Route Risk Mitigation

• US 20160167652A1
• Filed: 06/08/2015
• Published: 06/16/2016
• Est. Priority Date: 05/10/2007
• Status: Active Grant

• Current Assignee: Arity International Limited (Allstate Corporation)
• Original Assignee: Allstate Insurance Company (Allstate Corporation)
• Inventors: Slusar, Mark V.
• Granted Patent: US 9,932,033 B2
• Time in Patent Office: Days
• CPC Class Codes: B60W 2050/146 Display means
• CPC Class Codes: B60W 2520/10 Longitudinal speed
• CPC Class Codes: B60W 2520/26 Wheel slip
• CPC Class Codes: B60W 2552/00 Input parameters relating to infrastructure
• CPC Class Codes: B60W 2552/05 Type of road, e.g. motorways, local streets, paved or unpaved roads
• CPC Class Codes: B60W 2552/10 Number of lanes
• CPC Class Codes: B60W 2552/20 Road profile, i.e. the change in elevation or curvature of a plurality of continuous road segments
• CPC Class Codes: B60W 2552/35 Road bumpiness, e.g. potholes
• CPC Class Codes: B60W 2552/40 Coefficient of friction
• 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 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 2710/18 Braking system
• CPC Class Codes: B60W 2710/20 Steering systems
• CPC Class Codes: B60W 2720/10 Longitudinal speed
• CPC Class Codes: B60W 30/09 Taking automatic action to avoid collision, e.g. braking and steering
• CPC Class Codes: B60W 30/143 Speed control B60W30/16 takes precedence
• CPC Class Codes: B60W 50/082 Selecting or switching between different modes of propelling
• CPC Class Codes: B60W 60/0059 Estimation of the risk associated with autonomous or manual driving, e.g. situation too complex, sensor failure or driver incapacity
• CPC Class Codes: G01C 21/3461 Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types, segments such as motorways, toll roads, ferries
• CPC Class Codes: G01C 21/3874 : Structures specially adapted for data searching and retrieval
• CPC Class Codes: G06Q 40/08 : Insurance

Abstract

A method is disclosed for analyzing historical accident information to adjust driving actions of an autonomous vehicle over a travel route in order to avoid accidents which have occurred over the travel route. Historical accident information for the travel route can be analyzed to, for example, determine accident types which occurred over the travel route and determine causes and/or probable causes of the accident types. In response to determining accident types and causes/probable causes of the accident types over the travel route, adjustments can be made to the driving actions planned for the autonomous vehicle over the travel route. In addition, in an embodiment, historical accident information can be used to analyze available travel routes and select a route which presents less risk of accident than others.

Claims

1. A method, comprising:

receiving a first travel route for an autonomous vehicle;

determining an autonomous route risk value for the first travel route using historical accident information of vehicles engaged in autonomous driving over the first travel route;

determining a manual route risk value for the first travel route using historical accident information of vehicles engaged in manual driving over the first travel route;

comparing the autonomous route risk value to the manual route risk value and determine which of autonomous driving or manual driving provides a lower risk of accident over the first travel route; and

storing the determination that either autonomous driving or manual driving provides the lower risk of accident over the first travel route.

2. The method of claim 1, further comprising:

notifying an operator of an autonomous vehicle of the determination that either autonomous driving or manual driving provides the lower risk of accident over the first travel route.

3. The method of claim 1, wherein when manual driving provides the lower risk of accident over the first travel route,

adjusting driving actions planned for the autonomous vehicle over the first travel route to decrease a risk of accident over the first travel route.

4. The method of claim 3, further comprising analyzing the historical accident information of vehicles engaged in autonomous driving over the first travel route and the historical accident information of vehicles engaged in manual driving over the first travel route to identify accident types which occur more frequently over the first travel route for vehicles engaged in autonomous driving than vehicles engaged in manual driving.

5. The method of claim 4 wherein the analysis identifies a first accident type, the method further comprising determining a probable cause of the first accident type, and adjusting the driving actions planned for the autonomous vehicle over the first travel route based on the probable cause to avoid the first accident type.

6. The method of claim 5 wherein the probable cause of the first accident type is excess speed, the adjusting driving actions step comprises reducing a speed of travel of the autonomous vehicle over the first travel route.

7. The method of claim 5 wherein the probable cause of the first accident type is lack of vehicle traction on the road, the adjusting driving actions step comprises engaging an all-wheel-drive function of the autonomous vehicle.

8. The method of claim 5 wherein the probable cause of the first accident type is wildlife crossing, the adjusting driving actions step comprises reduce a speed of travel of the autonomous vehicle and prepare for sudden braking and/or evasive maneuvers.

9. The method of claim 4 further comprising creating a list of the accident types which occur more frequently over the first travel route for vehicles engaged in autonomous driving than vehicles engaged in manual driving.

10. The method of claim 1 further comprising overriding an autonomous driving over the first travel route when manual driving provides the lower risk of accident over the first travel route.

11. A method, comprising:

receiving a first travel route and a second travel route for an autonomous vehicle;

determining a first route risk value for the first travel route and a second route risk value for the second travel route;

comparing the first route risk value to the second route risk value to determine a travel route which provides less route risk;

selecting as a selected route of travel the travel route which provides less route risk.

12. The method of claim 11 further comprising analyzing historical accident information of vehicles engaged in autonomous driving over the selected travel route and identify a first accident type which occurs over the selected travel route for vehicles engaged in autonomous driving.

13. The method of claim 12 further comprising identifying a probable cause of the first accident type and adjusting the driving actions planned for the autonomous vehicle over the selected travel route based on the probable cause to avoid the first accident type.

14. The method of claim 13 wherein the probable cause of the first accident type is excess speed, the adjusting driving actions step comprises reducing a speed of travel of the autonomous vehicle over the selected travel route.

15. The method of claim 13 wherein the probable cause of the first accident type is lack of vehicle traction on the road, the adjusting driving actions step comprises engaging an all-wheel-drive function of the autonomous vehicle over the selected travel route.

16. The method of claim 13 wherein the probable cause of the first accident type is a wildlife crossing, the adjusting driving actions step comprises reduce a speed of travel of the autonomous vehicle and prepare for sudden braking and/or evasive maneuvers over the selected travel route.

17. The method of claim 12 further comprising identifying a plurality of accident types that occur over the selected travel route and notifying an operator of the autonomous vehicle of the plurality of accident types.

18. The method of claim 17 wherein the notification is one of a list of the plurality of accident types and a route safety map which identifies where the accident types have occurred along the selected travel route.

19. A driving analysis computing device comprising:

a processing unit comprising a processor;

a memory unit; and

an electronic receiver, wherein the driving analysis computing device is configured to;

receive via the electronic receiver a first vehicle operational data collected by a first vehicle data acquiring component, wherein the first vehicle operational data comprises driving data collected from a first vehicle engaged in autonomous driving;

receive a first travel route and a second travel route for an autonomous vehicle;

determine a first route risk value for the first travel route and a second route risk value for the second travel route;

compare the first route risk value to the second route risk value to determine a travel route which provides less route risk;

select as a selected route of travel the travel route which provides less route risk.

20. The device of claim 19 further comprising a historical accident information database and wherein the device is further configured to analyze the historical accident information database and identify a first accident type which occurs over the selected travel route for vehicles engaged in autonomous driving over the selected travel route;

identify a probable cause of the first accident type; and

adjust the driving actions planned for the autonomous vehicle over the selected travel route based on the probable cause in order to avoid the first accident type.