# US 10,320,923 B2 · Predictive resource preparation and handoff for vehicle-to-infrastructure systems > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,320,923 B2 - Document ID: US10320923B2 - Status: Granted - Kind: B2 - Application number: 15/254,240 - Filed: 2016-09-01 - Published: 2018-03-01 - Granted: 2019-06-11 - Earliest claimed: 2016-09-01 - Assignees: Cisco Technology, Inc. - Applicants: Cisco Technology, Inc. - Inventors: Ashok Krishnaji Moghe; John George Apostolopoulos; Andrea A. Gil Batres - Web page: https://patentgrep.com/patent/US10320923B2 ## CPC - H04L 67/52: specially adapted for the location of the user terminal - G07C 5/008: {communicating information to a remotely located station (transmission systems for measured values G08C)} - G08G 1/0112: {from the vehicle, e.g. floating car data [FCD]} - G08G 1/0133: {for classifying traffic situation} - G08G 1/0145: {for active traffic flow control} - G08G 1/052: with provision for determining speed or overspeed {(speed measuring in general G01P)} - G08G 1/087: Override of traffic control, e.g. by signal transmitted by an emergency vehicle - G08G 1/123: indicating the position of vehicles, e.g. scheduled vehicles; {Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams}(transmission of navigation instructions to vehicles G08G1/0968) - G08G 1/205: {Indicating the location of the monitored vehicles as destination, e.g. accidents, stolen, rental} - G08G 1/207: {with respect to certain areas, e.g. forbidden or allowed areas with possible alerting when inside or outside boundaries} - H04L 67/12: specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks - H04W 4/44: for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H] - H04W 36/0083: {Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists} - H04W 36/322: {by location data} - H04W 36/324: {by mobility data, e.g. speed data} - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE - G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93}) - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## Abstract In one embodiment, a prediction agent process collects travel information of a vehicle, and determines a profile of the vehicle, the profile indicative of one or more real-time resource requirements of the vehicle. The prediction agent process also predicts a path of the vehicle based on the travel information, and determines a next resource node along the predicted path having one or more real-time resources corresponding to the one or more real-time resource requirements of the vehicle. After further predicting a time of arrival of the vehicle being within range of the next resource node based on the travel information, the prediction agent process informs the next resource node of the profile of the vehicle and the predicted time of arrival, the informing causing the next resource node to operate the one or more real-time resources for the vehicle for the predicted time of arrival. ## Claims ### Claim 1 A method, comprising: collecting, by a prediction agent process in a network, travel information of a vehicle; determining, by the prediction agent process, a profile of the vehicle, the profile indicative of one or more real-time resource requirements of the vehicle; predicting, by the prediction agent process, a path of the vehicle based on the travel information; determining, by the prediction agent process, a next resource node along the predicted path having one or more real-time resources corresponding to the one or more real-time resource requirements of the vehicle, wherein the next resource node is a stationary node in the network, along the predicted path that participates with one or more vehicle-to-infrastructure (V2X) processes executing on the vehicle; predicting, by the prediction agent process, a time of arrival of the vehicle being within range of the next resource node based on the travel information; and informing, by the prediction agent process, the next resource node of the profile of the vehicle and the predicted time of arrival, the informing causing the next resource node to operate the one or more real-time resources for the vehicle for the predicted time of arrival. ### Claim 2 (depends on 1) The method as in claim 1, wherein predicting the path comprises a linear path prediction based on a current path of the vehicle. ### Claim 3 (depends on 1) The method as in claim 1, wherein predicting the path comprises a heuristic path prediction based on statistical modelling according to the travel information and profile of the vehicle. ### Claim 4 (depends on 1) The method as in claim 1, wherein predicting the path comprises a cooperative path prediction based on the travel information corresponding to a pre-calculated path. ### Claim 5 (depends on 4) The method as in claim 4, wherein the prediction agent process operates within one or more devices of a network infrastructure other than the vehicle, and wherein the cooperative path prediction is based on a negotiation between the vehicle and the prediction agent process. ### Claim 6 (depends on 1) The method as in claim 1, wherein predicting the path comprises: determining a plurality of predicted paths, wherein the prediction agent process determines a corresponding next resource node along each of the plurality of predicted paths, predicts a corresponding time of arrival of the vehicle at each of the corresponding next resource nodes, and informs each of the corresponding the next resource nodes of the