Vehicle localization with map-matched sensor measurements

• US 10,585,409 B2
• Filed: 01/30/2017
• Issued: 03/10/2020
• Est. Priority Date: 09/08/2016
• Status: Active Grant

• Current Assignee: Siemens Electronic Design Automation GmbH
• Original Assignee: Mentor Graphics Corporation (Siemens AG)
• Inventors: Mercep, Ljubo, Pollach, Matthias
• Primary Examiner(s): Black, Thomas G
• Assistant Examiner(s): Lewandroski, Sara J
• Application Number:
  US15/419,922
• Publication Number:
  US 20180066954A1
• Time in Patent Office: 1,135 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 2420/54 Audio sensitive means, e.g. ultrasound
• CPC Class Codes: B60W 2554/20 Static objects
• CPC Class Codes: B60W 2554/4026 Cycles
• CPC Class Codes: B60W 2554/4029 Pedestrians
• CPC Class Codes: B60W 2554/4041 Position
• CPC Class Codes: B60W 2556/20 Data confidence level
• CPC Class Codes: B60W 2556/35 Data fusion
• CPC Class Codes: B60W 60/0027 using trajectory prediction for other traffic participants
• CPC Class Codes: G01C 21/28 with correlation of data from several navigational instruments
• CPC Class Codes: G01C 21/3407 specially adapted for specific applications
• CPC Class Codes: G01C 21/3415 Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
• 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: G01S 13/723 by using numerical data
• CPC Class Codes: G01S 13/862 Combination of radar systems with sonar systems
• CPC Class Codes: G01S 13/865 Combination of radar systems with lidar systems
• CPC Class Codes: G01S 13/867 Combination of radar systems with cameras
• CPC Class Codes: G01S 13/931 of land vehicles
• CPC Class Codes: G01S 15/931 of land vehicles
• CPC Class Codes: G01S 17/931 : of land vehicles
• CPC Class Codes: G01S 2013/93271 : in the front of the vehicles
• CPC Class Codes: G01S 2013/93272 : in the back of the vehicles
• CPC Class Codes: G01S 2013/93274 : on the side of the vehicles
• CPC Class Codes: G01S 7/4808 : Evaluating distance, position or velocity data
• CPC Class Codes: G05B 17/02 : electric
• 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 1/024 : in combination with a laser lasers per se H01S
• CPC Class Codes: G05D 1/0246 : using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0255 : using acoustic signals, e.g. ultra-sonic singals sonar systems designed for anti-collision purposes G01S15/93
• CPC Class Codes: G05D 1/0257 : using a radar radar systems designed for anti-collision purposes between land vehicles or between land vehicle and fixed obstacles G01S13/931
• CPC Class Codes: G05D 1/0259 : using magnetic or electromagnetic means
• CPC Class Codes: G05D 1/0268 : using internal positioning means
• CPC Class Codes: G05D 1/0276 : using signals provided by a source external to the vehicle involving a plurality of vehicles G05D1/0287; automatically controlling vehicle speed responsive to externally generated signals B60K31/0058
• CPC Class Codes: G05D 1/0278 : using satellite positioning signals, e.g. GPS
• CPC Class Codes: G06F 16/29 : Geographical information databases
• CPC Class Codes: G06F 16/5854 : using shape and object relationship
• CPC Class Codes: G06F 30/20 : Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06V 20/56 : exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G08G 1/161 : Decentralised systems, e.g. inter-vehicle communication
• CPC Class Codes: G08G 1/163 : involving continuous checking
• CPC Class Codes: G08G 1/164 : Centralised systems, e.g. external to vehicles

Abstract

This application discloses a computing system to implement vehicle localization in an assisted or automated driving system. The computing system can receive an environmental model populated with measurement data captured by sensors mounted in a vehicle. The computing system can detect a location of the vehicle relative to the map data based on a correlation between the measurement data and the map data. The computing system can detect landmarks in the map data and switch to sparsely-populated map data from higher-definition map data for subsequent location detections. When the computing system does not detect a vehicle location, the computing system can track movement of the vehicle based on subsequent measurement data in the environmental model. After reacquiring a vehicle location, the computing can realign the tracked movement of the vehicle and measured data to the map data or modify the map data to include the tracked data.

Claims

1. A method comprising:

when a vehicle is without an external reference to a global coordinate field, comparing, by a computing system, map data having the global coordinate field to at least a portion of an environmental model having an environmental coordinate field populated with measurement data captured by sensors mounted in the vehicle, wherein the measurement data is temporally aligned and spatially aligned in the environmental model;

when the comparison does not identify a correlation between the measurement data in the environmental model and the map data, tracking, by the computing system, movement of the vehicle based, at least in part, on subsequent measurement data in the environmental model;

detecting, by the computing system, a location of the vehicle relative to the map data based on a subsequent comparison of the map data to the subsequent measurement data in the environmental model; and

when the subsequent comparison identifies a correlation between the subsequent measurement data in the environmental model and the map data, aligning, by the computing system, the tracked movement of the vehicle and the subsequent measurement data to the map data based, at least in part, on the detected location of the vehicle relative to the map data.

