Autonomous vehicle speed calibration

• US 9,817,399 B2
• Filed: 09/22/2016
• Issued: 11/14/2017
• Est. Priority Date: 02/10/2015
• Status: Active Grant

• Current Assignee: MobilEye Vision Technologies Ltd. (Intel Corporation)
• Original Assignee: MobilEye Vision Technologies Ltd. (Intel Corporation)
• Inventors: Braunstein, Daniel, Gdalyahu, Yoram, Rubinsky, Sergey
• Primary Examiner(s): Amin, Bhavesh V
• Application Number:
  US15/273,060
• Publication Number:
  US 20170008521A1
• Time in Patent Office: 418 Days
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2420/408 Radar; Laser, e.g. lidar
• CPC Class Codes: B60W 2552/30 Road curve radius
• CPC Class Codes: B60W 2552/53 Road markings, e.g. lane marker or crosswalk
• CPC Class Codes: B60W 2554/20 Static objects
• CPC Class Codes: B60W 2554/40 Dynamic objects, e.g. animals, windblown objects
• CPC Class Codes: B60W 2554/60 Traversable objects, e.g. speed bumps or curbs
• CPC Class Codes: B60W 2555/60 Traffic rules, e.g. speed limits or right of way
• CPC Class Codes: B60W 2556/35 Data fusion
• CPC Class Codes: B60W 2556/40 High definition maps
• CPC Class Codes: B60W 2556/45 External transmission of data to or from the vehicle
• 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 30/10 Path keeping cruise control for automatically following a preceding vehicle B60W30/165
• CPC Class Codes: B60W 30/14 Adaptive cruise control
• CPC Class Codes: B60W 30/18 Propelling the vehicle
• CPC Class Codes: B60W 40/06 Road conditions
• CPC Class Codes: B60W 60/00 Drive control systems specially adapted for autonomous road vehicles
• CPC Class Codes: B60W 60/0015 : specially adapted for safety
• CPC Class Codes: B60W 60/00274 : considering possible movement changes
• CPC Class Codes: B62D 15/025 : Active steering aids, e.g. helping the driver by actively influencing the steering system after environment evaluation B62D1/28 takes precedence
• CPC Class Codes: G01C 21/14 : by recording the course traversed by the object G01C21/16 takes precedence
• CPC Class Codes: G01C 21/1652 : with ranging devices, e.g. LIDAR or RADAR
• CPC Class Codes: G01C 21/1656 : with passive imaging devices, e.g. cameras
• CPC Class Codes: G01C 21/20 : Instruments for performing navigational calculations G01C21/24, G01C21/26 take precedence
• CPC Class Codes: G01C 21/30 : Map- or contour-matching
• CPC Class Codes: G01C 21/32 : Structuring or formatting of map data
• CPC Class Codes: G01C 21/34 : Route searching; Route guidance
• CPC Class Codes: G01C 21/3407 : specially adapted for specific applications
• CPC Class Codes: G01C 21/3476 : using point of interest [POI] information, e.g. a route passing visible POIs
• CPC Class Codes: G01C 21/36 : Input/output arrangements for on-board computers
• CPC Class Codes: G01C 21/3602 : Input other than that of destination using image analysis, e.g. detection of road signs, lanes, buildings, real preceding vehicles using a camera
• CPC Class Codes: G01C 21/3623 : using a camera or code reader, e.g. for optical or magnetic codes
• CPC Class Codes: G01C 21/3644 : Landmark guidance, e.g. using POIs or conspicuous other objects
• CPC Class Codes: G01C 21/3658 : Lane guidance
• CPC Class Codes: G01C 21/3691 : Retrieval, searching and output of information related to real-time traffic, weather, or environmental conditions arrangements for giving variable traffic instructions G08G1/09
• CPC Class Codes: G01C 21/3811 : Point data, e.g. Point of Interest [POI]
• CPC Class Codes: G01C 21/3819 : Road shape data, e.g. outline of a route
• CPC Class Codes: G01C 21/3822 : Road feature data, e.g. slope data
• CPC Class Codes: G01C 21/3841 : Data obtained from two or more sources, e.g. probe vehicles
• CPC Class Codes: G01C 21/3896 : Transmission of map data from central databases
• CPC Class Codes: G01S 13/867 : Combination of radar systems with cameras
• CPC Class Codes: G01S 19/10 : providing dedicated supplementary positioning signals
• CPC Class Codes: G01S 2013/93271 : in the front of the vehicles
• CPC Class Codes: G01S 5/0027 : of actual mobile position, i.e. position determined on mobile
• CPC Class Codes: G01S 5/16 : using electromagnetic waves other than radio waves
• 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 1/0212 : with means for defining a desired trajectory involving a plurality of land vehicles G05D1/0287
• CPC Class Codes: G05D 1/0219 : ensuring the processing of the whole working surface
• CPC Class Codes: G05D 1/0221 : involving a learning process
• CPC Class Codes: G05D 1/0246 : using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0251 : extracting 3D information from a plurality of images taken from different locations, e.g. stereo vision stereoscopic image analysis H04N13/00; depth recovery from images G06T7/593
• CPC Class Codes: G05D 1/0253 : extracting relative motion information from a plurality of images taken successively, e.g. visual odometry, optical flow determining position or orientation from images G06T7/70
• CPC Class Codes: G05D 1/0278 : using satellite positioning signals, e.g. GPS
• CPC Class Codes: G05D 1/0287 : involving a plurality of land vehicles, e.g. fleet or convoy travelling traffic control systems for road vehicles G08G1/00, particularly anticollision systems G08G1/16
• CPC Class Codes: G06F 16/2379 : Updates performed during online database operations; commit processing
• CPC Class Codes: G06F 16/29 : Geographical information databases
• CPC Class Codes: G06T 2207/20081 : Training; Learning
• CPC Class Codes: G06T 2207/30256 : Lane; Road marking
• CPC Class Codes: G06T 2207/30261 : Obstacle
• CPC Class Codes: G06T 7/00 : Image analysis
• CPC Class Codes: G06V 20/56 : exterior to a vehicle by using sensors mounted on the vehicle
• 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 20/582 : of traffic signs
• CPC Class Codes: G06V 20/584 : of vehicle lights or traffic lights
• 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: G06V 20/63 : Scene text, e.g. street names
• CPC Class Codes: G06V 20/64 : Three-dimensional objects
• 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
• CPC Class Codes: G08G 1/0141 : for traffic information dissemination
• CPC Class Codes: G08G 1/0145 : for active traffic flow control
• 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 1/096725 : where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096805 : where the transmitted instructions are used to compute a route
• CPC Class Codes: G08G 1/167 : Driving aids for lane monitoring, lane changing, e.g. blind spot detection
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04W 4/44 : for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]

