# US 2025/0146830 A1 · Systems and methods for dynamically generating optimal routes for vehicle operation management

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- Number: US 2025/0146830 A1
- Document ID: US20250146830A1
- Status: Published application
- Kind: A1
- Application number: 18/954,130
- Filed: 2024-11-20
- Published: 2025-05-08
- Earliest claimed: 2019-02-15
- Assignees: State Farm Mutual Automobile Insurance Company
- Applicants: State Farm Mutual Automobile Insurance Company
- Inventors: Aaron Williams; Ryan Michael Gross; Joseph Robert Brannan
- Web page: https://patentgrep.com/patent/US20250146830A1

## CPC

- G01C 21/3492: {employing speed data or traffic data, e.g. real-time or historical (traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968)}
- B64C 39/024: {of the remote controlled vehicle type, i.e. RPV}
- G01C 21/20: Instruments for performing navigational calculations (G01C21/24, G01C21/26 take precedence)
- G01C 21/3438: {Rendezvous; Ride sharing}
- G01C 21/3453: {Special cost functions, i.e. other than distance or default speed limit of road segments}
- G05D 1/00: Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots (drive control systems specially adapted for autonomous road vehicles B60W60/00)
- G05D 1/0088: {characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours (using knowledge based models G06N5/00)}
- G05D 1/227: Handing over between remote control and on-board control; Handing over between remote control arrangements
- G06Q 10/047: Optimisation of routes or paths, e.g. travelling salesman problem
- G06Q 10/06316: {Sequencing of tasks or work}
- G06Q 10/0635: Risk analysis of enterprise or organisation activities
- G06Q 10/0639: Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q 10/08355: {Routing methods}
- G06Q 50/40: Business processes related to the transportation industry (shipping G06Q10/083)
- G08G 1/0112: {from the vehicle, e.g. floating car data [FCD]}
- G08G 1/0129: {for creating historical data or processing based on historical data}
- G08G 1/0133: {for classifying traffic situation}
- G08G 1/0145: {for active traffic flow control}
- G08G 1/096716: {where the received information does not generate an automatic action on the vehicle control}
- G08G 1/096725: {where the received information generates an automatic action on the vehicle control}
- G08G 1/096816: {where the complete route is transmitted to the vehicle at once}
- G08G 1/096844: {where the complete route is dynamically recomputed based on new data}
- G08G 1/202: {Dispatching vehicles on the basis of a location, e.g. taxi dispatching}
- H04W 4/024: Guidance services
- H04W 4/44: for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
- B64U 2101/60: for transporting passengers; for transporting goods other than weapons
- B64U 2101/64: for parcel delivery or retrieval
- B64U 2201/104: using satellite radio beacon positioning systems, e.g. GPS
- G05D 2101/10: using artificial intelligence [AI] techniques
- G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S)
- B64C: AEROPLANES; HELICOPTERS
- G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- 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)
- B64U: UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR

## 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.

## Claims

### Claim 1

A vehicle routing and analytics (VRA) computing device for generating a respective optimal route for each vehicle of a plurality of vehicles, the VRA computing device communicatively coupled to the plurality of vehicles, wherein the VRA computing device comprises at least one processor in communication with at least one memory, wherein the at least one processor is programmed to:
retrieve a vehicle definition for each vehicle of the plurality of vehicles, each vehicle definition including availability parameters and delivery preferences associated with the respective vehicle;
retrieve 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 priority criteria;
execute at least one of artificial intelligence and deep learning functionality using the vehicle definition and the plurality of task definitions as inputs;
receive, as output from the executed at least one of artificial intelligence and deep learning functionality, a respective subset of tasks for each vehicle of the plurality of vehicles;
generate a respective optimal route for each vehicle of the plurality of vehicles, each optimal route including a scheduled list of the subset of tasks for the vehicle to perform, wherein each optimal route prioritizes the subset of tasks based at least in part upon the priority criteria of the subset of tasks; and
control at least one vehicle of the plurality of vehicles to travel along the respective optimal route.

### Claim 2 (depends on 1)

The VRA computing device of claim 1, wherein each optimal route further includes navigation instructions instructing a respective driver of the corresponding vehicle to operate the corresponding vehicle according to the navigation instructions in the absence of a control signal for controlling the corresponding vehicle.

### Claim 3 (depends on 1)

The VRA computing device of claim 1, wherein each task of the plurality of tasks is assigned to one vehicle of the plurality of vehicles.

### Claim 4 (depends on 1)

The VRA computing device of claim 1, wherein the priority criteria include one or more of: an identification of the cargo as at least one of a person or an object, a prioritized vehicle characteristic, an availability limitation, or a vehicle characteristic restriction.

### Claim 5 (depends on 1)

The VRA computing device of claim 1, wherein the at least one processor is further programmed to generate each optimal route by scheduling tasks with more restrictive priority criteria before scheduling tasks with less restrictive priority criteria.

### Claim 6 (depends on 1)

The VRA computing device of claim 1, wherein one optimal route includes instructions regarding introduction of the cargo into the respective vehicle based at least in part upon the priority criteria of the corresponding task, the instructions to be provided to a human operator associated with the cargo.

### Claim 7 (depends on 1)

The VRA computing device of claim 1, wherein each vehicle of the plurality of vehicles has a plurality of sensors disposed thereon and configured to collect sensor data during operation of the respective vehicle, the respective plurality of sensors disposed on each vehicle including at least one sensor configured to identify check-in and check-out of cargo to and from the corresponding vehicle, and wherein the at least one processor is further programmed to:
receive, from the plurality of vehicles, sensor data during operation of the plurality of vehicles according to the generated optimal routes; and
monitor the received sensor data for adherence to the generated optimal routes.

