Patent Issued for Systems and methods for dynamically generating optimal routes for vehicle delivery management (USPTO 11493345): State Farm Mutual Automobile Insurance Company
2022 NOV 29 (NewsRx) -- By a
The patent’s assignee for patent number 11493345 is
News editors obtained the following quote from the background information supplied by the inventors: “It is well known to use vehicles for delivery of cargo, including persons and items. Newer methods of transporting such cargo are being implemented, such as ride-sharing and businesses contracting with third parties for package delivery. However, current systems require users to pick and choose discrete jobs. For example, a driver for a ride-share service seeks individual persons (or groups thereof) to provide rides to. Likewise, third-party delivery contractors accept individual delivery jobs. Current systems do not provide users with any strategies for maximizing revenue. Accordingly, it is left up to users to attempt to maximize their revenue, time, and/or other resources as they choose individual jobs or tasks. Moreover, many known systems may limit or place restrictions on concurrent jobs, such that users accept consecutive jobs and may be deprived of the advantages (in time and revenue) of concurrent jobs.”
As a supplement to the background information on this patent, NewsRx correspondents also obtained the inventors’ summary information for this patent: “The present embodiments may relate to, inter alia, systems and methods for optimizing delivery routing for a plurality of vehicles. Vehicle users (e.g., owners, lessors, fleet managers, etc.) may register with a vehicle routing system to receive vehicle routing services therefrom. The vehicle users may submit “definitions” of their vehicle that describe how they want their vehicle to be used (e.g., the times and locations that the vehicle is available to make deliveries). The vehicle routing system may receive a plurality of tasks (e.g., delivering cargo including person(s) and/or object(s)) to be completed, and may analyze the vehicle definitions and the plurality of tasks to generate an optimal route for each vehicle that maximizes the vehicle’s profit generation. In the exemplary embodiment, the vehicle routing system may include artificial intelligence and/or deep learning functionality to generate the optimal routes.
“In addition, the vehicle routing system may collect and process sensor data from the plurality of vehicles, as the vehicle operate according the optimal routes (e.g., performing delivery tasks). The sensor data may characterize use/performance of the vehicle, an ambient environment around the vehicle (e.g., weather, traffic), local infrastructure, and more. The vehicle routing system may process the sensor data (e.g., using the artificial intelligence and/or deep learning functionality) to generate vehicle analytics for each vehicle, identify trends, update optimal routes, and/or make recommendations.
“In the exemplary embodiment, the vehicle routing system maintains a management hub software application (“app”) that enables users to view vehicle analytics and recommendations made by the vehicle routing system. The management hub may additionally enable users to update their user preferences or vehicle definitions of registered vehicles. Additionally or alternatively, the management hub app provides users access to a plurality of other services provided by the vehicle routing system, including maintenance/repair services, financial services, marketing services, and the like.
“In one aspect, a vehicle routing and analytics (VRA) computing device for generating an optimal route for a vehicle that maximizes potential revenue for operation of the vehicle may be provided. The VRA computing device may include at least one processor in communication with a memory, wherein the at least one processor may be programmed to retrieve a vehicle definition for the vehicle, the vehicle definition including availability parameters and delivery preferences associated with the vehicle. The at least one processor may also be programmed to retrieve, based in part on the vehicle definition, a plurality of task definitions defining a respective plurality of tasks, each task including a respective cargo to be delivered, pick-up time, delivery time, pick-up location, delivery location, and task value. The at least one processor may be further programmed to generate, by executing at least one of artificial intelligence and deep learning functionality using the vehicle definition and the plurality of task definitions, an optimal route for the vehicle that includes a scheduled list of a subset of the plurality of tasks for the vehicle to perform, wherein the optimal route maximizes the potential revenue for operation of the vehicle within a period of time associated with the optimal route, and to transmit the optimal route to the vehicle for operation of the vehicle according to the optimal route. The VRA computing device may include additional, less, and/or alternative functionality, including that described herein.
