IP Library › Granted Patent US 11,493,351
Granted Patent B2
US 11,493,351 · App. 17/009,228 · Granted Nov 8, 2022

Systems and methods of connected driving based on dynamic contextual factors

Inventors: Howard Hayes (Glencoe, IL); Sunil Chintakindi (Menlo Park, CA); Tim Gibson (Barrington, IL)
Assignee: Allstate Insurance Company
G01C21/3461B60W30/0953B60W40/04B60W40/06B60W40/08B60W40/105G01C21/3626G01C21/3691G01C21/3807G01C21/3833B60W40/107B60W2420/42B60W2510/18B60W2510/20B60W2540/229
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Quick Facts
Patent No.
US 11,493,351
App. No.
17/009,228
Filed
Sep 1, 2020
Granted
Nov 8, 2022
Kind
B2
Art Unit
3669
USPC
701/423
Abstract

Systems including one or more sensors, coupled to a vehicle, may detect sensor information and provide the sensor information to another computing device for processing. A system includes one or more sensors, coupled to a vehicle and configured to detect sensor information, and a computing device configured to communicate with one or more mobile sensors to receive the mobile sensor information, communicate with the one or more sensors to receive the sensor information, and analyze the sensor information and the mobile sensor information to identify one or more risk factors.

Claims (50)

1. A system comprising:

one or more sensors coupled to a vehicle and configured to detect sensor information; and

a computing device in signal communication with the one or more sensors, wherein the computing device comprises:

a processor; and

memory storing instructions that, when executed by the processor, cause the computing device to:

communicate with the one or more sensors to receive the sensor information;

communicate with a mobile device to receive mobile information;

generate one or more dynamic risk values associated with operation of the vehicle based on analyzing the sensor information and the mobile information to determine at least one of a tailgating risk index, a road frustration index based on a number of vehicles in front of the vehicle, or a lane keeping index;

generate a recommendation based on the one or more dynamic risk values;

transmit the recommendation to the mobile device; and

cause the recommendation to be displayed on an interface of the mobile device.

2. The system of claim 1 , wherein the memory further stores instructions that, when executed by the processor cause the computing device to:

receive additional information, wherein the additional information includes at least one of additional sensor information or additional mobile information; and

calculate one or more updated dynamic risk values by applying a machine learning model to the additional information.

3. The system of claim 1 , wherein generating the one or more dynamic risk values includes generating at least one risk assessment associated with operation of the vehicle based on the sensor information and the mobile information.

4. The system of claim 3 , wherein the at least one risk assessment includes at least one of a driver attention index or an internal distraction index.

5. The system of claim 1 , wherein generating the recommendation includes determining whether the one or more dynamic risk values exceeds a risk threshold.

6. The system of claim 1 , wherein the recommendation comprises instructions that cause one or more features of the mobile device to be enabled or disabled.

7. The system of claim 1 , wherein the recommendation comprises a risk map, the risk map including one or more locational risk assessments.

8. The system of claim 1 , wherein the recommendation comprises a calculated safe route.

9. The system of claim 1 , wherein generating the one or more dynamic risk values includes accessing a database of risk scores associated with one or more aspects of vehicle operation.

10. The system of claim 1 , wherein the mobile information includes at least one of: accident information, geographic information, environmental information, risk information, or vehicle information.

11. A method comprising:

collecting sensor information from one or more sensors coupled to a vehicle;

collecting mobile information from a mobile device associated with an occupant in the vehicle;

analyzing the sensor information and the mobile information;

generating one or more dynamic risk values associated with operation of the vehicle based on analyzing the sensor information and the mobile information to determine at least one of a tailgating risk index, a road frustration index based on a number of vehicles in front of the vehicle, or a lane keeping index; and

disabling one or more features of the mobile device based on the one or more dynamic risk values.

12. The method of claim 11 , further comprising generating a risk assessment associated with an operation parameter of the vehicle based on the sensor information and the mobile information.

13. The method of claim 12 , wherein generating the risk assessment includes generating a current risk assessment and a future risk assessment.

14. The method of claim 11 , further comprising:

receiving additional information, wherein the additional information includes at least one of additional sensor information or additional mobile information; and

generating one or more updated dynamic risk values by applying a machine learning model to the additional information.

15. The method of claim 11 , further comprising:

transmitting a recommendation to the mobile device, wherein the recommendation is based on the one or more dynamic risk values and is configured to be displayed on an interface of the mobile device.

16. An apparatus comprising:

a processor;

a wireless communication interface; and

memory storing instructions that, when executed by the processor, cause the apparatus to:

receive, from one or more sensors coupled to a vehicle, sensor information associated with operation of the vehicle;

receive, from a mobile device, mobile information associated with operation of the vehicle;

generate one or more dynamic risk values associated with operation of the vehicle based on analyzing the sensor information and the mobile information to determine at least one of a tailgating risk index, a road frustration index based on a number of vehicles in front of the vehicle, or a lane keeping index;

transmit a recommendation to the mobile device, wherein the recommendation is generated based on the one or more dynamic risk values; and

cause the recommendation to be displayed on an interface of the mobile device.

17. The apparatus of claim 16 , wherein the memory stores further instructions that, when executed by the processor cause the apparatus to:

receive additional information, wherein the additional information includes at least one of additional sensor information or additional mobile information; and

generate one or more updated dynamic risk values by applying a machine learning model to the additional information.

18. The apparatus of claim 16 , wherein the sensor information includes at least one of a vehicles speed, a rate of acceleration, braking, steering, impacts to the vehicle, or usage of vehicle controls.

19. The apparatus of claim 16 , wherein the sensor information includes vehicle operational data collected by one or more internal vehicle sensors, one or more computers, or one or more cameras.

20. The apparatus of claim 16 , wherein the mobile information includes at least one of: accident information, geographic information, environmental information, risk information, or vehicle information.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 26, 2022
From: HAYES, HOWARD; CHINTAKINDI, SUNIL; GIBSON, TIM
To: ALLSTATE INSURANCE COMPANY
Reel/Frame 059731/0379 →
Continuity (3)
Provisional Application 63043561 · Jun 24, 2020
Provisional Application 62895390 · Sep 3, 2019
Related Publication 20210063179A1 · Mar 4, 2021