comprising: performing neural network processing of the sensor suite data; and updating the digital environment based on processed sensor suite data. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the determining the AV location comprises determining the AV location within a predetermined radius relative to the non-AV location. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein the mapping the digital environment is performed by an algorithm configured to: perform the feature extraction on the sensor suite data to identify vehicle, pedestrian, and object mapping data based on visual analysis performed on the sensor suite data. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the generating the route for the non-AV in the digital environment comprises finding a shortest path between a first state and a final state via a shortest pathfinding algorithm using the global positioning system data, and wherein the route in the digital environment is configured to avoid the AV location. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, further comprising: merging, via data fusion, the sensor suite data and visual analysis data; and augmenting an existing global positioning system navigation system with the merged sensor suite data and visual analysis data. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, further comprising modifying the route based on a driver input. ### Claim 10 (depends on 1) The computer-implemented method of claim 1, wherein the sensor suite data comprises AV data comprising light detection and ranging data. ### Claim 11 A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: receive a sensor suite data from an autonomous vehicle (AV), wherein the sensor suite data indicates that the AV has switched from operating in an autonomous driving mode to operating in a non-autonomous driving mode, or that the AV has switched from operating in a non-autonomous driving mode to operating in an autonomous driving mode; determine an AV location based on the sensor suite data using a neural network trained on the sensor suite data fused with global positioning system data; determine a non-AV location of a non-AV based on the global positioning system data; map a digital environment based on the sensor suite data and the non-AV location, wherein the mapping includes extracting features from the sensor suite data to identify one or more objects within a real-world environment, and wherein the mapping further includes dynamically updating the digital environment based on changes in the extracted features; generate a route for the non-AV in the digital environment based on the sensor suite data, the AV location, and the non-AV location, wherein the route is configured to avoid the AV location in response to the sensor suite data indicating that the AV has switched to operating in the autonomous driving mode, and wherein the route is dynamically modified in response to the sensor suite data indicating that the AV has switched to operating in the non-autonomous driving mode; and communicate the route to a device of the non-AV. ### Claim 12 (depends on 11) The computer program product of claim 11, wherein the sensor suite data comprises a status of a sensor indicating that the AV is operating in the autonomous driving mode. ### Claim 13 (depends on 11) The computer program product of claim 11, wherein the determining the AV location comprises identifying the location of the AV based on a global positioning system and the sensor suite data. ### Claim 14 (depends on 13) The computer program product of claim 13, wherein the program instructions are executable to: perform neural network processing of the sensor suite data; and update the digital environment based on processed sensor suite data. ### Claim 15 (depends on 11) The computer program product of claim 11, wherein the determining the AV location comprises determining the AV location within a predetermined radius relative to the non-AV location. ### Claim 16 (depends on 11) The computer program product of claim 11, wherein the mapping the digital environment is performed by an algorithm configured to: perform the feature extraction on the sensor suite data to identify vehicle, pedestrian, and object mapping data based on visual analysis performed on the sensor suite data. ### Claim 17 (depends on 11) The computer program product of claim 11, wherein the generating the route for the non-AV in the digital environment comprises finding a shortest path between a first state and a final state via a shortest pathfinding algorithm using the global positioning system data, and wherein the route in the digital environment is configured to avoid the AV location. ### Claim 18 A system comprising: a processor set, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: receive a sensor suite data from an autonomous vehicle (AV), wherein the sensor suite data indicates that the AV has switched from operating in an autonomous driving mode to operating in a non-autonomous driving mode, or that the AV has switched from operating in a non-autonomous driving mode to operating in an autonomous driving mode; determine an AV location based on the sensor suite data using a neural network trained on the sensor suite data fused with global positioning system data; determine a non-AV location of a non-AV based on the global positioning system data; map a digital environment based on the sensor suite data and the non-AV location, wherein the mapping includes extracting features from the sensor suite data to identify one or more objects within a real-world environment, and wherein the mapping further includes dynamically updating the digital environment based on changes in the extracted features; generate a route for the non-AV in the digital environment based on the sensor suite data, the AV location, and the non-AV location, wherein the route is configured to avoid the AV location in response to the sensor suite data indicating that the AV has switched to operating in the autonomous driving mode, and wherein the route is dynamically modified in response to the sensor suite data indicating that the AV has switched to operating in the non-autonomous driving mode; and communicate the route to a device of the non-AV.