“In another aspect, a computer-implemented method for generating an optimal route for a vehicle to travel that maximizes potential revenue for operation of the vehicle may be provided. The method may be implemented by a vehicle routing and analytics (VRA) computing device including at least one processor. The method may include retrieving a vehicle definition for the vehicle, the vehicle definition including availability parameters and delivery preferences associated with the vehicle. The method may also include retrieving, based in part on the vehicle definition, a plurality of task definitions defining a respective plurality of tasks, each task including a respective cargo to be delivered, pick-up time, delivery time, pick-up location, delivery location, and task value. The method may further include generating, by executing at least one of artificial intelligence and deep learning functionality using the vehicle definition and the plurality of task definitions, an optimal route for the vehicle that includes a scheduled list of a subset of the plurality of tasks for the vehicle to perform, wherein the optimal route maximizes the potential revenue for operation of the vehicle within a period of time associated with the optimal route, and transmitting the optimal route to the vehicle for operation of the vehicle according to the optimal route. The method may include additional, fewer, and/or alternative steps, including those described herein.”
The claims supplied by the inventors are:
“1. A computer system for directing a floating delivery autonomous vehicle, the computer system comprising at least one of: one or more processors, one or more sensors, one or more transceivers, and one or more servers, the computer system configured to: receive autonomous vehicle (AV) condition data from at least one source including a plurality of AVs, the AV condition data generated by AV-mounted sensors and that indicates weather, road, traffic, congestion, or accident conditions; retrieve, from a memory unit, a plurality of service requests generated by a corresponding plurality of customer computing devices, each service request including a pick-up location, a drop-off location, and information identifying (i) one or more passengers, or (ii) a type and a weight of one or more packages; calculate a lowest cost route for operation of the floating delivery AV between respective pick-up and drop-off locations of only a subset of the plurality of service requests, by selecting the subset of the plurality of service requests based upon at least one of: (i) the AV condition data, and (ii) passenger or package information identified in the subset of the plurality of service requests; generate an overall route that the floating delivery AV will travel from an origination location to a final location, the overall route including instructions for the floating delivery AV to travel to each of the respective pick-up and drop-off locations of the subset of the plurality of service requests as waypoints; transmit the overall route to the floating delivery AV to direct the floating delivery AV to travel the overall route to each pick-up and drop-off location and to pick-up and drop off the (i) one or more passengers or (ii) one or more packages at each respective pick-up and drop-off location as indicated in the subset of the plurality of service requests; receive an additional service request for pick-up and drop-off of an additional (i) one or more passengers or (ii) one or more packages, the additional service request including corresponding pick-up and drop-off locations; continuously receive updated AV condition data; while directing the floating delivery AV along the overall route, dynamically update the overall route by calculating an updated lowest cost route that includes the pick-up and drop-off locations in the additional service request as additional waypoints along the overall route, based upon at least one of (i) the updated AV condition data or (ii) other data received since the overall route was initially generated; transmit updated instructions to the floating delivery AV to direct the floating delivery AV along the dynamically updated overall route; and update a remote status hub with location information associated with the floating delivery AV, the status hub configured to display at least one of a current status and current location of each (i) one or more passengers or (ii) one or more packages transported by the floating delivery AV.
“2. The computer system of claim 1, wherein the AV condition data includes telematics data including speed, GPS location, route, direction, cornering, braking, and acceleration data of the respective AV vehicles and image data.
“3. The computer system of claim 1, wherein the AV condition data is generated at at least one of: smart infrastructure, mobile devices, intelligent homes, drones, planes, or satellites.
“4. The computer system of claim 1, wherein the lowest cost route is calculated based upon shortest time or shortest distance traveled to complete the respective route.
“5. The computer system of claim 1, wherein the lowest cost route is calculated based upon optimizing downtime of the floating delivery AV for at least one of maintenance, refueling, or recharging.
“6. The computer system of claim 1, wherein the lowest cost route is calculated based upon optimizing safety of traveling the respective route.