Abstract

Systems and methods may calibrate an indicator of speed of an autonomous vehicle. In one implementation, a system may include at least one processor programmed to: receive from a camera at least a plurality of images representative of an environment of the vehicle; analyze the plurality of images to identify at least two recognized landmarks; determine, based on known locations of the two recognized landmarks, a value indicative of a distance between the at least two recognized landmarks; determine, based on an output of at least one sensor associated with the autonomous vehicle, a measured distance between the at least two landmarks; determine a correction factor for the at least one sensor based on a comparison of the value indicative of the distance between the at least to recognized landmarks and the measured distance between the at least two landmarks.

Claims

1. A system for calibrating an output of at least one sensor associated with an autonomous vehicle, the system comprising:

at least one processor programmed to;

receive from a camera a plurality of images representative of an environment of the autonomous vehicle;

analyze the plurality of images to identify at least two recognized landmarks;

determine, based on known locations of the two recognized landmarks, a value indicative of a distance between the at least two recognized landmarks;

determine, based on the output of the at least one sensor associated with the autonomous vehicle, a measured distance between the at least two landmarks;

determine a correction factor for the at least one sensor based on a comparison of the value indicative of the distance between the at least two recognized landmarks and the measured distance between the at least two landmarks; and

apply the correction factor to a value associated with the output of the at least one sensor to obtain a corrected value to be used by the autonomous vehicle.

2. The system of claim 1, wherein the two recognized landmarks include one or more of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a reflector, a landmark beacon, or a lamppost.

3. The system of claim 1, wherein the at least one sensor includes a speedometer associated with the vehicle.

4. The system of claim 1, wherein the known locations of the two recognized landmarks are received from a server based system located remotely with respect to the vehicle.

5. The system of claim 4, wherein each of the known locations constitutes a refined location determined based on a plurality of GPS-based measurements.

18. The system of claim 1, wherein the known locations of the two recognized landmarks are stored in a database.

19. The system of claim 18, wherein the database is included in a remote server.

20. The system of claim 1, wherein a road model stored in a database includes the known locations of the two recognized landmarks.

6. A system for calibrating an output of at least one sensor associated with an autonomous vehicle, the system comprising:

at least one processor programmed to;

determine a distance along a road segment based on the output of the at least one sensor associated with the autonomous vehicle;

receive, via a wireless transceiver from a remote server, a distance value associated with the road segment;

determine a correction factor for the at least one sensor based on the determined distance along the road segment and the distance value received via the wireless transceiver; and

apply the correction factor to a value associated with the output of the at least one sensor to obtain a corrected value to be used by the autonomous vehicle.

7. The system of claim 6, wherein the distance value associated with the road segment, received via the wireless transceiver, is determined based on prior measurements made by a plurality of measuring vehicles.

8. The system of claim 7, wherein the plurality of measuring vehicles includes at least 100 measuring vehicles.

9. The system of claim 7, wherein the plurality of measuring vehicles includes at least 1000 measuring vehicles.

10. The system of claim 6, wherein the correction factor is determined such that an operation on the determined distance along the road segment by the correction factor matches the distance value received via the wireless transceiver.

11. The system of claim 6, wherein the at least one processor is programmed to determine a composite correction factor based on a plurality of determined correction factors.

12. The system of claim 11, wherein the composite correction factor is determined by averaging the plurality of determined correction factors.

13. The system of claim 11, wherein the composite correction factor is determined by finding a mean of the plurality of determined correction factors.

14. An autonomous vehicle comprising:

a body;

a camera; and

at least one processor programmed to;

receive from the camera a plurality of images representative of an environment of the autonomous vehicle;

analyze the plurality of images to identify at least two recognized landmarks;

determine, based on known locations of the two recognized landmarks, a value indicative of a distance between the at least two recognized landmarks;

determine, based on an output of at least one sensor associated with the autonomous vehicle, a measured distance between the at least two landmarks;

determine a correction factor for the at least one sensor based on a comparison of the value indicative of the distance between the at least two recognized landmarks and the measured distance between the at least two landmarks; and

apply the correction factor to a value associated with the output of the at least one sensor to obtain a corrected value to be used by the autonomous vehicle.

15. The autonomous vehicle of claim 14, wherein the at least one sensor includes a speedometer associated with the autonomous vehicle.

16. The autonomous vehicle of claim 14, wherein the two recognized landmarks include one or more of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a reflector, a landmark beacon, or a lamppost.

17. The autonomous vehicle of claim 14, wherein the known locations of the two recognized landmarks are received from a server based system located remotely with respect to the autonomous vehicle.