METHODS AND SYSTEMS FOR COLLECTING DRIVING INFORMATION AND CLASSIFYING DRIVERS AND SELF-DRIVING SYSTEMS

• US 20170255966A1
• Filed: 05/12/2017
• Published: 09/07/2017
• Est. Priority Date: 03/28/2014
• Status: Active Grant

• Current Assignee: Joseph Khoury
• Original Assignee: Joseph Khoury
• Inventors: Khoury, Joseph
• Granted Patent: US 9,898,759 B2
• Time in Patent Office: Days
• CPC Class Codes: B60W 40/00 Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: G01C 21/3617 using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement
• CPC Class Codes: G01C 21/3697 Output of additional, non-guidance related information, e.g. low fuel level G01C21/3679 takes precedence
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• CPC Class Codes: G06Q 30/0266 based on the position of the vehicle
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/085 using electronic data carriers
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/012 from other sources than vehicle or roadside beacons, e.g. mobile networks
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/52 specially adapted for the location of the user terminal
• CPC Class Codes: H04L 67/53 using third party service providers
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 69/04 Protocols for data compression, e.g. ROHC

Abstract

Systems and methods for efficiently addressing technical and privacy/authorization obstacles associated with tracking of individuals in a vehicle, and enabling route-based analysis to determine driving behavior, socio-demographics, future profitability, and interests of individuals or self-driving systems. Driving information is collected using a device associated with a driver and a vehicle or using data collected by systems of self-driving vehicles. The frequency and methods used for the collection of driving information can be modified based on location and movement of the device and based on previous classification of the driver or self-driving system, thereby enabling efficient use of bandwidth and battery and increasing accuracy of the classification. The driving information is encoded and transmitted to a server, where future typical route segments that the driver is likely to travel are predicted, and the driver, or the self-driving system, is classified into one or more groups based on the encoded driving information.

Claims

1. A method of collecting driving information and classifying drivers, including human drivers or self-driving systems, the method comprising:

collecting driving information using a device associated with a driver and a vehicle, the driving information including routes driven in the vehicle, geocoded locations, mileage, times of day, and speeds, the collection of driving information being enabled and disabled based on location and movement of the device to reduce use of power and bandwidth;

encoding the driving information and transmitting the encoded driving information to a server;

storing, in a database associated with the server, an identifier associated with the driver and the encoded driving information;

determining, and storing in the database, predicted future typical route segments that the driver is likely to travel over a certain period of time and associated times of day based on the encoded driving information; and

classifying the driver into one or more groups based on the encoded driving information.

2. A method as in claim 1 further comprising:

associating an advertisement or promotional content with at least one of the typical route segments driven by the driver in the vehicle;

determining a point along the associated route segment at which the advertisement or promotional content is to be presented to the driver to provide a sufficient amount of time for the driver to take an action associated with the advertisement or promotional content;

presenting the advertisement or promotional content to the driver via the device when the driver drives along the associated route segment in the vehicle and reaches the point along the associated route segment at which the advertisement or promotional content is to be presented;

enabling the driver to respond to the advertisement or promotional content; and

providing the driver with a share of advertising revenue if the driver responds to the advertisement or promotional content.

3. A method as in claim 2 wherein the point along the route segment at which the advertisement or promotional content is to be presented to the driver is a certain distance before an exit or turn leading to an establishment associated with the advertisement or promotional content, the certain distance determined based on vehicle speed and location of vehicle along the route segment.

4. A method as in claim 2 further including:

providing representations of typical route segments driven by specific target groups of drivers to an advertisement provider; and

receiving from the advertisement provider an advertisement request and a selection of at least one of the route segments driven by the specific target groups of drivers to be associated with an advertisement or promotional content.

5. A method as in claim 1 further including:

matching the driving information with route segments known to be associated with self-driving vehicles;

associating the device and driver identifier with a self-driving capable vehicle status;

identifying time periods, mileage, and route segments during which the vehicle is driven in self-driven mode or driven manually by the driver; and

allocating the driving information according to the identified time periods, mileage, and route segments to affect the classification of the driver or self-driving system into groups reflecting profitability of various insurance products.

6. A method as in claim 2 further including:

presenting the driver with an incentive to visit an establishment associated with at least one of the typical route segments if the driver repeatedly travels along the route segment;

detecting the presence of the driver around the location of the establishment for a sufficient amount of time to determine whether a visit to the establishment occurred to determine whether to allow a redemption of any promotional incentive provided by the establishment; and

crediting an account of the driver with the incentive if the driver visits the establishment and fulfills any requirements of the incentive.

