Systems and methods for dynamically generating optimal routes for vehicle delivery management
AssigneeState Farm Mutual Automobile Insurance Company
InventorsAaron Williams, Ryan Michael Gross, Joseph Robert Brannan
Life of this patent
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Abstract
A vehicle routing system includes a vehicle routing and analytics (VRA) computing device, one or more databases, and one or more vehicles communicatively coupled to the VRA computing device. The VRA computing device is configured to generate an optimal route for a vehicle to travel that maximizes potential revenue for operation of the vehicle, the optimal route including a schedule of a plurality of tasks, and generate analytics associated with operation of the vehicle. The VRA computing device is further configured to provide a management hub software application accessible by vehicle users associated with vehicles, tasks sources, and other users.
Claims20
A vehicle routing and analytics (VRA) computing device comprising at least one processor in communication with a memory, the VRA computing device communicatively coupled to a fleet of vehicles, wherein the at least one processor is programmed to:
- retrieve a vehicle definition for each vehicle of the fleet of vehicles, each vehicle definition including availability parameters and delivery preferences associated with the respective vehicle;
- control each vehicle of the fleet of vehicles to travel along a respective optimal route, the optimal route including a scheduled list of tasks for the vehicle to perform;
- receive respective sensor data from each vehicle of the fleet of vehicles as the vehicle is travelling along the optimal route;
- process the received sensor data to generate vehicle analytics for each vehicle of the fleet of vehicles, wherein the vehicle analytics include a real-time risk profile;
- detect, from the generated vehicle analytics using artificial intelligence and deep learning functionality, a respective real-time status of each vehicle of the fleet of vehicles;
- and
- update the vehicle definition for at least one vehicle of the fleet of vehicles based at least in part upon the detected real-time status of the at least one vehicle.
A computer-implemented method for controlling each vehicle of a fleet of vehicles, the method implemented using a vehicle routing analytics (VRA) computing device communicatively coupled to the fleet of vehicles, wherein the VRA computing device includes at least one processor in communication with a memory, wherein the method comprises:
- retrieving a vehicle definition for each vehicle of the fleet of vehicles, each vehicle definition including availability parameters and delivery preferences associated with the respective vehicle;
- controlling each vehicle of the fleet of vehicles to travel along a respective optimal route, the optimal route including a scheduled list of tasks for the vehicle to perform;
- receiving respective sensor data from each vehicle of the fleet of vehicles as the vehicle is travelling along the optimal route;
- processing the received sensor data to generate vehicle analytics for each vehicle of the fleet of vehicles, wherein the vehicle analytics include a real-time risk profile;
- detecting, from the generated vehicle analytics using artificial intelligence and deep learning functionality, a respective real-time status of each vehicle of the fleet of vehicles;
- and
- updating the vehicle definition for at least one vehicle of the fleet of vehicles based at least in part upon the detected real-time status of the at least one vehicle.
At least one non-transitory computer-readable storage medium having stored thereon computer-executable instructions that, when executed by at least one processor of a vehicle routing and analytics (VRA) computing device communicatively coupled to a fleet of vehicles, the computer-executable instructions cause the at least one processor to:
- retrieve a vehicle definition for each vehicle of the fleet of vehicles, each vehicle definition including availability parameters and delivery preferences associated with the respective vehicle;
- control each vehicle of the fleet of vehicles to travel along a respective optimal route, the optimal route including a scheduled list of tasks for the vehicle to perform;
- receive respective sensor data from each vehicle of the fleet of vehicles as the vehicle is travelling along the optimal route;
- process the received sensor data to generate vehicle analytics for each vehicle of the fleet of vehicles, wherein the vehicle analytics include a real-time risk profile;
- detect, from the generated vehicle analytics using artificial intelligence and deep learning functionality, a respective real-time status of each vehicle of the fleet of vehicles;
- and
- update the vehicle definition for at least one vehicle of the fleet of vehicles based at least in part upon the detected real-time status of the at least one vehicle.
Dependencies are inferred from the claim text.