DRIVER RISK ASSESSMENT SYSTEM AND METHOD HAVING CALIBRATING AUTOMATIC EVENT SCORING

• US 20160314630A1
• Filed: 02/05/2016
• Published: 10/27/2016
• Est. Priority Date: 05/09/2006
• Status: Active Grant

• Current Assignee: Lytx, Inc.
• Original Assignee: Lytx, Inc.
• Inventors: Cook, Bryon, Gilles, Louis
• Granted Patent: US 9,978,191 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/0808 Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005
• CPC Class Codes: G07C 5/085 using electronic data carriers

Abstract

A Driver Risk Assessment System and Method Having Calibrating Automatic Event Scoring is disclosed. The system and method provide robust and reliable event scoring and reporting, while also optimizing data transmission bandwidth. The system includes onboard vehicular driving event detectors that record data related to detected driving events and selectively store or transfer data related to said detected driving events. If elected, the onboard vehicular system will score a detected driving event, compare the local score to historical values previously stored within the onboard system, and upload selective data or data types to a remote server or user if the system concludes that a serious driving event has occurred. Importantly, the onboard event scoring system, if enabled, will continuously evolve and improve in its reliability by being periodically re-calibrated with the ongoing reliability results of manual human review of automated predictive event reports. The system may further respond to independent user requests by transferring select data to said user at a variety of locations and formats.

Claims

1. A system for processing driving data, comprising:

an interface configured to receive a driving data compromising a sensor data of a vehicle; and

a processor configured to;

receive a scoring selection,

wherein the scoring selection comprises one of the following;

an absence of scoring, a manual scoring, or an automatic scoring; and

indicate to execute a process on the driving data based at least in part on the scoring selection,

wherein a result of the manual scoring is used to improve an accuracy of the automatic scoring.

2. The system of claim 1, wherein the processor is further to apply a set of data analytics comprising a decision tree to the driving data.

3. The system of claim 1, wherein the processor is further to determine a risk identification for the driving data by using a decision tree.

4. The system of claim 1, wherein the processor is further to determine a risk identification for the driving data comprising a prediction of a risky driving event.

5. The system of claim 4, wherein the risk identification comprises a profile, wherein the profile comprises a vehicle type and a risk prediction version.

6. The system of claim 1, wherein the process corresponding to the absence of scoring comprises a transfer of the driving data to a remote data storage repository.

7. The system of claim 1, wherein the process corresponding to the absence of scoring comprises collecting driving data for diagnostic purposes.

8. The system of claim 1, wherein the process corresponding to the absence of scoring comprises analyzing the driving data to study an operability of sensor triggers.

9. The system of claim 1, wherein the process corresponding to the manual scoring comprises a review of the driving data by a human reviewer.

10. The system of claim 1, wherein the process corresponding to the manual scoring comprises an assignment of an event score to the driving data by a human reviewer.

11. The system of claim 1, wherein the process corresponding to the manual scoring comprises reporting a result of the manual scoring to a user in the event the result meets an event criteria.

12. The system of claim 1, wherein a result of the manual scoring comprises one or more of the following:

a vehicle type, a version of a risk prediction decision tree, a risk identification, or a point value that assesses the riskiness of a driver behavior.

13. The system of claim 1, wherein a result of the automatic scoring is based at least in part on a risk confidence data.

14. The system of claim 13, wherein the result of the manual scoring updates the risk confidence data.

15. The system of claim 1, wherein a statistical reliability of a risk identification profile is assessed based on the result of the manual scoring.

16. The system of claim 1, wherein an ongoing reliability analysis evaluates how often the result of the manual scoring indicates a risky driving event occurred as a percentage of times a result of the automatic scoring indicated a corresponding risk identification.

17. The system of claim 1, wherein the process corresponding to the automatic scoring comprises determining whether a risky driving event has occurred.

18. The system of claim 1, wherein the process corresponding to the automatic scoring comprises determining whether a risky driving event has occurred by determining whether a risk identification assigned to the driving data indicates the risky driving event is reliably predicted.

19. A method for processing driving data, comprising:

receiving a driving data compromising a sensor data of a vehicle;

receiving, using a processor, a scoring selection, wherein the scoring selection comprises one of the following;

an absence of scoring, a manual scoring, or an automatic scoring; and

executing a process on the driving data based at least in part on the scoring selection, wherein a result of the manual scoring is used to improve an accuracy of the automatic scoring.

20. A computer program product for processing driving data, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for:

receiving a driving data compromising a sensor data of a vehicle;

receiving a scoring selection, wherein the scoring selection comprises one of the following;

an absence of scoring, a manual scoring, or an automatic scoring; and

executing a process on the driving data based at least in part on the scoring selection, wherein a result of the manual scoring is used to improve an accuracy of the automatic scoring.