# US 12,007,248 B2 · Ice thickness estimation for mobile object operation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,007,248 B2 - Document ID: US12007248B2 - Status: Granted - Kind: B2 - Application number: 17/685,443 - Filed: 2022-03-03 - Published: 2023-09-07 - Granted: 2024-06-11 - Earliest claimed: 2022-03-03 - Assignees: Ford Global Technologies, LLC - Applicants: Ford Global Technologies, LLC - Inventors: Arun Dutta; Nithya Somanath; Colleen Cauvet; Collin Hurley; David Hamilton; Donald Paul Bilger; Javier Onate - Web page: https://patentgrep.com/patent/US12007248B2 ## CPC - G01C 21/3841: {Data obtained from two or more sources, e.g. probe vehicles} - B60W 40/06: Road conditions - B63B 49/00: Arrangements of nautical instruments or navigational aids (nautical measuring instruments G01C; radio navigation, analogous arrangements using other waves G01S) - B63B 79/15: for monitoring environmental variables, e.g. wave height or weather data - 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 21/3602: {Input other than that of destination using image analysis, e.g. detection of road signs, lanes, buildings, real preceding vehicles using a camera} - G01C 21/3626: {Details of the output of route guidance instructions (traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968)} - G01C 21/3691: {Retrieval, searching and output of information related to real-time traffic, weather, or environmental conditions (arrangements for giving variable traffic instructions G08G1/09)} - G01C 21/3815: {Road data} - G01C 21/3822: {Road feature data, e.g. slope data} - G01C 21/3848: {Data obtained from both position sensors and additional sensors} - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/08: Learning methods - G06V 10/82: using neural networks - G06V 20/56: exterior to a vehicle by using sensors mounted on the vehicle - B60W 2555/20: Ambient conditions, e.g. wind or rain - B60W 2556/50: of positioning data, e.g. GPS [Global Positioning System] data - G01B 15/02: for measuring thickness - G01B 21/085: {using thermal means} - G01J 2005/0077: {Imaging} - G06N 3/084: Backpropagation, e.g. using gradient descent - 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) - B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - B63B: SHIPS OR OTHER WATERBORNE VESSELS; EQUIPMENT FOR SHIPPING (arrangements of vessel ventilation, heating, cooling, or air-conditioning B63J2/00; floating substructures as supports of dredgers or soil-shifting machines E02F9/06) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G01B: MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS - G01J: MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY (light sources F21, H01J, H01K, H05B; investigating properties of materials by optical means G01N) ## Abstract Upon determining a mobile object is approaching an ice layer above a body of water, a thermal image of the ice layer is obtained. The thermal image and ambient temperature data are input to a neural network that outputs a plurality of regions of the ice layer and respective estimated thicknesses for the regions. A classification for each region is determined based on its estimated thickness and the mobile object. The classification is one of preferred or nonpreferred. The classifications for the regions are output. ## Claims ### Claim 1 A system, comprising a computer including a processor and a memory, the memory storing instructions executable by the processor programmed to: upon determining a mobile object is approaching an ice layer above a body of water, obtain a thermal image of the ice layer; input the thermal image and ambient temperature data to a neural network that outputs a plurality of regions of the ice layer and respective estimated thicknesses for the regions; determine a classification for each region based on its estimated thickness and the mobile object, wherein the classification is one of preferred or nonpreferred; predict a future time at which the classification for one region will transition between preferred and nonpreferred based on a characteristic of the mobile object and predicted ambient temperature data; and output the classifications for the regions. ### Claim 2 (depends on 1) The system of claim 1, wherein the mobile object is one of a land vehicle or a pedestrian, and wherein the instructions further include instructions to classify one of the regions as preferred based on the estimated thickness of the respective region being greater than a threshold thickness. ### Claim 3 (depends on 2) The system of claim 2, wherein the instructions further include instructions to determine the threshold thickness based on a user input. ### Claim 4 (depends on 2) The system of claim 2, wherein the instructions further include instructions to determine the threshold thickness based on a characteristic of the mobile object. ### Claim 5 (depends on 2) The system of claim 2, wherein the instructions further include instructions to classify the one of the regions as nonpreferred based on the estimated thickness of the respective region being less than or equal to the threshold thickness. ### Claim 6 (depends on 1) The system of claim 1, wherein the mobile object is a watercraft, and wherein the instructions further include instructions to classify one of the regions as preferred based on the estimated thickness of the respective region being less than or equal to a threshold thickness. ### Claim 7 (depends on 1) The system of claim 1, wherein the instructions further include instructions to determine a planned path for the mobile object based on the classifications of the regions. ### Claim 8 (depends on 7) The system of claim 7, wherein the instructions further include instructions to output the planned path for the mobile object. ### Claim 9 (depends on 7) The system of claim 7, wherein the instructions further include instructions to provide the planned path for the mobile object to a remote computer. ### Claim 10 (depends on 1) The system of claim 1, wherein the instructions further include instructions to update map data for the ice layer based on the classifications of the regions. ### Claim 11 (depends on 10) The system of claim 10, wherein the instructions further include instructions to provide the updated map data to a remote computer. ### Claim 12 (depends on 11) The system of claim 11, further comprising the remote computer including a second processor and a second memory, the second memory storing instructions executable by the second processor to update a map based on aggregated data including updated map data from a plurality of mobile objects. ### Claim 13 (depends on 1) The system of claim 1, wherein the instructions further include instructions to, upon determining the mobile object remains stationary for a duration of a timer, determining updated classifications for the region in which the mobile object is located based on an updated ambient temperature and temperature data of the mobile object. ### Claim 14 (depends on 13) The system of claim 13, wherein the updated ambient temperature is obtained upon expiration of the timer. ### Claim 15 (depends on 13) The system of claim 13, wherein the instructions further include instructions to output the updated classification. ### Claim 16 (depends on 13) The system of claim 13, wherein the instructions further include instructions to update map data for the ice layer based on the updated classification. ### Claim 17 (depends on 1) The system of claim 1, wherein the ambient temperature data includes current ambient temperature and historical ambient temperature. ### Claim 18 A method, comprising: upon determining a mobile object is approaching an ice layer above a body of water, obtaining a thermal image of the ice layer; inputting the thermal image and ambient temperature data to a neural network that outputs a plurality of regions of the ice layer and respective estimated thicknesses for the regions; determining a classification for each region based on its estimated thickness and the mobile object, wherein the classification is one of preferred or nonpreferred; predict a future time at which the classification for one region will transition between preferred and nonpreferred based on a characteristic of the mobile object and predicted ambient temperature data; and outputting the classifications for the regions. ### Claim 19 (depends on 18) The method of claim 18, further comprising determining a planned path for the mobile object based on the classifications of the regions. ### Claim 20 (depends on 18) The method of claim 18, wherein the mobile object is one of a land vehicle or a pedestrian, and wherein the instructions further include instructions to classify one of the regions as preferred or nonpreferred based on comparing the estimated thickness of the respective region to a threshold thickness.