SYSTEMS AND METHODS FOR NAVIGATING LANE MERGES AND LANE SPLITS

• US 20180120859A1
• Filed: 10/31/2017
• Published: 05/03/2018
• Est. Priority Date: 10/31/2016
• Status: Active Grant

• Current Assignee: MobilEye Vision Technologies Ltd. (Intel Corporation)
• Original Assignee: MobilEye Vision Technologies Ltd. (Intel Corporation)
• Inventors: Eagelberg, Dor, Stein, Gideon, Taieb, Yoav, Gdalyahu, Yoram, Fridman, Ofer
• Granted Patent: US 10,739,782 B2
• Time in Patent Office: Days
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2552/53 Road markings, e.g. lane marker or crosswalk
• CPC Class Codes: B60W 2554/4041 Position
• CPC Class Codes: B60W 2554/804 Relative longitudinal speed
• CPC Class Codes: B60W 30/09 Taking automatic action to avoid collision, e.g. braking and steering
• CPC Class Codes: B60W 30/18163 Lane change; Overtaking manoeuvres
• CPC Class Codes: B60W 60/0027 using trajectory prediction for other traffic participants
• CPC Class Codes: B60W 60/00274 considering possible movement changes
• CPC Class Codes: G01C 21/34 Route searching; Route guidance
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0246 using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/249 from positioning sensors located off-board the vehicle, e.g. from cameras
• CPC Class Codes: G05D 1/43 Control of position or course in two dimensions
• CPC Class Codes: G05D 1/81 Handing over between on-board automatic and on-board manual control
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06V 20/58 Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
• CPC Class Codes: G06V 20/588 Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
• CPC Class Codes: G08G 1/09623 Systems involving the acquisition of information from passive traffic signs by means mounted on the vehicle G08G1/0967 takes precedence
• CPC Class Codes: G08G 1/165 for passive traffic, e.g. including static obstacles, trees
• CPC Class Codes: G08G 1/166 for active traffic, e.g. moving vehicles, pedestrians, bikes
• CPC Class Codes: G08G 1/167 : Driving aids for lane monitoring, lane changing, e.g. blind spot detection

Abstract

Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

Claims

1. A system for navigating a host vehicle, the system comprising:

at least one processing device programmed to;

receive, from an image capture device, a plurality of images representative of an environment of the host vehicle, the environment including a road on which the host vehicle is traveling;

analyze at least one of the plurality of images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling;

analyze at least one of the plurality of images to identify at least one lane mark associated with the lane in which the target vehicle is traveling;

detect one or more lane mark characteristics of the at least one identified lane mark;

use the one or more detected lane mark characteristics to determine a type of the at least one identified lane mark;

determine at least one characteristic of the target vehicle; and

determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

2. The system of claim 1, wherein the determined lane mark type includes a merge lane.

3. The system of claim 1, wherein the one or more detected lane mark characteristics includes a distance of the at least one identified lane mark to a reference point.

4. The system of claim 3, wherein the reference point includes at least one other lane mark of the road.

5. The system of claim 1, wherein the one or more detected lane mark characteristics includes an intersection of the at least one identified lane mark with at least one other lane mark of the road.

6. The system of claim 1, wherein the one or more detected lane mark characteristics includes a change in lateral distances between the at least one identified lane mark and at least one other lane mark of the road.

7. The system of claim 1, wherein determining at least one characteristic of the target vehicle includes analyzing at least one of the plurality of images.

8. The system of claim 1, wherein the at least one determined characteristic of the target vehicle includes a position of the target vehicle on the road forward of the host vehicle.

9. The system of claim 1, wherein the at least one determined characteristic of the target vehicle includes a detected lateral motion of the target vehicle relative to the at least one identified lane mark.

10. The system of claim 1, wherein the at least one determined characteristic of the target vehicle includes a velocity of the target vehicle relative to the host vehicle.

11. The system of claim 1, wherein the at least one determined characteristic of the target vehicle includes a predicted trajectory of the target vehicle.

12. The system of claim 1, wherein the determined navigation action for the host vehicle includes changing a rate of acceleration.

13. The system of claim 1, wherein the determined navigation action for the host vehicle includes a steering action of the host vehicle.

14. The system of claim 1, wherein the at least one processing device is further programmed to predict a navigational action of the target vehicle based on the determined lane mark type.

15. The system of claim 14, wherein the predicted navigational action of the target vehicle includes a lane merge.

16. A system for navigating a host vehicle, the system comprising:

at least one processing device programmed to;

receive, from an image capture device, a plurality of images representative of an environment of the host vehicle, the environment including a road on which the host vehicle is traveling;

analyze at least one of the plurality of images to identify a target vehicle traveling in a lane of the road in which the host vehicle is traveling;

analyze at least one of the plurality of images to identify at least one lane mark associated with the lane;

detect one or more characteristics of the at least one identified lane mark;

use the one or more detected lane mark characteristics to determine a type of the at least one identified lane mark;

determine at least one characteristic of the target vehicle; and

determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

17. The system of claim 16, wherein the determined lane mark type includes a split lane.

18. The system of claim 16, wherein the one or more detected lane mark characteristics includes a distance of the at least one identified lane mark to a reference point.

19. The system of claim 18, wherein the reference point includes at least one other lane mark of the road.

20. The system of claim 16, wherein the one or more detected lane mark characteristics includes a split of the at least identified lane mark into another lane mark.

21. The system of claim 16, wherein the one or more detected lane mark characteristics includes a change in lateral distances between the at least one identified lane mark and at least one other lane mark of the road.

22. The system of claim 16, wherein determining at least one characteristic of the target vehicle includes analyzing at least two of the plurality of images.

23. The system of claim 16, wherein the at least one determined characteristic of the target vehicle includes a lateral motion of the target vehicle relative to the at least one identified lane mark.

24. The system claim 16, wherein the determined navigational action for the host vehicle includes maintaining or increasing a rate of acceleration.

25. The system of claim 16, wherein the at least one processing device is further programmed to predict a navigational action of the target vehicle based on the determined lane mark type.

26. The system of claim 25, wherein the predicted navigational action of the target vehicle includes a lane change or exiting the road.

27. A method for navigating a host vehicle, the method comprising:

receiving, from an image capture device, a plurality of images representative of an environment of the host vehicle, the environment including a road on which the host vehicle is traveling;

analyzing, by at least one processing device, at least one of the plurality of images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling;

analyzing at least one of the plurality of images to identify at least one lane mark associated with the lane in which the target vehicle is traveling;

detecting one or more lane mark characteristics of the at least one identified lane mark;

use the one or more detected lane mark characteristics to determine a type of the at least one identified lane mark;

determining at least one characteristic of the target vehicle; and

determining a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

28. A method for navigating a host vehicle, the method comprising:

receiving, from an image capture device, a plurality of images representative of an environment of the host vehicle, the environment including a road on which the host vehicle is traveling;

analyzing, by at least one processing device, at least one of the plurality of images to identify a target vehicle traveling in a lane of the road in which the host vehicle is traveling;

analyzing at least one of the plurality of images to identify at least one lane mark associated with the lane;

detecting one or more characteristics of the at least one identified lane mark;

use the one or more detected lane mark characteristics to determine a type of the at least one identified lane mark;

determining at least one characteristic of the target vehicle; and

determining a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.