2. The method of claim 1, further comprising when the comparison identifies the correlation between the measurement data in the environmental model and the map data, detecting, by the computing system, landmarks in the map data based, at least in part, on the correlation between the measurement data in the environmental model and the map data.

3. The method of claim 2, wherein the detected landmarks in the map data correspond to static objects having characteristics conducive to being measured by one or more of the sensors.

4. The method of claim 2, further comprising switching, by the computing system, to sparsely-populated map data from higher-definition map data for subsequent comparisons with the environmental model based on the detection of the landmarks.

5. The method of claim 1, further comprising associating, by the computing system, the tracked movement of the vehicle and the subsequent measurement data to the map data based, at least in part, on a previously detected location of the vehicle relative to the map data.

6. The method of claim 1, further comprising modifying, by the computing system, the map data to include the subsequent measurement data utilized to track the movement of the vehicle.

7. An apparatus comprising at least one memory device storing instructions configured to cause one or more processing devices to perform operations comprising:

when a vehicle is without an external reference to a global coordinate field, comparing map data having the global coordinate field to at least a portion of an environmental model having an environmental coordinate field populated with measurement data captured by sensors mounted in the vehicle, wherein the measurement data is temporally aligned and spatially aligned in the environmental model;

when the comparison does not identify a correlation between the measurement data in the environmental model and the map data, tracking movement of the vehicle based, at least in part, on subsequent measurement data in the environmental model;

detecting a location of the vehicle relative to the map data based on a subsequent comparison of the map data to the subsequent measurement data in the environmental model; and

when the subsequent comparison identifies a correlation between the subsequent measurement data in the environmental model and the map data, aligning the tracked movement of the vehicle and the subsequent measurement data to the map data based, at least in part, on the detected location of the vehicle relative to the map data.

8. The apparatus of claim 7, wherein the instructions are further configured to cause the one or more processing devices to perform operations comprising modifying the map data to include the subsequent measurement data utilized to track the movement of the vehicle.

9. The apparatus of claim 7, wherein the instructions are further configured to cause the one or more processing devices to perform operations comprising, when the comparison identifies the correlation between the measurement data in the environmental model and the map data, detecting landmarks in the map data based, at least in part, on the correlation between the measurement data in the environmental model and the map data.

10. The apparatus of claim 9, wherein the detected landmarks in the map data correspond to static objects having characteristics conducive to being measured by one or more of the sensors.

11. The apparatus of claim 9, wherein the instructions are further configured to cause the one or more processing devices to perform operations comprising switching to sparsely-populated map data from higher-definition map data for subsequent comparisons with the environmental model based on the detection of the landmarks.

12. The apparatus of claim 7, wherein the instructions are further configured to cause the one or more processing devices to perform operations comprising associating the tracked movement of the vehicle and the subsequent measurement data to the map data based, at least in part, on a previously detected location of the vehicle relative to the map data.

13. A system comprising:

a memory device configured to store machine-readable instructions; and

a computing system including one or more processing devices, in response to executing the machine-readable instructions, configured to;

when a vehicle is without an external reference to a global coordinate field, compare map data having the global coordinate field to at least a portion of an environmental model having an environmental coordinate field populated with measurement data captured by sensors mounted in the vehicle, wherein the measurement data is temporally aligned and spatially aligned in the environmental model;

when the comparison does not identify a correlation between the measurement data in the environmental model and the map data, track movement of the vehicle based, at least in part, on subsequent measurement data in the environmental model;

detect a location of the vehicle relative to the map data based on a subsequent comparison of the map data to the subsequent measurement data in the environmental model; and

when the subsequent comparison identifies a correlation between the subsequent measurement data in the environmental model and the map data, align the tracked movement of the vehicle and the subsequent measurement data to the map data based, at least in part, on the detected location of the vehicle relative to the map data.

14. The system of claim 13, wherein the one or more processing devices, in response to executing the machine-readable instructions, are configured to associate the tracked movement of the vehicle and the subsequent measurement data to the map data based, at least in part, on a previously detected location of the vehicle relative to the map data.

15. The system of claim 13, wherein the one or more processing devices, in response to executing the machine-readable instructions, are configured to modify the map data to include the subsequent measurement data utilized to track the movement of the vehicle.

16. The system of claim 13, wherein the one or more processing devices, in response to executing the machine-readable instructions, are configured to, when the comparison identifies the correlation between the measurement data in the environmental model and the map data, detect landmarks in the map data based, at least in part, on the correlation between the measurement data in the environmental model and the map data, wherein the detected landmarks in the map data correspond to static objects having characteristics conducive to being measured by one or more of the sensors.

17. The system of claim 16, wherein the one or more processing devices, in response to executing the machine-readable instructions, are configured to switch to sparsely-populated map data from higher-definition map data for subsequent comparisons with the environmental model based on the detection of the landmarks.