Methods and systems for generating a horizon for use in advanced driver assistance system (ADAS)

• US 9,726,505 B2
• Filed: 11/01/2013
• Issued: 08/08/2017
• Est. Priority Date: 11/02/2012
• Status: Active Grant

• Current Assignee: TomTom Navigation B.V (TomTom NV)
• Original Assignee: TomTom Navigation B.V (TomTom NV)
• Inventors: Manoliu, Adrian-Victor, Trofimov, Sergey Yuryevich, Raginsky, Alexander, Zheltov, Stanislav, Serbanescu, Alexandru, Koponen, Lauri, Schaminee, Johannes
• Primary Examiner(s): Nguyen, Nga X
• Application Number:
  US14/440,089
• Publication Number:
  US 20150300825A1
• Time in Patent Office: 1,376 Days
• Field of Search: 701 36, 701409, 701468, 701117, 701408, 701410, 701422, 701454, 701450, 701300, 701540, 340905, 3409951
• CPC Class Codes: B60R 16/023 for transmission of signals between vehicle parts or subsystems
• CPC Class Codes: B60T 7/12 for automatic initiation; for initiation not subject to will of driver or passenger limiting speed of vehicles other than rail vehicles B60K31/00
• 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 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 30/10 Path keeping cruise control for automatically following a preceding vehicle B60W30/165
• CPC Class Codes: B60W 30/18145 Cornering
• CPC Class Codes: B60W 30/18154 Approaching an intersection
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: G01C 21/26 specially adapted for navigation in a road network
• CPC Class Codes: G01C 21/32 Structuring or formatting of map data
• CPC Class Codes: G01C 21/3484 Personalized, e.g. from learned user behaviour or user-defined profiles
• CPC Class Codes: G01C 21/3667 Display of a road map G01C21/3614 takes precedence; guidance using 3D or perspective road maps G01C21/3635
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G08G 1/16 Anti-collision systems road vehicle drive control systems for predicting or avoiding probable or impending collision otherwise than by control of a particular sub-unit B60W30/08

Abstract

A method of generating a horizon for use by an ADAS of a vehicle involves using digital location-based data, driver data and/or vehicle data to determine the likelihood that different outgoing paths are taken at a decision point along a currently traversed road segment, and deriving a probability that each path may be taken. The probability may be based on one or more of: an angle of the path relative to the incoming path, the road class of the path, a speed profile of the path, historical paths taken by vehicles at the decision point, and historical paths taken at the decision point by the individual driver or vehicle.

Claims

1. A method of generating a horizon for use in an advanced driver assistance system (ADAS) of a vehicle, comprising:

obtaining data representing a pre-calculated route to be traveled by the vehicle;

determining a most probable outgoing path that the vehicle is expected to travel in the immediate future at a decision point, wherein the most probable outgoing path is an outgoing path corresponding to a portion of the pre-calculated route;

using one or more of stored digital location-based data, vehicle data and driver data to determine data indicative of a relative probability that each of one or more other outgoing paths at the decision point will be taken by the vehicle;

determining at least one alternative outgoing path that the vehicle will be expected to travel in the immediate future at the decision point based on the data indicative of the relative probability;

generating the horizon using the determined most probable outgoing path and the at least one alternative outgoing path; and

providing data associated with the generated horizon to one or more applications, the data associated with the generated horizon configured to be used by the one or more applications when controlling corresponding vehicle subsystems.

2. The method of claim 1, wherein the digital location-based data is digital map data representative of a plurality of road segments of a road network, the data representative of each road segment being associated with data indicative of one or more road segment attributes, and wherein the method comprises using the attribute data associated with the road segment data to determine the data indicative of the relative probability.

3. The method of claim 2, wherein the attribute data associated with a road segment and used in determining the data indicative of the relative probability includes data indicative of one or more of:

a geometry of the road segment, a gradient of the road segment, an angle of the road segment, a road class of the segment, a speed limit associated with the segment, vehicle flow data indicative of vehicle flow along the segment, and vehicle speed profile data for the road segment.

4. The method of claim 1, wherein the vehicle data is data indicative of one or more of:

vehicle type, vehicle speed, and historical movements of the vehicle, optionally a turn history of the vehicle.

5. The method of claim 1, comprising determining, for each respective possible outgoing path, data indicative of a relative probability that the path will be taken in preference to any other one of the possible outgoing paths at the decision point.

6. The method of claim 1, further comprising storing the determined data indicative of the relative probability that each of the one or more other outgoing paths will be taken, optionally in association with data indicative of the respective outgoing path to which the probability data relates and/or the decision point to which it relates.