7. A method as in claim 2 wherein the device is a mobile computing device, and wherein the method further includes:

creating a profile of the driver based on the driver'"'"'s use of the mobile device and the encoded driving information;

associating a socio-demographic and potential interests for products and services with the profile of the driver based on the driving information;

associating, based on the socio-demographics and interests, the driver with corresponding advertising categories used by online advertiser networks and exchanges; and

synchronizing the driver'"'"'s activity on the mobile device and the routes driven by the driver and locations visited with the driver'"'"'s virtual activity on internet browsers to represent in the profile of the driver web sites visited using the browser and physical establishments visited or driven by that are associated with the typical route segments to categorize the driver in an advertising category to be used by advertisement providers.

8. A method as in claim 1 wherein:

classifying the driver into one or more groups includes classifying, into the one or more groups, a group of drivers or a fleet of self-driven or partially self-driven vehicles that use materially similar underlying self-driving technology component software, hardware, or sensor configuration;

collecting driving information includes collecting driving information using a device associated with each driver and associated vehicle or using connectivity to processors and systems associated with a self-driven or partially self-driven vehicle, the driving information including mileage, routes driven by the vehicle, time of day, and speed;

classifying the driver into one or more groups based on the encoded driving information includes determining an insurance risk for each driver or, in a case of a fleet of self-driven or partially self-driven vehicles, determining an insurance risk by aggregating the encoded driving information of all vehicles of the fleet that uses similar underlying self-driving technology components, the insurance risk determination including applying (i) a plurality of features of the routes driven including speed limit, historical claims along the routes, and zip codes traversed, and (ii) a plurality of features of the driver including historical routes driven, driving behavior, times of day, and (iii) if known, age, gender, zip code, vehicle identification number, driver'"'"'s license number, vehicle information, credit score, and historical infractions or, in a case of a fleet of self-driven or partially self-driven vehicles, the underlying driving technology including sensors sensitivity, historical cumulative mileage, and associated claims, to assigned weights for scaling and determining the insurance risk; and

the method further comprising;

presenting the driver or underlying driving technology insurance risk classification to a plurality of entities;

receiving requests from at least one of the entities to send promotional messages to drivers within a specific risk classification or to provide insurance for or offer services to the driver or fleet of self-driven vehicles using the underlying driving technology; and

selecting one of the entities to provide insurance for or offer services to the driver or fleet of self-driven vehicles using the underlying driving technology.

9. A method as in claim 8 wherein determining an insurance risk for a plurality of self-driven vehicles includes determining an insurance risk based on a combined mileage for the plurality of self-driven vehicles and a combination of routes driven by the plurality of self-driven vehicles.

10. A method as is claim 8 further including modifying the weights affecting the plurality of factors used to determine the classification of insurance risks in response to actual loss data being provided to cause the determined insurance risk, when applied retroactively to historical data, to correspond more closely to an actual insurance risk as calculated from actual loss data.

11. A method as is claim 1 further including adjusting bandwidth used by the device for collecting the driving information or adjusting frequency or accuracy of collection based on the classification of the driver.

12. A method as is claim 1 wherein classifying the driver into one or more groups includes classifying the driver according to a predicted profitability potential for certain products or services or according to interests, preferences, behavior profile, and socio-demographics of the driver based on the encoded driving information.

13. A system for collecting driving information and classifying drivers, including human drivers or self-driving systems, the system comprising:

a device associated with a driver and a vehicle and configured to (i) collect driving information, the driving information including routes driven in the vehicle, geocoded locations, mileage, times of day, and speeds, (ii) enable and disable the collection of driving information based on location and movement of the device to reduce use of power and bandwidth, (iii) encode the driving information, and (iv) transmit the encoded driving information to a server;

a database associated with the server, in communication with the device, and storing an identifier associated with the driver, the encoded driving information, and predicted future typical route segments that the driver is likely to travel over a certain period of time and associated times of day based on the encoded driving information; and

a processor associated with the server, in communication with the database and the device, and configured to determine the predicted future typical route segments and classify the driver into one or more groups based on the encoded driving information.

14. A system as in claim 13 wherein:

the database is further configured to store advertisements or promotional content received from advertising providers;

the device is further configured to (i) present an advertisement or promotional content to the driver as the driver drives along a route segment corresponding to the advertisement or promotional content and (ii) enable the driver to respond to the advertisement or promotional content; and

the processor is further configured to (i) associate the advertisement or promotional content with at least one route segment corresponding to the advertisement or promotional content in response to a request from an advertising provider, (ii) determine a point along the route segment at which the advertisement or promotional content is to be presented to the driver to provide a sufficient amount of time for the driver to take an action associated with the advertisement or promotional content, (iii) cause the device to present the advertisement or promotional content to the driver at the determined point along the route segment, and (iv) provide the driver with a share of advertising revenue if the driver responds to the advertisement or promotional content.

