# US 12,005,909 B2 · Vehicle roof assembly > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,005,909 B2 - Document ID: US12005909B2 - Status: Granted - Kind: B2 - Application number: 17/192,992 - Filed: 2021-03-05 - Published: 2021-09-16 - Granted: 2024-06-11 - Earliest claimed: 2020-03-13 - Assignees: Ford Global Technologies, LLC - Applicants: Ford Global Technologies, LLC - Inventors: Mostafa Parchami; Stephen Giardinelli; John Anthony Lockwood - Web page: https://patentgrep.com/patent/US12005909B2 ## CPC - B60R 11/04: Mounting of cameras operative during drive; Arrangement of controls thereof relative to the vehicle - B60W 50/00: Details of control systems for road vehicle drive control not related to the control of a particular sub-unit {, e.g. process diagnostic or vehicle driver interfaces} - B60R 11/00: Arrangements for holding or mounting articles, not otherwise provided for - B62D 65/02: Joining sub-units or components to, or positioning sub-units or components with respect to, body shell or other sub-units or components - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 5/04: Inference or reasoning models - G06N 20/00: Machine learning - B60R 2011/004: {outside the vehicle} - B60R 2011/0082: {collapsible, e.g. for storing after use} - B60R 2011/0085: {with adjustment by rotation in their operational position} - B60R: VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR - 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 - B62D: MOTOR VEHICLES; TRAILERS (steering, or guiding on a desired track, of agricultural machines or implements A01B69/00; wheels, castors, axles, increasing wheel adhesion B60B; vehicle tyres, tyre inflation or tyre changing B60C; connections between vehicles of a train or the like B60D; vehicles for use on rail and road, amphibious or convertible vehicles B60F; suspension arrangements B60G; heating, cooling, ventilating or other air treating devices B60H; windows, windscreens, non-fixed roofs, doors or similar devices, protective coverings for vehicles not in use B60J; propulsion plant arrangements, auxiliary drives, transmissions, controls, instrumentation or dashboards B60K; electric equipment or propulsion of electrically-propelled vehicles B60L; power supply for electrically-propelled vehicles B60M; passenger accommodation not otherwise provided for B60N; adaptations for load transportation or to carry special loads or objects B60P; arrangement of signalling or lighting devices, the mounting or supporting thereof or circuits therefor, for vehicles in general B60Q; vehicles, vehicle fittings or vehicle parts not otherwise provided for B60R; servicing, cleaning, repairing, supporting, lifting, or manoeuvring, not otherwise provided for, B60S; brake arrangements, brake control systems or parts thereof B60T; air-cushion vehicles B60V; motor-cycles, accessories therefor B62J, B62K; testing of vehicles G01M) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A system includes a shaft extendable from a vehicle roof, a sensor mount mounted to the shaft, a plurality of sensors supported by the sensor mount, and a computer communicatively coupled with one or more of the sensors to receive data from the one or more of the sensors, the computer including a processor and a memory. The memory stores instructions executable by the processor to extend the shaft to position the sensor mount to a specified height above the roof. ## Claims ### Claim 1 A system, comprising: a shaft extendable from a vehicle roof; a sensor mount mounted to the shaft; one or more sensors supported by the sensor mount; and a computer communicatively coupled with one or more of the sensors to receive data from the one or more of the sensors, and including a processor and a memory, the memory storing instructions executable by the processor to: extend the shaft to position the sensor mount to a specified height above the roof, wherein the specified height is a height based on a geographic location. ### Claim 2 (depends on 1) The system of claim 1, wherein the shaft is rotatable from a stowed position to a deployed position. ### Claim 3 (depends on 2) The system of claim 2, wherein the instructions further include instructions to rotate the shaft from the stowed position to the deployed position and, then, to extend the shaft to position the sensor mount to the specified height above the vehicle roof. ### Claim 4 (depends on 1) The system of claim 1, wherein the memory stores a plurality of specified geographic locations including the geographic location, and wherein each of the plurality of geographic locations is assigned a respective specified height of the shaft above the vehicle roof. ### Claim 5 (depends on 4) The system of claim 4, wherein the instructions further include instructions to collect respective data at each of the specified geographic locations and to train the machine learning program with the data from each of the specified geographic locations. ### Claim 6 (depends on 4) The system of claim 4, wherein each of the specified geographic locations is a location of an infrastructure element, and the respective specified height is a height of an infrastructure sensor associated with the respective specified geographic location. ### Claim 7 (depends on 1) The system of claim 1, wherein the sensor mount is rotatable relative to the shaft. ### Claim 8 (depends on 7) The system of claim 7, wherein the instructions further include instructions to rotate the sensor mount to a specified angle relative to the shaft, the specified angle specified to capture data about a roadway; wherein the specified angle is in a plane that includes the shaft. ### Claim 9 (depends on 7) The system of claim 7, wherein the instructions further include instructions to rotate the sensor mount to a specified rotation angle about the shaft, the specified rotation angle specified to capture data about a roadway; wherein the specified angle is in a plane that does not include the shaft. ### Claim 10 (depends on 1) The system of claim 1, wherein the shaft includes a base and an extension, the extension extendable from the base to the specified height. ### Claim 11 (depends on 1) The system of claim 1, wherein the instructions further include instructions to train a machine learning program with the received data. ### Claim 12 (depends on 11) The system of claim 11, wherein the machine learning program is trained to output an identified object upon receiving data from one or more of the plurality of sensors. ### Claim 13 (depends on 1) The system of claim 1, wherein the instructions further include instructions to transmit the collected data to an external server to train a machine learning program with the collected data. ### Claim 14 A method, comprising: extending a shaft extendable from a vehicle roof of a vehicle to position a sensor mount to a specified height above the vehicle roof; collecting data with one or more sensors supported by the sensor mount, including respective data at each of a plurality of specified geographic locations; and training a machine learning program with the collected data, including with the data from each of the specified geographic locations, wherein each of the geographic locations is assigned a respective specified height of the shaft above the vehicle roof. ### Claim 15 (depends on 14) The method of claim 14, further comprising rotating the shaft from a stowed position to a deployed position and, then, extending the shaft to position the sensor mount to the specified height above the vehicle roof. ### Claim 16 (depends on 14) The method of claim 14, wherein each geographic location is a location of an infrastructure element, and the respective specified height is a height of a set of infrastructure sensors. ### Claim 17 (depends on 14) The method of claim 14, wherein the machine learning program is trained to output an identified object upon receiving data from the one or more sensors.