7. The method of claim 1, comprising determining relative probability data in respect of every possible outgoing path present at the decision point for a given incoming path.

8. The method of claim 1, comprising determining the data indicative of a relative probability that each of the one or more other outgoing paths will be taken at the decision point using data indicative of one or both of an angle defined between the other outgoing path and an incoming path and road class of the other outgoing path.

9. The method of claim 8, comprising determining that the other outgoing path is relatively more probable when the other outgoing path is associated with a relatively lower angle relative to the incoming path and/or when the other outgoing path is associated with a road class indicative of relatively greater significance.

10. The method of claim 1, comprising determining the relative probability that each of the one or more other outgoing paths from the decision point will be taken based on speed profile data associated with a road segment representing the respective outgoing path.

11. The method of claim 10, wherein said speed profile data is indicative of an average speed of travel for traversing each of the one or more outgoing paths during one or more time periods, and wherein the relative probability that each of the one or more other outgoing paths from the decision point will be taken is determined using the average speed for the time period appropriate for the time at which the horizon is generated.

12. The method of claim 10, wherein a relatively higher probability is associated with an other outgoing path having a speed profile indicative of a relatively higher average speed being associated with the road segment representing the other outgoing path.

13. The method of any of claim 10, comprising determining the relative probability that each of the one or more other outgoing paths will be taken by comparing a current vehicle speed as the vehicle approaches the decision point with average speed data indicated by speed profile data associated with the road segment representing each of the one or more other outgoing paths, optionally wherein a relatively higher probability is associated with an other outgoing path providing a relatively smaller difference in speed compared to the current vehicle speed.

14. The method of claim 1, comprising using data indicative of a historic relative probability that each of the one or more other outgoing paths from the decision point has been taken in respect of the incoming path to determine the relative probability that each of the one or more other outgoing paths at a decision point will be taken by the vehicle.

15. The method of claim 14, further comprising:

providing a probability matrix having, in respect of each of one or more decision points of a road network, data indicative of the relative probability that each of a plurality of possible outgoing paths at the decision point will be taken by a vehicle for each of one or more possible incoming paths, wherein the data indicative of the relative probability that a given possible outgoing path will be taken is based upon positional data relating to the movement of a plurality of devices associated with vehicles with respect to time; and

using the probability matrix in determining the relative probability of each of the one or more other outgoing paths being taken.

16. The method of claim 1, wherein the data indicative of the relative probability that each of the one or more other outgoing paths will be taken is determined based on data indicative of historical paths taken by an individual driver and/or vehicle at the decision point.

17. The method of claim 1, wherein determining the relative probability of each path of the one or more other outgoing paths comprises ranking the one or more other outgoing paths according to the likelihood that the vehicle may be expected to travel along each of the one or more other outgoing paths and/or determining a probability factor in respect of each of the one or more other outgoing paths indicative of the relative probability that the other outgoing path will be taken.

18. The method of claim 1, wherein generating the horizon is carried out by a horizon generating subsystem of the ADAS, and wherein providing the data associated with the generated horizon to the one or more applications comprises the horizon generating subsystem providing the data associated with the generated horizon over a vehicle bus to one or more ADAS applications of the vehicle.

19. The method of claim 18, wherein, when controlling corresponding vehicle subsystems, the one or more ADAS applications use the data provided over the vehicle bus to carry out one or more of:

issuing a speed warning, providing a speed recommendation, and automatically controlling the braking function of the vehicle.

20. A non-transitory computer-readable medium comprising computer readable instructions executable to perform a method according to claim 1.

21. A system for generating a horizon for use in an advanced driver assistance system (ADAS) of a vehicle, the system comprising:

a processor;

a memory coupled to the processor; and

the processor configured to;

obtain data representing a pre-calculated route to be traveled by the vehicle;

determine a most probable outgoing path that the vehicle is expected to travel in the immediate future at a decision point, wherein the most probable outgoing path is an outgoing path corresponding to a portion of the pre-calculated route;

use one or more of stored digital location-based data, vehicle data and driver data to determine data indicative of a relative probability that each of one or more other outgoing paths at the decision point will be taken by the vehicle;

determine at least one alternative outgoing path that the vehicle will be expected to travel in the immediate future at the decision point based on the data indicative of the relative probability;

generate the horizon using the determined most probable outgoing path and the at least one alternative outgoing path; and

provide data associated with the generated horizon to one or more applications, the data associated with the generated horizon configured to be used by the one or more applications when controlling corresponding vehicle subsystems.

22. The system of claim 21, wherein the one or more applications are ADAS applications.