Methods and systems for autonomous vehicle recharging or refueling are disclosed. Autonomous electric vehicles may be automatically recharged by routing the vehicles to available charging stations when not in operation, according to methods described herein. A charge level of the battery of an auton
Methods and systems for autonomous vehicle recharging or refueling are disclosed. Autonomous electric vehicles may be automatically recharged by routing the vehicles to available charging stations when not in operation, according to methods described herein. A charge level of the battery of an autonomous electric vehicle may be monitored until it reaches a recharging threshold, at which point an on-board computer may generate a predicted use profile for the vehicle. Based upon the predicted use profile, a time and location for the vehicle to recharge may be determined. In some embodiments, the vehicle may be controlled to automatically travel to a charging station, recharge the battery, and return to its starting location in order to recharge when not in use.
대표청구항▼
1. A computer-implemented method for automatically recharging an autonomous electric vehicle, comprising: detecting, by one or more sensors disposed within the autonomous electric vehicle, charge information associated with a charge level of a battery of the autonomous electric vehicle;determining,
1. A computer-implemented method for automatically recharging an autonomous electric vehicle, comprising: detecting, by one or more sensors disposed within the autonomous electric vehicle, charge information associated with a charge level of a battery of the autonomous electric vehicle;determining, by one or more processors, the charge level of the battery based upon the charge information;generating, by the one or more processors, a predicted use profile for the autonomous electric vehicle based upon prior vehicle use data;determining, by the one or more processors, a time and a location at which to charge the battery based upon the charge level and the predicted use profile;controlling, by the one or more processors, the autonomous electric vehicle to travel fully autonomously to the determined location at the determined time;causing, by the one or more processors, the battery of the autonomous electric vehicle to charge at the location;determining, by the one or more processors, a return location for the vehicle based upon the predicted use profile; andcontrolling, by the one or more processors, the autonomous electric vehicle to travel fully autonomously to the return location. 2. The computer-implemented method of claim 1, wherein: the charge information is determined when the autonomous electric vehicle is not in use; andthe determined time is a current time at which the time and location are determined. 3. The computer-implemented method of claim 1, further comprising: determining, by the one or more processors, that the charge level is below a maximum recharging threshold,wherein the time and location are determined when the charge level is below the maximum recharging threshold. 4. The computer-implemented method of claim 3, wherein: the predicted use profile indicates a next predicted use of the autonomous electric vehicle; andthe time and location are determined when sufficient time exists to recharge the battery before the next predicted use. 5. The computer-implemented method of claim 1, wherein: the charge information is determined when the autonomous electric vehicle is in use;the predicted use profile includes one or more predicted breaks in vehicle operation, each predicted break being associated with a break time and a break location; andthe time and location are determined based upon the one or more predicted breaks. 6. The computer-implemented method of claim 1, wherein the location at which to charge the battery is associated with a charging station selected from a plurality of charging stations based at least in part upon availability of the selected charging station. 7. The computer-implemented method of claim 1, wherein the return location is determined based upon the predicted use profile and is distinct from a prior location from which the autonomous electric vehicle travels to the location at which to charge the battery. 8. The computer-implemented method of claim 1, further comprising: identifying, using one or more geolocation components within the autonomous electric vehicle, a current location of the autonomous electric vehicle; andidentifying, by the one or more processors, one or more charging stations in an area surrounding the current location from a database including location data for a plurality of charging stations,wherein the location at which to charge the battery is selected from the location data associated with the one or more charging stations based at least in part upon distance from the current location. 9. The computer-implemented method of claim 8, further comprising: accessing, by the one or more processors, map data containing map information regarding a plurality of road segments, the map information including location data associated with each road segment and an indication of suitability for autonomous operation feature use associated with each road segment; andidentifying, by the one or more processors, a route consisting of one or more road segments from the plurality of road segments between the current location and the location at which to charge the battery,wherein controlling the autonomous electric vehicle to travel fully autonomously to the determined location includes controlling the autonomous electric vehicle along the identified route. 