15. A system as in claim 14 wherein the point along the route segment at which the advertisement or promotional content is to be presented to the driver is a certain distance before an exit or turn leading to an establishment associated with the advertisement or promotional content, and wherein the processor is configured to determine the point along the route segment based on vehicle speed and location of vehicle along the route.

16. A system as in claim 14 wherein the processor is configured to (i) provide representations of typical route segments driven by a target group of drivers to an advertisement provider and (ii) associate advertisements or promotional content with at least one of the route segments based on a selection received from the advertisement provider of the route segment.

17. A system as in claim 13 wherein the processor is configured to (i) match the driving information with route segments known to be associated with self-driving vehicles, (ii) associate the device and driver identifier with a self-driving capable vehicle status, (iii) identify time periods, mileage, and route segments during which the vehicle is driven in self-driven mode or driven manually by the driver, and (iv) allocate the driving information according to the identified time periods, mileage, and route segments to affect the classification of the driver or self-driving system into groups reflecting profitability of various insurance products.

18. A system as in claim 14 wherein the processor is configured to (i) cause the device to present the driver with an incentive to visit an establishment associated with at least one of the route segments driven by the driver if the driver repeatedly travels along the route segment, (ii) detect the presence of the driver around the location of the establishment for a sufficient amount of time to determine whether a visit to the establishment occurred to determine whether to allow a redemption of any promotional incentive provided by the establishment, and (iii) update an account of the driver stored in the database with the incentive if the driver visits the establishment and fulfills any requirements of the incentive.

19. A system as in claim 14 wherein the device is a mobile computing device, and wherein the processor is configured to (i) create a profile of the driver based on the driver'"'"'s use of the mobile device and the encoded driving information, (ii) associate a socio-demographic and potential interests for products and services with the profile of the driver based on the driving information, (iii) associate, based on the socio-demographics and interests, the driver with corresponding advertising categories used by online advertiser networks and exchanges, and (iv) synchronize the driver'"'"'s activity on the mobile device and the routes driven by the driver and locations visited with the driver'"'"'s virtual activity on internet browsers to represent in the profile of the driver web sites visited using the browser and physical establishments visited or driven by that are associated with the typical route segments to categorize the driver in an advertising category to be used by advertisement serving providers.

20. A system as in claim 13 wherein:

the processor is further configured to classify, into the one or more groups, a group of drivers or a fleet of self-driven or partially self-driven vehicles that use materially similar underlying self-driving technology component software, hardware, or sensor configuration;

the device used to collect driving information includes (i) a device associated with each driver and its associated vehicle or (ii) processors and systems associated with a self-driven or partially self-driven vehicle, the driving information including mileage, routes driven by the vehicle, time of day, and speed; and

the processor is further configured to;

classify the driver into one or more groups based on the encoded driving information by determining an insurance risk for each driver or, in a case of a fleet of self-driven or partially self-driven vehicles, determining an insurance risk by aggregating the encoded driving information of all vehicles of the fleet that uses similar underlying self-driving technology components, the insurance risk determination including applying (i) a plurality of features of the routes driven including speed limit, historical claims, and zip codes traversed, and (ii) a plurality of features of the driver including historical routes driven, driving behavior, times of day, and (iii) if known, age, gender, zip code, vehicle identification number, driver'"'"'s license number, vehicle information, credit score, and historical infractions or, in a case of a fleet of self-driven or partially self-driven vehicles, the underlying driving technology including sensors sensitivity, historical cumulative mileage, and associated claims, to assigned weights for scaling and determining the risk; and

present the driver or underlying driving technology insurance risk classification to a plurality of entities;

receive requests from at least one of the entities to send promotional messages to drivers within a specific risk classification or provide insurance for or offer services to the driver or fleet of self-driven vehicles using the underlying driving technology; and

enable selection of one of the entities to provide insurance for or offer services to the driver or fleet of self-driven vehicles using the underlying driving technology.

21. A system as in claim 20 wherein the processor is configured to determine the insurance risk based on a combined mileage for a plurality of self-driven vehicles and a combination of routes driven by the plurality of self-driven vehicles.

22. A system as in claim 20 wherein the processor is configured to determine when a driver is driving a self-driven vehicle and when the self-driven vehicle is in an automatic driving mode.

23. A system as is claim 20 wherein the processor is configured to modify the weights affecting the plurality of factors used to determine the classification of insurance risks in response to actual loss data being provided to cause the determined insurance risk, when applied retroactively to historical data, to correspond more closely to an actual insurance risk as calculated from actual loss data.

24. A system as is claim 13 wherein the device or processor is configured to adjust bandwidth used by the device for collecting the driving information or adjusting frequency or accuracy of collection based on the classification of the driver.

25. A system as is claim 13 wherein the processor classifies the driver into one or more groups according to a predicted profitability potential for certain products or services or according to interests, preferences, behavior profile, and socio-demographics of the driver based on the encoded driving information.