SYSTEM AND METHOD FOR NOTIFYING VEHICLE DRIVER OF LOCALIZED DRIVING CONDITIONS

• US 20130338914A1
• Filed: 06/14/2012
• Published: 12/19/2013
• Est. Priority Date: 06/14/2012
• Status: Abandoned Application

• Current Assignee: Wavemarket Incorporated (Gen Digital Inc.)
• Original Assignee: Wavemarket Incorporated (Gen Digital Inc.)
• Inventors: Weiss, Andrew
• Application Number:
  US13/523,025
• Publication Number:
  US 20130338914A1
• Time in Patent Office: Days
• US Class Current: 701/465
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0141 for traffic information dissemination
• CPC Class Codes: G08G 1/09626 where the origin of the information is within the own vehicle, e.g. a local storage device, digital map
• CPC Class Codes: G08G 1/096716 where the received information does not generate an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096741 where the source of the transmitted information selects which information to transmit to each vehicle
• CPC Class Codes: G08G 1/096775 where the origin of the information is a central station

Abstract

A driving assessment system and method is described that automatically assesses driving conditions around a driver to identify safety hazards and to subsequently inform that driver when an unusually hazardous condition exists. The driving assessment is performed by obtaining and storing safety related data from the driver and from external sources and then processing that data in real time to produce a driving hazard assessment and warning. Beneficially the driving hazard assessment automatically obtains and considers existing conditions of the road system local to the driver.

Claims

1. A processor based method comprising:

accessing current location data of a mobile device corresponding to a first user;

accessing environmental data corresponding to the current location data;

generating a driving condition assessment based on the current location data and the environmental data; and

providing the driving condition assessment to the first user.

2. The processor based method of claim 1, wherein the driving condition assessment comprises a risk assessment corresponding to a road on which the mobile device travels.

3. The processor based method of claim 1, wherein the environmental data comprises a physical road condition, the method further comprising:

determining an estimated time of arrival of the mobile device at a location of the physical road condition based at least on the current location data of the mobile device; and

providing the driving condition assessment with an indication of the physical road condition to the first user a predetermined period of time prior to the estimated time of arrival at the location of the physical road condition.

4. The processor based method of claim 3, wherein the physical road condition comprises at least one of:

a pothole;

a curve;

an intersection;

an animal crossing;

a construction area; and

a location corresponding to an elevated auto accident rate.

5. The processor based method of claim 1, wherein the environmental data comprises a weather condition, the method further comprising:

determining an estimated time of arrival of the mobile device at a location of the weather condition based at least on the current location data of the mobile device; and

providing the driving condition assessment with an indication of the weather condition to the first user a predetermined period of time prior to the estimated time of arrival at the location of the weather condition.

6. The processor based method of claim 5, wherein the weather condition comprises at least one of:

a rain condition;

an ice condition;

a snow condition; and

a fog condition.

7. The processor based method of claim 1, further comprising:

determining ambient lighting corresponding to a current time of day; and

generating the driving condition assessment further based on the determined ambient lighting.

8. The processor based method of claim 1, further comprising:

accessing descriptive data of a motor vehicle corresponding to the first user; and

generating the driving condition assessment further based on the motor vehicle descriptive data.

9. The processor based method of claim 8, wherein the motor vehicle descriptive data comprises at least one of the motor vehicle:

make;

model;

age;

mileage;

predetermined design defects;

maintenance history;

tire age; and

tire distance traveled.

10. The processor based method of claim 1, further comprising:

accessing current speed data of a mobile device corresponding to the first user; and

generating the driving condition assessment further based on the current speed data of the mobile device.

11. The processor based method of claim 10, further comprising:

determining a speed limit corresponding to a road corresponding to the current location;

comparing the current speed data with the speed limit; and

generating the driving condition assessment further based on the comparison of the current speed data with the speed limit.

12. The processor based method of claim 1, further comprising:

accessing driving history data corresponding to the first user; and

generating the driving condition assessment further based on the driving history data.

13. The processor based method of claim 1, further comprising

accessing predetermined sensor data corresponding to the mobile device;

predicting a driver skill level of the first user based on the predetermined sensor data; and

generating the driving condition assessment further based on the predicted driver skill level.

14. The processor based method of claim 13, wherein the predetermined sensor data comprises at least one of location data generated via the mobile device and location data generated through cell site interpolation.

15. The processor based method of claim 13, wherein the predetermined sensor data comprises predetermined location data and predetermined velocity data generated via the mobile device.

16. The processor based method of claim 13, wherein the predetermined sensor data comprises predetermined location data, predetermined velocity data, and predetermined acceleration data generated via the mobile device.