10. The computer-implemented method of claim 1, wherein the predicted use profile indicates a plurality of use periods and use locations over at least one day. 11. A computer system for automatically recharging an autonomous electric vehicle, comprising: one or more processors disposed within the autonomous electric vehicle;one or more sensors disposed within the autonomous electric vehicle and communicatively connected to the one or more processors; anda program memory coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the computer system to: detect charge information associated with a charge level of a battery of the autonomous electric vehicle using the one or more sensors;determine the charge level of the battery based upon the charge information;generate a predicted use profile for the autonomous electric vehicle based upon prior vehicle use data;determine a time and a location at which to charge the battery based upon the charge level and the predicted use profile;control the autonomous electric vehicle to travel fully autonomously to the determined location at the determined time;cause the battery of the autonomous electric vehicle to charge at the location;determine a return location for the vehicle based upon the predicted use profile; andcontrol the autonomous electric vehicle to travel fully autonomously to the return location. 12. The computer system of claim 11, wherein: the predicted use profile indicates a next predicted use of the autonomous electric vehicle; andthe time and location are determined when sufficient time exists to recharge the battery before the next predicted use. 13. The computer system of claim 11, wherein the location at which to charge the battery is associated with a charging station selected from a plurality of charging stations based at least in part upon availability of the selected charging station. 14. The computer system of claim 11, wherein the return location is determined based upon the predicted use profile and is distinct from a prior location from which the autonomous electric vehicle travels to the location at which to charge the battery. 15. The computer system of claim 11, wherein: the executable instructions further cause the computer system to: identify a current location of the autonomous electric vehicle using one or more geolocation components within the autonomous electric vehicle; andidentify one or more charging stations in an area surrounding the current location from a database including location data for a plurality of charging stations; andthe location at which to charge the battery is selected from the location data associated with the one or more charging stations based at least in part upon distance from the current location. 16. The computer system of claim 11, wherein the predicted use profile indicates a plurality of use periods and use locations over at least one day. 17. A tangible, non-transitory computer-readable medium storing executable instructions for automatically recharging an autonomous electric vehicle that, when executed by at least one processor of a computer system, cause the computer system to: detect charge information associated with a charge level of a battery of the autonomous electric vehicle using one or more sensors disposed within the autonomous electric vehicle;determine the charge level of the battery based upon the charge information;generate a predicted use profile for the autonomous electric vehicle based upon prior vehicle use data;determine a time and a location at which to charge the battery based upon the charge level and the predicted use profile;control the autonomous electric vehicle to travel fully autonomously to the determined location at the determined time;cause the battery of the autonomous electric vehicle to charge at the location;determine a return location for the vehicle based upon the predicted use profile; andcontrol the autonomous electric vehicle to travel fully autonomously to the return location. 18. The tangible, non-transitory computer-readable medium of claim 17, wherein: the predicted use profile indicates a next predicted use of the autonomous electric vehicle; andthe time and location are determined when sufficient time exists to recharge the battery before the next predicted use. 19. The tangible, non-transitory computer-readable medium of claim 17, wherein the location at which to charge the battery is associated with a charging station selected from a plurality of charging stations based at least in part upon availability of the selected charging station. 20. The tangible, non-transitory computer-readable medium of claim 17, further storing instructions that cause the computer system to: identify a current location of the autonomous electric vehicle using one or more geolocation components within the autonomous electric vehicle; andidentify one or more charging stations in an area surrounding the current location from a database including location data for a plurality of charging stations,wherein the location at which to charge the battery is selected from the location data associated with the one or more charging stations based at least in part upon distance from the current location.
연구과제 타임라인
LOADING...
LOADING...
LOADING...
LOADING...
LOADING...
이 특허에 인용된 특허 (157)
Fields, Brian Mark; Roberson, Steve; Chan, Aaron Scott; Sanchez, Kenneth Jason; Donovan, Matthew Christopher Jon, Advanced vehicle operator intelligence system.
Rupp, Matt Y.; Engelman, Gerald H.; Miller, Alex Maurice; Zwicky, Timothy D.; Tellis, Levasseur; Stephenson, Richard Lee, Autonomous control in a dense vehicle environment.
Christensen, Scott T.; Hayward, Gregory; Gay, Christopher E.; Cielocha, Steven; Binion, Todd, Dynamic auto insurance policy quote creation based on tracked user data.
Kelley Ishmael C. (6625 Piney Br. Rd. N.W. Washington DC 20012) Brailsford Lawrence J. (500-23rd St. N.W. Washington DC 20037), Electronic alarm signaling system.
Summerville David F. (Garland TX) Williston John P. (Plano TX) Wand Martin A. (Plano TX) Doty Thomas J. (Dallas TX), Hierarchical control system for automatically guided vehicles.
Grimm, Donald K.; Bai, Fan; Ebrahimian, Rozalina, Method and apparatus for determining traffic safety events using vehicular participative sensing systems.
James Secreet ; David Richard Capo ; Herbert H. Ohliger, III, Method and apparatus for measuring and recording vehicle speed and for storing related data.
