Determining vehicle collision risk

• US 9,242,654 B2
• Filed: 06/27/2014
• Issued: 01/26/2016
• Est. Priority Date: 06/27/2014
• Status: Active Grant

• Current Assignee: Hyundai Motor Company (Hyundai Motor Group), Kia Corp.
• Original Assignee: International Business Machines Corporation
• Inventors: Woods, Eric, Do, Lydia M., Nesbitt, Pamela A.
• Primary Examiner(s): NGUYEN, TAN QUANG
• Application Number:
  US14/317,214
• Publication Number:
  US 20150375756A1
• Time in Patent Office: 578 Days
• Field of Search: 701/70, 701/301, 701/96, 701/32.2, 701/32.3, 701/33.4, 340/540, 340/425.5
• US Class Current: 1/1
• CPC Class Codes: B60W 2050/0075 Automatic parameter input, automatic initialising or calibrating means
• CPC Class Codes: B60W 2050/143 Alarm means
• CPC Class Codes: B60W 2050/146 Display means
• CPC Class Codes: B60W 2520/10 Longitudinal speed
• CPC Class Codes: B60W 2540/043 Identity of occupants
• CPC Class Codes: B60W 2540/30 Driving style
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2554/804 Relative longitudinal speed
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 2556/45 External transmission of data to or from the vehicle
• CPC Class Codes: B60W 2556/55 using telemetry
• CPC Class Codes: B60W 2556/65 Data transmitted between vehicles
• CPC Class Codes: B60W 30/08 Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
• CPC Class Codes: B60W 30/0956 the prediction being responsive to traffic or environmental parameters
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: B60W 50/14 Means for informing the driver, warning the driver or prompting a driver intervention
• 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

An approach is provided for determining vehicle accident risk. The approach determines a current vehicle state of the vehicle during operation of the vehicle. The current vehicle state is based on one or more intra-vehicle factors and one or more extra-vehicle factors that pertaining to the vehicle. The current vehicle state is compared to a number of historic accident-related vehicle states that correspond to a vehicle accidents previously recorded for a number of vehicles. Based on the comparison, the process detects an accident risk that pertains to the vehicle. The driver of the vehicle is then notified of the detected accident risk.

Claims

1. A method, in an information handling system comprising a processor and a memory, of determining vehicle accident risk, the method comprising:

during operation of a selected vehicle, determining a current vehicle state of the selected vehicle, wherein the current vehicle state is based on a predicted upcoming geographic location of the selected vehicle;

comparing the current vehicle state to a plurality of historic accident-related vehicle states that correspond to a plurality of vehicle accidents previously recorded for a plurality of vehicles;

detecting an accident risk pertaining to the selected vehicle based on the comparison;

determining whether the detected accident risk reaches a threshold, wherein the threshold is based on a current driver profile corresponding to a driver of the selected vehicle; and

notifying the driver of the selected vehicle of the detected accident risk based upon the determination.

2. The method of claim 1 further comprising:

identifying the predicted upcoming geographic location of the selected vehicle based on a current speed and a current direction of the selected vehicle;

identifying an accident occurrence value pertaining to the identified predicted upcoming geographic location by retrieving one or more accident records from the plurality of vehicle accidents, wherein the retrieved accident records are proximate to the predicted upcoming geographic location;

comparing the accidence occurrence value to the threshold;

determining, based on the comparison, that the predicted upcoming geographic location is a high risk location; and

notifying the driver of the high risk location.

3. The method of claim 1, wherein the current vehicle state is further based on one or more intra-vehicle factors and one or more extra-vehicle factors.

4. The method of claim 3 further comprising:

retrieving the current driver profile corresponding to the driver of the selected vehicle, wherein the current driver profile includes an experience level of the driver, an age of the driver, any impediments of the driver, a previous accident history of the driver, and any driving restrictions of the driver;

identifying a driver risk level based on the retrieved current driver profile; and

adjusting the threshold based on the identified driver risk level.

5. The method of claim 3 wherein at least one of the intra-vehicle factors is selected from a group consisting of a vehicle quality factor, a vehicle performance factor, a current driving style factor, a current distraction factor, a current mobile phone usage factor, a current electronic device usage factor, a current noise volume factor, a current passenger activity level factor, a current conversation level factor, and a current driver profile factor.

6. The method of claim 3 wherein at least one of the extra-vehicle factors is selected from a group consisting of a time of day factor, a day of week factor, a day of year factor, a traffic congestion factor, a traffic relative positions factor, a traffic throughput factor, a traffic average speed factor, a road construction factor, an external vehicles anomalies factor, a weather factor, a sunlight intensity factor, a darkness factor, a sun position factor, and a sun orientation factor.

7. The method of claim 1 further comprising:

sending a second notification to a non-driver, wherein the second notification informs the non-driver of the detected accident risk.

