PREDICTIVE RESOURCE PREPARATION AND HANDOFF FOR VEHICLE-TO-INFRASTRUCTURE SYSTEMS

• US 20180063261A1
• Filed: 09/01/2016
• Published: 03/01/2018
• Est. Priority Date: 09/01/2016
• Status: Active Grant

• Current Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Original Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Inventors: Moghe, Ashok Krishnaji, Apostolopoulos, John George, Gil Batres, Andrea A.
• Granted Patent: US 10,320,923 B2
• Time in Patent Office: Days
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/0145 for active traffic flow control
• CPC Class Codes: G08G 1/052 with provision for determining speed or overspeed speed measuring in general G01P
• CPC Class Codes: G08G 1/087 Override of traffic control, e.g. by signal transmitted by an emergency vehicle
• CPC Class Codes: 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, tramstransmission of navigation instructions to vehicles G08G1/0968
• CPC Class Codes: G08G 1/205 Indicating the location of the monitored vehicles as destination, e.g. accidents, stolen, rental
• CPC Class Codes: G08G 1/207 with respect to certain areas, e.g. forbidden or allowed areas with possible alerting when inside or outside boundaries
• CPC Class Codes: 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
• CPC Class Codes: H04L 67/52 specially adapted for the location of the user terminal
• CPC Class Codes: H04W 36/0083 Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists
• CPC Class Codes: H04W 36/322 by location data
• CPC Class Codes: H04W 36/324 by mobility data, e.g. speed data
• CPC Class Codes: H04W 4/44 for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]

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

1. A method, comprising:

collecting, by a prediction agent process, 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;

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.

2. The method as in claim 1, wherein predicting the path comprises a linear path prediction based on a current path of the vehicle.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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.

10. 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.

11. 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.

12. 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.

13. 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.

14. 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.

15. 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.

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;

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.

17. 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.

18. 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.

19. 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.

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;

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.