# US 2026/0087799 A1 · Real time air perception > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0087799 A1 - Document ID: US20260087799A1 - Status: Published application - Kind: A1 - Application number: 18/896,906 - Filed: 2024-09-26 - Published: 2026-03-26 - Earliest claimed: 2024-09-26 - Assignees: Autobrains Technologies Ltd - Applicants: Autobrains Technologies Ltd - Inventors: Igal RAICHELGAUZ; Maya RAPAPORT - Web page: https://patentgrep.com/patent/US20260087799A1 ## CPC - G06V 20/182: {Network patterns, e.g. roads or rivers} - G01C 21/20: Instruments for performing navigational calculations (G01C21/24, G01C21/26 take precedence) - G01C 21/30: Map- or contour-matching - G01C 21/3602: {Input other than that of destination using image analysis, e.g. detection of road signs, lanes, buildings, real preceding vehicles using a camera} - G01C 21/3852: {Data derived from aerial or satellite images} - G06T 7/74: {involving reference images or patches} - G06V 10/764: using classification, e.g. of video objects - G06V 10/82: using neural networks - G06V 20/17: taken from planes or by drones - G06V 20/56: exterior to a vehicle by using sensors mounted on the vehicle - G06V 20/588: {Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road} - G06T 2207/10032: Satellite or aerial image; Remote sensing - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30256: Lane; Road marking - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - 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) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract A method for real time air perception, the method includes continuously obtaining, by a computerized system, aerial image signatures of patches of aerial images in accordance with a determined driving path of the vehicle, such that the patches of aerial images capture at least parts of an environment of the determined driving path for a vehicle; processing, by the computerized system and in real time, the aerial image signatures in accordance with one or more road elements within an environment along the determined driving path of the vehicle; and providing perception results, based on the processing by a classification process running with a neural network in a real time driving of the vehicle, for use in an autonomy-level driving of the vehicle. ## Claims ### Claim 1 A method of real time air perception, comprising: continuously obtaining, by a computerized system, aerial image signatures of patches of aerial images in accordance with a determined driving path of a vehicle, such that the patches of aerial images capture at least parts of an environment of the determined driving path for the vehicle; processing, by the computerized system and in real time, the aerial image signatures in accordance with one or more road elements within an environment along the determined driving path of the vehicle; and providing perception results, based on the processing by a classification process running with a neural network in a real time driving of the vehicle, for use in an autonomy-level driving of the vehicle. ### Claim 2 (depends on 1) The method according to claim 1, wherein the processing of the aerial image signatures involves providing aerial-to-vehicle fused results between the aerial image signatures and sensor signatures of sensor data captured by a sensor of the vehicle. ### Claim 3 (depends on 1) The method according to claim 1, wherein the continuously obtaining involves downloading the aerial image signatures in accordance with the determined driving path for the vehicle. ### Claim 4 (depends on 1) The method according to claim 1, wherein providing the perception results involves classifying a road element, based on the processing. ### Claim 5 (depends on 1) The method according to claim 1, wherein providing the perception results involves providing behavioral information, based on the processing, of a road users captured by the patches of the aerial images. ### Claim 6 (depends on 1) The method according to claim 1, wherein providing the perception results involves providing a prediction, based on the processing, of a future location of a road user captured by the patches of the aerial images. ### Claim 7 (depends on 1) The method according to claim 1, further comprising determining a location of the vehicle based on, at least in part, the processing of the aerial image signatures. ### Claim 8 A non-transitory computer readable medium of real time air perception, that stores instructions executable by a processor for: continuously obtaining, by a computerized system, aerial image signatures of patches of aerial images in accordance with a determined driving path of a vehicle, such that the patches of aerial images capture at least parts of an environment of the determined driving path for the vehicle; processing, by the computerized system and in real time, the aerial image signatures in accordance with one or more road elements within an environment along the determined driving path of the vehicle; and providing perception results, based on the processing by a classification process running with a neural network in a real time driving of the vehicle, for use in an autonomy-level driving of the vehicle. ### Claim 9 (depends on 8) The non-transitory computer readable medium according to claim 8, wherein the processing of the aerial image signatures involves providing aerial-to-vehicle fused results between the aerial image signatures and sensor signatures of sensor data captured by a sensor of the vehicle. ### Claim 10 (depends on 8) The non-transitory computer readable medium according to claim 8, wherein the continuously obtaining involves downloading the aerial image signatures in accordance with the determined driving path for the vehicle. ### Claim 11 (depends on 8) The non-transitory computer readable medium according to claim 8, wherein providing the perception results involves classifying a road element, based on the processing. ### Claim 12 (depends on 8) The non-transitory computer readable medium according to claim 8, wherein providing the perception results involves providing behavioral information, based on the processing, of a road users captured by the patches of the aerial images. ### Claim 13 (depends on 8) The non-transitory computer readable medium according to claim 8, wherein providing the perception results involves providing a prediction, based on the processing, of a future location of a road user captured by the patches of the aerial images. ### Claim 14 (depends on 8) The non-transitory computer readable medium according to claim 8, further storing instructions executable by a processor for determining a location of the vehicle based on, at least in part, the processing of the aerial image signatures.