### Claim 8

A computer-implemented method for generating a respective optimal route for each vehicle of a plurality of vehicles, the method implemented using a vehicle routing analytics (VRA) computing device communicatively coupled to the plurality of vehicles, wherein the VRA computing device includes at least one processor in communication with at least one memory, wherein the method comprises:
retrieving a vehicle definition for each vehicle of the plurality of vehicles, each vehicle definition including availability parameters and delivery preferences associated with the respective vehicle;
retrieving 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 priority criteria;
executing at least one of artificial intelligence and deep learning functionality using the vehicle definition and the plurality of task definitions as inputs;
receiving, as output from the executed at least one of artificial intelligence and deep learning functionality, a respective subset of tasks for each vehicle of the plurality of vehicles;
generating a respective optimal route for each vehicle of the plurality of vehicles, each optimal route including a scheduled list of the subset of tasks for the vehicle to perform, wherein each optimal route prioritizes the subset of tasks based at least in part upon the priority criteria of the subset of tasks; and
controlling at least one vehicle of the fleet of vehicles to travel along the optimal route.

### Claim 9 (depends on 8)

The computer-implemented method of claim 8, wherein generating each optimal route comprises generating each optimal route to include navigation instructions instructing a respective driver of the corresponding vehicle to operate the corresponding vehicle according to the navigation instructions in the absence of a control signal for controlling the corresponding vehicle.

### Claim 10 (depends on 8)

The computer-implemented method of claim 8, wherein generating each optimal route comprises scheduling tasks with more restrictive priority criteria before scheduling tasks with less restrictive priority criteria.

### Claim 11 (depends on 8)

The computer-implemented method of claim 8, wherein generating each optimal route comprises generating one optimal route to include instructions regarding introduction of the cargo into the respective vehicle based at least in part upon the priority criteria of the corresponding task, the instructions to be provided to a human operator associated with the cargo.

### Claim 12 (depends on 11)

The computer-implemented method of claim 11, further comprising causing the instructions to be displayed to the human operator associated with the cargo at the initiation of the corresponding task.

### Claim 13 (depends on 8)

The computer-implemented method of claim 8, wherein each vehicle of the plurality of vehicles has a plurality of sensors disposed thereon and configured to collect sensor data during operation of the respective vehicle, the respective plurality of sensors disposed on each vehicle including at least one sensor configured to identify check-in and check-out of cargo to and from the corresponding vehicle, the method further comprising:
receiving, from the plurality of vehicles, sensor data during operation of the plurality of vehicles according to the generated optimal routes; and
monitoring the received sensor data for adherence to the generated optimal routes.

### Claim 14

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 plurality of vehicles, cause the at least one processor to:
retrieve a vehicle definition for each vehicle of the plurality of vehicles, each vehicle definition including availability parameters and delivery preferences associated with the respective vehicle;
retrieve 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 priority criteria;
execute at least one of artificial intelligence and deep learning functionality using the vehicle definition and the plurality of task definitions as inputs;
receive, as output from the executed at least one of artificial intelligence and deep learning functionality, a respective subset of tasks for each vehicle of the plurality of vehicles;
generate a respective optimal route for each vehicle of the plurality of vehicles, each optimal route including a scheduled list of the subset of tasks for the vehicle to perform, wherein each optimal route prioritizes the subset of tasks based at least in part upon the priority criteria of the subset of tasks; and
control at least one vehicle of the plurality of vehicles to travel along the respective optimal route.

### Claim 15 (depends on 14)

The at least one non-transitory computer-readable storage medium of claim 14, wherein each optimal route further includes navigation instructions instructing a respective driver of the corresponding vehicle to operate the corresponding vehicle according to the navigation instructions in the absence of a control signal for controlling the corresponding vehicle.

### Claim 16 (depends on 14)

The at least one non-transitory computer-readable storage medium of claim 14, wherein each task of the plurality of tasks is assigned to one vehicle of the plurality of vehicles.

### Claim 17 (depends on 14)

The at least one non-transitory computer-readable storage medium of claim 14, wherein the priority criteria include one or more of: an identification of the cargo as at least one of a person or an object, a prioritized vehicle characteristic, an availability limitation, or a vehicle characteristic restriction

### Claim 18 (depends on 14)

The at least one non-transitory computer-readable storage medium of claim 14, wherein the computer-executable instructions further cause the at least one processor to generate each optimal route by scheduling tasks with more restrictive priority criteria before scheduling tasks with less restrictive priority criteria.

### Claim 19 (depends on 14)

The at least one non-transitory computer-readable storage medium of claim 14, wherein one optimal route includes instructions regarding introduction of the cargo into the respective vehicle based at least in part upon the priority criteria of the corresponding task, the instructions to be provided to a human operator associated with the cargo.

### Claim 20 (depends on 14)

The at least one non-transitory computer-readable storage medium of claim 14, wherein each vehicle of the plurality of vehicles has a plurality of sensors disposed thereon and configured to collect sensor data during operation of the respective vehicle, the respective plurality of sensors disposed on each vehicle including at least one sensor configured to identify check-in and check-out of cargo to and from the corresponding vehicle, and wherein the computer-executable instructions further cause the at least one processor to:
receive, from the plurality of vehicles, sensor data during operation of the plurality of vehicles according to the generated optimal routes; and
monitor the received sensor data for adherence to the generated optimal routes.