17. The processor based method of claim 13, the method further comprising accessing map data corresponding to the predetermined location data, wherein predicting the driver skill level comprises comparing the predetermined sensor data and the map data.

18. The processor based method of claim 17, wherein the predetermined sensor data comprises at least one of:

predetermined location data;

predetermined velocity data; and

predetermined acceleration data;

wherein the map data comprises at least one of;

indications of traffic intersections;

indications of traffic signs; and

indications of traffic signals;

and wherein comparing the predetermined sensor data with the map data comprises determining at least one of the velocity and acceleration of the mobile device at or a predetermined distance from a corresponding traffic intersection, traffic sign, or traffic signal based on the predetermined sensor data.

19. The processor based method of claim 17, wherein the predetermined sensor data comprises at least one of:

predetermined location data;

predetermined speed data; and

predetermined acceleration data;

wherein the map data comprises rule definitions comprising at least one of;

indications of road directional restrictions;

indications of lane configurations;

indications of traffic intersections;

indications of traffic signs; and

indications of traffic signals;

and wherein predicting the driver skill level comprises predicting if a vehicle in which the mobile device travels has adhered to the rule definitions based on the comparison of the predetermined sensor data and the map data.

20. The processor implemented method of claim 1, further comprising:

accessing data comprising at least one of a gender of the first user, an age of the first user, and an indication of the health of the first user; and

generating the driving condition assessment further based on the at least one of the gender of the first user, the age of the first user, and the indication of the health of the first user.

21. The processor implemented method of claim 1, further comprising:

accessing current location data of a mobile device corresponding to a second user;

comparing the current location data of the mobile device corresponding to the first user and the current location data of the mobile device corresponding to the second user; and

generating the driving condition assessment further based on the comparison of the current location data of the mobile device corresponding to the first user and the current location data of the mobile device corresponding to the second user.

22. The processor based method of claim 21, further comprising:

accessing driving history data corresponding to the second user; and

generating the driving condition assessment further based on the driving history data corresponding to the second user.

23. The processor based method of claim 21, further comprising:

accessing predetermined sensor data corresponding to the mobile device corresponding to the second user;

predicting a driver skill level of the second user based on the predetermined sensor data; and

generating the driving condition assessment further based on the predicted driver skill level of the second user.

24. The processor implemented method of claim 21, further comprising:

accessing current sensor data comprising the current location data and at least one of current velocity data and current acceleration data corresponding to the second user;

applying a classifier to the current sensor data corresponding to the second user;

generating the driving condition assessment further based on the application of the classifier to the current sensor data corresponding to the second user.

25. The processor implemented method of claim 21, further comprising:

accessing current sensor data comprising the current location data and at least one of current velocity data and current acceleration data corresponding to the first user;

accessing current sensor data comprising the current location data and at least one of current velocity data and current acceleration data corresponding to the second user;

determining based on the current sensor data corresponding to the first and second users that the second user is on a trajectory corresponding to a prospective future location of the first user; and

generating the driving condition assessment further based on the determined trajectory of the second user.

26. The processor implemented method of claim 25, further comprising:

determining based on the current sensor data corresponding to the second user that the second user is driving in an unsafe manner; and

generating the driving condition assessment further based on the determination that the second user is driving in an unsafe manner.

27. The processor implemented method of claim 21, further comprising:

accessing driving history data corresponding to the second user;

determining based on the driving history data that the second user is at risk to drive in an unsafe manner; and

generating the driving condition assessment further based on the determination that the second user is at risk to drive in an unsafe manner.

28. The processor implemented method of claim 21, further comprising:

accessing current sensor data and driving history data corresponding to the second user;

determining based on at least one of the current sensor data and the driving history data that the second user is on a trajectory corresponding to a prospective future location of the first user and that the second user is at risk to drive in an unsafe manner; and

generating the driving condition assessment further based on the determination that the second user is on a trajectory corresponding to a prospective future location of the first user and that the second user is at risk to drive in an unsafe manner.

29. The processor implemented method of claim 28, wherein the current sensor data corresponding to the second user comprises at least one of current acceleration data, current velocity data, and the current location data of the mobile device corresponding to the second user.

30. The processor implemented method of claim 21, further comprising:

training a classifier based on predetermined sensor data specific to a type of vehicle driven by the second user;

accessing current sensor data corresponding to the second user;

applying the classifier to the current sensor data corresponding to the second user; and

generating the driving condition assessment further based on the application of the classifier to the current sensor data corresponding to the second user.

31. The processor implemented method of claim 1, further comprising:

training a classifier based on predetermined sensor data specific to a type of vehicle driven by the first user;

accessing current sensor data corresponding to the first user; and

applying a classifier to the current sensor data to generate the driving condition assessment.

