Method and apparatus for road risk indices generation

• US 9,519,670 B2
• Filed: 08/29/2014
• Issued: 12/13/2016
• Est. Priority Date: 08/29/2014
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Stanek, Joseph F., Michelini, John Ottavio, Feldkamp, Timothy Mark, Lu, Jianbo, Tseng, Fling, D'Amato, Anthony Mario, Kumar, Pankaj, Baker, Ryan Lee, Throop, Medville J.
• Primary Examiner(s): EL CHANTI, HUSSEIN A
• Application Number:
  US14/473,272
• Publication Number:
  US 20160063045A1
• Time in Patent Office: 837 Days
• Field of Search: 701/301
• US Class Current: 1/1
• CPC Class Codes: G06F 16/2272 Management thereof
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083
• 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

Abstract

A system includes a processor configured to gather historical risk-affecting data with respect to a current road. The processor is also configured to gather current risk-affecting data with respect to the current road. Further, the processor is configured to generate a baseline risk index for the road based on the historical data. The processor is additionally configured to modify the baseline risk index based on the current data and provide a risk index value for the current road based on the modified baseline risk index

Claims

1. A system comprising:

a processor configured to;

gather historical accident-risk-affecting data and current accident-risk-affecting conditions with respect to a current road;

weight an effect that the conditions have on a known driver, based on a driver profile storing observed effects on driving behavior under similar conditions;

generate a risk value, representing an accident likelihood, for the driver, based on the historical data and the weighted effect; and

present the risk value to the driver.

2. The system of claim 1, wherein the historical accident-risk-affecting data includes road data.

3. The system of claim 1, wherein the current accident-risk-affecting conditions includes current road conditions.

4. The system of claim 1, wherein the historical accident-risk-affecting data includes traffic data.

5. The system of claim 1, wherein the current accident-risk-affecting conditions includes current traffic conditions.

6. The system of claim 1, wherein the historical accident-risk-affecting data includes environmental data.

7. The system of claim 1, wherein the current accident-risk-affecting conditions includes current environmental conditions.

8. The system of claim 1, wherein the current accident-risk-affecting conditions includes a current vehicle operator state.

9. The system of claim 1, wherein the historical accident-risk-affecting data includes vehicle system state data.

10. The system of claim 1, wherein the current accident-risk-affecting conditions includes current vehicle system conditions.

11. The system of claim 1, wherein the processor is further configured to generate the risk value based on observed effects of the historical risk-affecting data with respect to the current road.

12. The system of claim 1, wherein the processor is further configured to generate the risk value based on observed effects of the historical risk-affecting data with respect to roads having similar characteristics to the current road.

13. A computer-implemented method comprising:

gathering historical accident-risk-affecting data and current accident-risk-affecting conditions with respect to a current road;

weighting an effect that the conditions have on a known driver, based on a driver profile storing observed effects on driving behavior under similar conditions;

generating a risk value, representing an accident likelihood, for the driver, based on the historical data and the weighted effect; and

presenting the risk value to the driver.

14. A non-transitory machine-readable storage medium, storing instructions that, when executed, cause a processor to perform a method comprising:

gathering historical accident-risk-affecting data and current accident-risk-affecting conditions with respect to a current road;

weighting an effect that the conditions have on a known driver, based on a driver profile storing observed effects on driving behavior under similar conditions;

generating a risk value, representing an accident likelihood, for the driver, based on the historical data and the weighted effect; and

presenting the risk value to the driver.