IP Library Granted Patent US 12,600,253
Granted Patent B2
US 12,600,253 · App. 18/415,470 · Granted Apr 14, 2026

Systems and methods for the localization and navigation of a vehicle to a charge station

Inventors: Meghna Menon (Ann Arbor, MI); Smruti Panigrahi (Novi, MI); Gregory P. Linkowski (Dearborn, MI); Mario Anthony Santillo (Canton, MI)
Assignee: Ford Global Technologies, LLC
B60L53/36B60L53/37B60L53/38B60W30/06B60W60/001B60W2420/90B60W2520/06B60W2556/50
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Quick Facts
Patent No.
US 12,600,253
App. No.
18/415,470
Granted
Apr 14, 2026
Kind
B2
Abstract

A method of marshaling an autonomously operated vehicle including the maneuvering of a vehicle toward a position and orientation associated with a charging station, the receipt of location data associated with a first position of the vehicle, and the initiation of an approach to engage the charging station based on updated location data associated with a second position of the vehicle.

Claims (36)

1 . A method of marshaling an autonomously operated vehicle, the method comprising:

maneuvering, by a vehicle control system, the vehicle toward a position and orientation associated with a charging station, wherein the maneuvering of the vehicle toward the position and orientation associated with the charging station is based on one or more instructions received from an infrastructure system;

receiving, from one or more in-ground positioning sensors over which the vehicle travels, location data associated with a first position of the vehicle; and

initiating, based on updated location data associated with a second position of the vehicle, an approach to engage the charging station, wherein the updated location data is received from the one or more in-ground positioning sensors, and wherein the updated location data is based on the location data.

2 . The method of claim 1 , further comprising:

determining, based on the location data, that a direction of travel associated with the vehicle is outside an engageable distance from the charging station; and

re-localizing, based on the determination that the direction of travel associated with the vehicle is outside the engageable distance from the charging station, the vehicle, wherein the re-localized vehicle is re-directed toward the engageable distance from the charging station.

3 . The method of claim 1 , wherein the position associated with the charging station is a global coordinate position and the one or more in-ground positioning sensors include pressure sensors, magnets, ultrasonics, proximity sensors, ultra-wide band tags, RFID tags, or a combination thereof.

4 . The method of claim 1 , wherein the location data and the updated location data are received via one or more of: ultra-wide band, Bluetooth®, WIFI, CV2X, a public cellular network, or a private cellular network.

5 . The method of claim 1 , wherein maneuvering the vehicle toward the position and orientation associated with the charging station further comprises:

determining positional data associated with the first position and the second position of the vehicle.

6 . The method of claim 5 , wherein the positional data is based on one or more of: a deep learning model used to detect the charging station via a camera associated with the vehicle; a fiducial associated with the charging station identified via the camera, ultrasonics, or radar associated with the vehicle; or an alignment with a wheel chock and at least one wheel of the vehicle.

7 . The method of claim 6 , wherein the alignment with the wheel chock and the at least one wheel of the vehicle guides the vehicle to within a prespecified distance to the charging station, and wherein the prespecified distance to the charging station is within a range acceptable for an arm associated with the charging station to be able to plug a charger into a charging port of the vehicle.

8 . A method of marshaling an autonomously operated vehicle, the method comprising:

receiving location data from one or more in-ground positioning sensors over which the vehicle travels;

determining, based on a deep learning model and the location data, positional data associated with the vehicle, wherein the deep learning model is used to detect a charging station via a camera associated with the vehicle, wherein the determination of the positional data associated with the vehicle is made by a vehicle control system;

initiating, based on the positional data and an identification of the charging station, an approach to engage the charging station; and

maneuvering the vehicle, by a vehicle control system, toward a position and orientation associated with the charging station based on one or more instructions received from an infrastructure system.

9 . The method of claim 8 , wherein the positional data is associated with a position of the vehicle and the position is a global coordinate position.

10 . The method of claim 8 , wherein the one or more in-ground positioning sensors include pressure sensors, magnets, ultrasonics, proximity sensors, ultra-wide band tags, RFID tags, or a combination thereof.

11 . The method of claim 8 , wherein the location data is received via one or more of: ultra-wide band, Bluetooth®, WIFI, CV2X, a public cellular network, or a private cellular network.

12 . The method of claim 8 , wherein the vehicle is further configured to determine the positional data based on one or more of a fiducial associated with the charging station, identified via the camera, ultrasonics, or radar associated with the vehicle, or an alignment with a wheel chock and at least one wheel of the vehicle.

13 . The method of claim 12 , wherein the alignment with the wheel chock and the at least one wheel of the vehicle guides the vehicle to within a prespecified distance to the charging station, and wherein the prespecified distance to the charging station is within a range acceptable for an arm associated with the charging station to be able to plug a charger into a charging port of the vehicle.

14 . One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by at least one processor, cause the at least one processor to:

maneuver a vehicle toward a position and orientation associated with a charging station, wherein the maneuvering of the vehicle toward the position and orientation associated with the charging station is based on one or more instructions received from an infrastructure system;

receive, from one or more in-ground positioning sensors over which the vehicle travels, location data associated with a first position of the vehicle; and

initiate, based on updated location data associated with a second position of the vehicle, an approach to engage the charging station, wherein the updated location data is received from the one or more in-ground positioning sensors, and wherein the updated location data is based on the location data.

15 . The one or more non-transitory computer-readable media of claim 14 , wherein the at least one processor is further caused to:

determine, based on the location data, that a direction of travel associated with the vehicle is outside an engageable distance from the charging station; and

re-localize, based on the determination that the direction of travel associated with the vehicle is outside the engageable distance from the charging station, the vehicle, wherein the re-localized vehicle is re-directed toward the engageable distance from the charging station.

16 . The one or more non-transitory computer-readable media of claim 14 , wherein the position associated with the charging station is a global coordinate position and the one or more in-ground positioning sensors include pressure sensors, magnets, ultrasonics, proximity sensors, ultra-wide band tags, RFID tags, or a combination thereof.

17 . The one or more non-transitory computer-readable media of claim 14 , wherein the location data and the updated location data is received via one or more of: ultra-wide band, Bluetooth®, Wi-Fi, CV2X, a public cellular network, or a private cellular network.

18 . The one or more non-transitory computer-readable media of claim 14 , wherein the processor-executable instructions that, when executed by the at least one processor, maneuver the vehicle toward the position and orientation associated with the charging station, further causes the at least one processor to:

determine positional data associated with the first position and the second position of the vehicle.

19 . The one or more non-transitory computer-readable media of claim 14 , wherein the positional data is based on one or more of: a deep learning model used to detect the charging station via a camera associated with the vehicle; a fiducial associated with the charging station identified via the camera, ultrasonics, or radar associated with the vehicle; or an alignment with a wheel chock and at least one wheel of the vehicle.

20 . The one or more non-transitory computer-readable media of claim 19 , wherein the alignment with the wheel chock and the at least one wheel of the vehicle guides the vehicle to within a prespecified distance to the charging station, and wherein the prespecified distance to the charging station is within a range acceptable for an arm associated with the charging station to be able to plug a charger into a charging port of the vehicle.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 9, 2024
From: MENON, MEGHNA; SANTILLO, MARIO ANTHONY; PANIGRAHI, SMRUTI; LINKOWSKI, GREGORY P.
To: FORD GLOBAL TECHNOLOGIES, LLC
Reel/Frame 066427/0950 →
Continuity (1)
Related Publication 20250229660A1 · Jul 17, 2025
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