32. A driving hazard assessment and warning system, comprising:

a first mobile device having communications capabilities and producing first user location data, wherein the first user location data corresponds to a road;

a road description database comprising data corresponding to the road;

a computer input receiving the first user location data;

an alert subsystem; and

a processor operatively connected to the road description database, to the alert subsystem, and to the computer input;

wherein the processor uses the first user location data to access a description of the road from the road description database;

wherein the processor analyzes the obtained description of the road to identify a substantial safety hazard; and

wherein the processor causes the alert subsystem to output a warning if the processor identifies a substantial safety hazard.

33. The driving hazard assessment and warning system according to claim 32, further comprising weather data operatively input to the processor, and

wherein the processor analyzes the weather data to identify a substantial safety hazard.

34. The driving hazard assessment and warning system according to claim 33, wherein the weather data is operatively input to the processor from a weather database.

35. The driving hazard assessment and warning system according to claim 33, wherein weather data comprises data corresponding to at least one of the following:

rain, ice, snow, fog, time of day, sunrise time, sunset time, sleet, ambient light.

36. The driving hazard assessment and warning system according to claim 32, further comprising a vehicle database operatively connected to the processor,

wherein the processor identifies a first vehicle based on the first mobile device;

wherein the processor uses the first vehicle to access a description of the first vehicle from the vehicle database; and

wherein the processor analyzes the description of the first vehicle to identify a substantial safety hazard.

37. The driving hazard assessment and warning system according to claim 36, wherein the vehicle database comprises data corresponding to at least one of the following:

make, model, age, mileage, design defects, tire age, and tire mileage of the first vehicle.

38. The driving hazard assessment and warning system according to claim 32, wherein the processor creates a driver classification database, populates the driver classification database with at least one classification of the first user, and uses the driver classification database to identify a substantial safety hazard.

39. The driving hazard assessment and warning system according to claim 38, wherein the least one classification of the first user is that the first user is prone to speeding, drunk driving, driving while distracted, reckless driving, running red lights, running stop signs, driving the wrong direction, unsafe lane changes, tailgating, improper turns, road rage, drowsy driving, and street racing.

40. The driving hazard assessment and warning system according to claim 32 wherein the road description database comprises data corresponding to at least one of the following:

a pothole, a sharp curve, a multi-way stop, an animal crossing, road construction, and a high accident rate.

41. The driving hazard assessment and warning system according to claim 32, further comprising a second mobile device having communications capabilities and producing second user location data, wherein the second user location data corresponds to a second road;

wherein the computer input receives the second user location data; and

wherein the processor receives and analyzes the second user location data to determine if the second user presents a substantial safety hazard.

42. The driving hazard assessment and warning system according to claim 41, wherein the processor analyzes the first user location data and the second user location data to determine if there is a substantial crash hazard.

43. The driving hazard assessment and warning system according to claim 42, wherein the processor uses the second mobile device to obtain a description of the second vehicle from the vehicle database, and wherein the processor analyzes the description of the second vehicle to identify a substantial safety hazard.

44. The driving hazard assessment and warning system according to claim 32, further comprising a driver database operatively connected to the processor,

wherein the processor identifies a first user based on the first mobile device;

wherein the processor uses the first user identification to access a description of the first user from the driver database; and

wherein the processor analyzes the description of the first user to identify a substantial safety hazard.

45. The driving hazard assessment and warning system according to claim 44, wherein the driver database comprises data corresponding to at least one of the following:

age of the first user, gender of the first user, health of the first user, tobacco usage, alcohol usage, drug usage of the first user.

46. The driving hazard assessment and warning system according to claim 44, further comprising a second mobile device having communications capabilities and producing second user location data, wherein the second user location data corresponds to a second road;

wherein the computer input receives the second user location data;

wherein the processor receives and analyzes the second user location data to determine if the second user presents a substantial safety hazard; and

wherein the driver database comprises second user data corresponding to at least one of the following;

age of the second driver, gender of the second driver, health of the second driver, tobacco usage, alcohol usage, drug usage of the second driver and wherein the processor analyzes the second driver data to identify a substantial safety hazard.

47. The driving hazard assessment and warning system according to claim 46, wherein the driver classification database comprises at least one classification of the second user.

48. The driving hazard assessment and warning system according to claim 47, wherein the least one classification of the second user is that the second user is prone to speeding, drunk driving, driving while distracted, reckless driving, running red lights, running stop signs, driving the wrong direction, unsafe lane changes, tailgating, improper turns, road rage, drowsy driving, and street racing.