profile of the vehicle and the corresponding predicted time of arrival. ### Claim 7 (depends on 1) The method as in claim 1, wherein predicting the time of arrival is based on the travel information and sensor data of time-affecting factors along the predicted path. ### Claim 8 (depends on 1) The method as in claim 1, wherein the next resource node is caused to operate a traffic signal as the one or more real-time resources for the vehicle for the predicted time of arrival. ### Claim 9 (depends on 8) The method as in claim 8, wherein the next resource node is caused to operate the traffic signal according to a vehicular priority of the vehicle against one or more other vehicles predicted to be at the traffic signal at the predicted time of arrival. ### Claim 10 (depends on 1) The method as in claim 1, wherein the next resource node is caused to operate a dynamic wireless power transfer as the one or more real-time resources for the vehicle for the predicted time of arrival. ### Claim 11 (depends on 10) The method as in claim 10, wherein the next resource node is caused to operate the dynamic wireless power transfer by pre-charging an in-road inductive charging system substantially near the predicted time of arrival for delivery of an inductive charge to the vehicle at an actual time of arrival. ### Claim 12 (depends on 1) The method as in claim 1, wherein the prediction agent process operates within one or more devices of a network infrastructure other than the vehicle. ### Claim 13 (depends on 1) The method as in claim 1, wherein the prediction agent process operates within a current resource node, and wherein informing comprises a state handoff between the current resource node and the next resource node. ### Claim 14 (depends on 13) The method as in claim 13, wherein the state handoff comprises point-to-point (P2P) communication between the current resource node and the next resource node. ### Claim 15 (depends on 1) The method as in claim 1, further comprising: informing the vehicle of a pre-calculated path toward one or more particular resource nodes having one or more real-time resources corresponding to the one or more real-time resource requirements of the vehicle. ### Claim 16 An apparatus, comprising: one or more network interfaces to communicate within a vehicle-to-infrastructure (V2X) network; a processor coupled to the network interfaces and adapted to execute one or more processes; and a memory configured to store a process executable by the processor, the process when executed operable to: collect travel information of a vehicle; determine a profile of the vehicle, the profile indicative of one or more real-time resource requirements of the vehicle; predict a path of the vehicle based on the travel information; determine a next resource node along the predicted path having one or more real-time resources corresponding to the one or more real-time resource requirements of the vehicle, wherein the next resource node is a stationary node in the V2X network, along the predicted path that participates with one or more V2X processes executing on the vehicle; predict a time of arrival of the vehicle being within range of the next resource node based on the travel information; and inform the next resource node of the profile of the vehicle and the predicted time of arrival to cause the next resource node to operate the one or more real-time resources for the vehicle for the predicted time of arrival. ### Claim 17 (depends on 16) The apparatus as in claim 16, wherein the process when executed to predict the path is further operable to use a prediction selected from a group consisting of: a linear path prediction based on a current path of the vehicle; a heuristic path prediction based on statistical modelling according to the travel information and profile of the vehicle; and a cooperative path prediction based on the travel information corresponding to a pre-calculated path. ### Claim 18 (depends on 16) The apparatus as in claim 16, wherein the next resource node is caused to operate a traffic signal as the one or more real-time resources for the vehicle for the predicted time of arrival. ### Claim 19 (depends on 16) The apparatus as in claim 16, wherein the next resource node is caused to operate a dynamic wireless power transfer as the one or more real-time resources for the vehicle for the predicted time of arrival. ### Claim 20 A tangible, non-transitory, computer-readable media having software encoded thereon, the software when executed by a processor operable to: collect travel information of a vehicle; determine a profile of the vehicle, the profile indicative of one or more real-time resource requirements of the vehicle; predict a path of the vehicle based on the travel information; determine a next resource node along the predicted path having one or more real-time resources corresponding to the one or more real-time resource requirements of the vehicle, wherein the next resource node is a stationary node in a vehicle-to-infrastructure (V2X) network, along the predicted path that participates with one or more V2X processes executing on the vehicle; predict a time of arrival of the vehicle being within range of the next resource node based on the travel information; and inform the next resource node of the profile of the vehicle and the predicted time of arrival to cause the next resource node to operate the one or more real-time resources for the vehicle for the predicted time of arrival.