8. An information handling system comprising:

one or more processors;

a memory coupled to at least one of the processors;

a network adapter that connects the information handling system to a computer network; and

a set of instructions stored in the memory and executed by at least one of the processors to determining vehicle accident risk, wherein the set of instructions perform actions of;

during operation of a selected vehicle, determining a current vehicle state of the selected vehicle, wherein the current vehicle state is based on a predicted upcoming geographic location of the selected vehicle;

comparing the current vehicle state to a plurality of historic accident-related vehicle states that correspond to a plurality of vehicle accidents previously recorded for a plurality of vehicles;

detecting an accident risk pertaining to the selected vehicle based on the comparison;

determining whether the detected accident risk reaches a threshold, wherein the threshold is based on a current driver profile corresponding to a driver of the selected vehicle; and

notifying the driver of the selected vehicle of the detected accident risk based upon the determination.

9. The information handling system of claim 8 wherein the actions further comprise:

identifying the predicted upcoming geographic location of the selected vehicle based on a current speed and a current direction of the selected vehicle;

identifying an accident occurrence value pertaining to the identified predicted upcoming geographic location by retrieving one or more accident records from the plurality of vehicle accidents, wherein the retrieved accident records are proximate to the predicted upcoming geographic location;

comparing the accidence occurrence value to the threshold;

determining, based on the comparison, that the predicted upcoming geographic location is a high risk location; and

notifying the driver of the high risk location.

10. The information handling system of claim 8, wherein the current vehicle state is further based on one or more intra-vehicle factors and one or more extra-vehicle factors.

11. The information handling system of claim 10 wherein the actions further comprise:

retrieving the current driver profile corresponding to the driver of the selected vehicle, wherein the current driver profile includes an experience level of the driver, an age of the driver, any impediments of the driver, a previous accident history of the driver, and any driving restrictions of the driver;

identifying a driver risk level based on the retrieved current driver profile; and

adjusting the threshold based on the identified driver risk level.

12. The information handling system of claim 10 wherein at least one of the intra-vehicle factors is selected from a group consisting of a vehicle quality factor, a vehicle performance factor, a current driving style factor, a current distraction factor, a current mobile phone usage factor, a current electronic device usage factor, a current noise volume factor, a current passenger activity level factor, a current conversation level factor, and a current driver profile factor.

13. The information handling system of claim 10 wherein at least one of the extra-vehicle factors is selected from a group consisting of a time of day factor, a day of week factor, a day of year factor, a traffic congestion factor, a traffic relative positions factor, a traffic throughput factor, a traffic average speed factor, a road construction factor, an external vehicles anomalies factor, a weather factor, a sunlight intensity factor, a darkness factor, a sun position factor, and a sun orientation factor.

14. The information handling system of claim 8 wherein the actions further comprise:

sending a second notification to a non-driver, wherein the second notification informs the non-driver of the detected accident risk.

15. A computer program product stored in a computer readable storage medium, comprising computer instructions that, when executed by an information handling system, causes the information handling system to determining vehicle accident risk by performing actions comprising:

during operation of a selected vehicle, determining a current vehicle state of the selected vehicle, wherein the current vehicle state is based on a predicted upcoming geographic location of the selected vehicle;

comparing the current vehicle state to a plurality of historic accident-related vehicle states that correspond to a plurality of vehicle accidents previously recorded for a plurality of vehicles;

detecting an accident risk pertaining to the selected vehicle based on the comparison;

determining whether the detected accident risk reaches a threshold, wherein the threshold is based on a current driver profile corresponding to a driver of the selected vehicle; and

notifying the driver of the selected vehicle of the detected accident risk based upon the determination.

16. The computer program product of claim 15 wherein the actions further comprise:

identifying the predicted upcoming geographic location of the selected vehicle based on a current speed and a current direction of the selected vehicle;

identifying an accident occurrence value pertaining to the identified predicted upcoming geographic location by retrieving one or more accident records from the plurality of vehicle accidents, wherein the retrieved accident records are proximate to the predicted upcoming geographic location;

comparing the accidence occurrence value to the threshold;

determining, based on the comparison, that the predicted upcoming geographic location is a high risk location; and

notifying the driver of the high risk location.

17. The computer program product of claim 15, wherein the current vehicle state is further based on one or more intra-vehicle factors and one or more extra-vehicle factors.

18. The computer program product of claim 17 wherein the actions further comprise:

retrieving the current driver profile corresponding to the driver of the selected vehicle, wherein the current driver profile includes an experience level of the driver, an age of the driver, any impediments of the driver, a previous accident history of the driver, and any driving restrictions of the driver;

identifying a driver risk level based on the retrieved current driver profile; and

adjusting the threshold based on the identified driver risk level.

19. The computer program product of claim 17 wherein at least one of the intra-vehicle factors is selected from a group consisting of a vehicle quality factor, a vehicle performance factor, a current driving style factor, a current distraction factor, a current mobile phone usage factor, a current electronic device usage factor, a current noise volume factor, a current passenger activity level factor, a current conversation level factor, and a current driver profile factor.

20. The computer program product of claim 17 wherein at least one of the extra-vehicle factors is selected from a group consisting of a time of day factor, a day of week factor, a day of year factor, a traffic congestion factor, a traffic relative positions factor, a traffic throughput factor, a traffic average speed factor, a road construction factor, an external vehicles anomalies factor, a weather factor, a sunlight intensity factor, a darkness factor, a sun position factor, and a sun orientation factor.