“7. The computer system of claim 1, wherein the lowest cost route is calculated based upon weather along the respective route, including prioritizing picking up and dropping off the at least one of (i) one or more passengers and (ii) one or more packages in clear weather.
“8. The computer system of claim 1, wherein the lowest cost route is calculated based upon a route including the least traffic, congestion, or road construction along the respective route.
“9. A computer-implemented method for directing a floating delivery autonomous vehicle, the method being implemented via at least one of: one or more processors, one or more sensors, one or more transceivers, and one or more servers, the method comprising: receiving autonomous vehicle (AV) condition data from at least one source including a plurality of AVs, the AV condition data generated by AV-mounted sensors and that indicates weather, road, traffic, congestion, or accident conditions; retrieving, from a memory unit, a plurality of service requests generated by a corresponding plurality of customer computing devices, each service request including a pick-up location, a drop-off location, and information identifying (i) one or more passengers, or (ii) a type and a weight of one or more packages; calculating a lowest cost route for operation of the floating delivery AV between respective pick-up and drop-off locations of only a subset of the plurality of service requests, by selecting the subset of the plurality of service requests based upon at least one of: (i) the AV condition data, and (ii) passenger or package information identified in the subset of the plurality of service requests; generating an overall route that the floating delivery AV will travel from an origination location to a final location, the overall route including instructions for the floating delivery AV to travel to each of the respective pick-up and drop-off locations of the subset of the plurality of service requests as waypoints; transmitting the overall route to the floating delivery AV to direct the floating delivery AV to travel the overall route to each pick-up and drop-off location and to pick-up and drop off the (i) one or more passengers or (ii) one or more packages at each respective pick-up and drop-off location as indicated in the subset of the plurality of service requests; receiving an additional service request for pick-up and drop-off of an additional (i) one or more passengers or (ii) one or more packages, the additional service request including corresponding pick-up and drop-off locations; continuously receiving updated AV condition data; while directing the floating delivery AV along the overall route, dynamically updating the overall route by calculating an updated lowest cost route that includes the pick-up and drop-off locations in the additional service request as additional waypoints along the overall route, based upon at least one of (i) the updated AV condition data or (ii) other data received since the overall route was initially generated; transmitting updated instructions to the floating delivery AV to direct the floating delivery AV along the dynamically updated overall route; and updating a remote status hub with location information associated with the floating delivery AV, the status hub configured to display at least one of a current status and current location of each (i) one or more passengers or (ii) one or more packages transported by the floating delivery AV.
“10. The computer-implemented method system of claim 9, wherein receiving AV condition data comprises receiving the AV condition data including telematics data including speed, GPS location, route, direction, cornering, braking, and acceleration data of the respective AV vehicles and image data.
“11. The computer-implemented method system of claim 9, wherein receiving AV condition data comprises receiving the AV condition data generated at at least one of: smart infrastructure, mobile devices, intelligent homes, drones, planes, or satellites.
“12. The computer-implemented method system of claim 9, wherein calculating the lowest cost route comprises calculating the lowest cost route based upon shortest time or shortest distance traveled to complete the respective route.
“13. The computer-implemented method system of claim 9, wherein calculating the lowest cost route comprises calculating the lowest cost route based upon optimizing downtime of the floating delivery AV for at least one of maintenance, refueling, or recharging.
“14. The computer-implemented method system of claim 9, wherein calculating the lowest cost route comprises calculating the lowest cost route based upon optimizing safety of traveling the respective route.
“15. The computer-implemented method system of claim 9, wherein calculating the lowest cost route comprises calculating the lowest cost route based upon weather along the respective route, including prioritizing picking up and dropping off the at least one of (i) one or more passengers and (ii) one or more packages in clear weather.
“16. The computer-implemented method system of claim 9, wherein calculating the lowest cost route comprises calculating the lowest cost route based upon a route including the least traffic, congestion, or road construction along the respective route.”
For additional information on this patent, see: Brannan,
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