Galley, Lars; Hentschel, Elisabeth Hendrika; Kuhn, Klaus-Peter; Stolzmann, Wolfgang, Method and control device for recognising inattentiveness according to at least one parameter which is specific to a driver.
Fuehrer, Thomas, Method and device for supplying a collision signal pertaining to a vehicle collision, a method and device for administering collision data pertaining to vehicle collisions, as well as a method and device for controlling at least one collision protection device of a vehicle.
Blumer, Frederick T.; Berkobin, Eric C.; Holmes, Randy, Method and system for adjusting a charge related to use of a vehicle during a period based on operational performance data.
Nielsen, Steven; Chambers, Curtis; Farr, Jeffrey, Methods, apparatus, and systems for acquiring and analyzing vehicle data and generating an electronic representation of vehicle operations.
Bauer, Alan Rex; Burns, Kurtis Tavis; Esposito, Michael Vincent; Huber, Jr., David Charles; O'Malley, Patrick Lawrence, Monitoring system for determining and communicating a cost of insurance.
Bauer, Alan Rex; Burns, Kurtis Tavis; Esposito, Michael Vincent; O'Malley, Patrick Lawrence; Olexa, Byron John; McMillan, Robert John, Monitoring system for determining and communicating a cost of insurance.
Lowrey, Larkin H.; Borrego, Diego A.; Wettig, Alan; Lightner, Bruce Davis; Banet, Matthew J.; Washicko, Paul; Berkobin, Eric C.; Link, II, Charles M., Peripheral access devices and sensors for use with vehicle telematics devices and systems.
Fields, Brian M.; He, Jibo; Nepomuceno, John Adrian; Roberson, Steve; Plummer, Bryan Allen; Houdek, Kurtis C.; Jain, Neeraj, Real-time driver observation and scoring for driver's education.
Brandmaier, Jennifer A.; Balabas, Jason; Spinneweber, Robert A.; Lieggi, Christopher J.; Chen, Tao, Roadside assistance service provider assignment system.
Douros, Kenneth; Gardner, Judith Lee; Gardner, Robert Michael; Hurwitz, Joshua B.; Leivian, Robert H.; Nagel, Jens; Remboski, Donald; Wheatley, David John; Wood, Clifford A., System and method for driver performance improvement.
Gay, Chris; McKinney, John C.; Duncan, Leonard D.; Fellows, Jeffrey R., System and method for the assessment, pricing, and provisioning of distance-based vehicle insurance.
Gay, Chris; McKinney, John C.; Duncan, Leonard D.; Fellows, Jeffrey R., System and method for the assessment, pricing, and provisioning of distance-based vehicle insurance.
Gay, Chris; McKinney, John C.; Fellows, Jeffrey R.; Duncan, Leonard D., System and method for the assessment, pricing, and provisioning of distance-based vehicle insurance.
Jenkins Paul C. ; Deal David V. ; Cuthbertson Thomas G. ; Morton James W. ; Smith Andrew D. ; Hoy David R. ; Egeberg Gerald W., System for monitoring vehicle efficiency and vehicle and driver performance.
Medina, III, Reynaldo; Oakes, III, Charles Lee; Billman, Bradly Jay; Bueché, Jr., Michael Patrick, Systems and methods for automobile accident claims initiation.
Hyde, Roderick A.; Kare, Jordin T.; Tuckerman, David B., Systems and methods for insurance based on monitored characteristics of an autonomous drive mode selection system.
Hattori Atsushi,JPX ; Tsuchiya Akiyoshi,JPX ; Enomoto Hiroyuki,JPX ; Osawa Keiichi,JPX, Vehicle information communication system and method capable of communicating with external management station.
Everett, William Curtis; Hutchinson, Richard Ashton; Steigerwald, III, Wilbert John; Say, William Andrew; O'Malley, Patrick Lawrence; Shrallow, Dane Allen; Ling, Raymond Scott; McMillan, Robert John, Vehicle monitoring system.
Ling, Raymond Scott; Hutchinson, Richard Ashton; Steigerwald, III, Wilbert John; Say, William Andrew; O'Malley, Patrick Lawrence; Shrallow, Dane Allen; Everett, William Curtis; McMillan, Robert John, Vehicle monitoring system.
Albertson, Jacob C.; Arnold, Kenneth C.; Goldman, Steven D.; Paolini, Michael A.; Sessa, Anthony J., Warning a vehicle operator of unsafe operation behavior based on a 3D captured image stream.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.