Systems and methods for precise sub-lane vehicle positioning

• US 8,452,535 B2
• Filed: 12/13/2010
• Issued: 05/28/2013
• Est. Priority Date: 12/13/2010
• Status: Active Grant

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations LLC (General Motors Company)
• Inventors: Zeng, Shuqing, Sadekar, Varsha
• Primary Examiner(s): Black, Thomas
• Assistant Examiner(s): PAIGE, TYLER D
• Application Number:
  US12/966,032
• Publication Number:
  US 20120150437A1
• Time in Patent Office: 897 Days
• Field of Search: 701/400, 701/408, 701/409, 701/410, 701/411, 701/412, 701/416, 701/422, 701/428, 701/439
• US Class Current: 701/466
• CPC Class Codes: B60W 2520/14 Yaw
• CPC Class Codes: B60W 2552/30 Road curve radius
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 30/12 Lane keeping
• CPC Class Codes: G01C 21/30 Map- or contour-matching
• CPC Class Codes: G01S 13/931 of land vehicles
• CPC Class Codes: G01S 19/14 specially adapted for specific applications
• CPC Class Codes: G01S 19/42 Determining position
• CPC Class Codes: G01S 19/48 by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
• CPC Class Codes: G01S 19/50 whereby the position solution is constrained to lie upon a particular curve or surface, e.g. for locomotives on railway tracks
• CPC Class Codes: G01S 2013/932 using own vehicle data, e.g. ground speed, steering wheel direction
• CPC Class Codes: G01S 2013/9323 Alternative operation using light waves

Abstract

A vehicle having an on-board computer, vehicle sensors, a satellite-positioning unit, a database storing a lane-level map performs a method to determine a new pose of the vehicle using map matching. The method includes the on-board computer of the vehicle receiving new data from at least one of the vehicle sensors and collecting measurements from the vehicle sensors. The method also includes the on-board computer of the vehicle computing propagation of vehicle pose with respect to consecutive time instances and performing a curve-fitting process. The method further includes the on-board computer of the vehicle performing a sub-routine of updating at least one observation model based on results of the curve-fitting process, performing a tracking sub-routine including using a probability distribution to update the vehicle pose in terms of data particles, and performing a particle filtering sub-routine based on the data particles to compute the new vehicle pose.

Claims

1. A method, performed by a vehicle having an on-board computer, vehicle sensors, a satellite-positioning unit, a database storing a lane-level map for determining a new pose of the vehicle using map matching, comprising:

collecting, by the on-board computer of the vehicle, measurements from the vehicle sensors;

computing, by the on-board computer of the vehicle, propagation of vehicle pose with respect to consecutive time instances;

performing, by the on-board computer of the vehicle, a curve-fitting process;

performing, by the on-board computer of the vehicle, a sub-routine of updating at least one observation model based on results of the curve-fitting process;

performing, by the on-board computer of the vehicle, a tracking sub-routine to update the vehicle pose in terms of data particles; and

performing, by the on-board computer of the vehicle, a particle-filtering sub-routine based on the data particles to compute the new vehicle pose, the particle-filtering sub-routine employing a sampling technique for identifying, from amongst the data particles being of a first distribution, more-applicable data particles and less-relevant particles.

2. The method of claim 1, wherein the curve-fitting process comprises:

obtaining global-positioning coordinates indicating a position of the vehicle;

identifying the location of the vehicle within the lane-level map;

defining a current segment of a present road for the vehicle, wherein the road segment is represented by waypoints identifying aspects of the road;

computing a curvature profile of a cubic curve fitted from the waypoints; and

identifying a closest point on the curve from the vehicle.

3. The method of claim 1, wherein the sampling technique comprises:

sampling, using data points associated with a different distribution, the data particles of the first distribution to identify the applicable particles and in so doing identify the less-relevant particles; and

computing the new vehicle pose as a mean of the applicable particles identified, wherein the less-relevant particles are considered less than the applicable particles or not at all.

4. The method of claim 1, wherein the sub-routine of updating at least one observation model based on results of the curve-fitting process comprises:

generating vision observation model;

generating a radar observation model;

generating a global-positioning observation model;

generating a map-attribute-matching observation model;

updating the vision observation model subsequent to generating the vision observation model;

updating the radar observation model subsequent to generating the radar observation model;

updating the global-positioning observation model subsequent to generating the global-positioning observation model; and

updating the map-attribute-matching observation model subsequent to generating the map-attribute observation model.

5. The method of claim 4, wherein the sub-routine of updating the vision model comprises:

retrieving data from the lane-level map identifying at least one registered lane line near the vehicle;

computing a position of the lane line with respect to a reference frame of the host vehicle;

matching the detected and registered lane stripes and boundaries using a distance analysis, yielding matched data; and

computing an observation probability distribution with respect to vision data, from a camera of the vehicle sensors, using the matched data.

6. The method of claim 5, wherein:

retrieving data from the lane-level map identifying registered lane stripes near the vehicle is performed using the propagation computed;

the identified lane stripes are those determined to be within a predetermined threshold distance of a position of the vehicle;

computing the position of the lane line with respect to the reference frame of the host vehicle comprises projecting the lane stripes procured from a coordinate system of the lane-level map onto the reference frame of the vehicle; and

matching the detected and registered lane stripes and boundaries comprises using a Euclidean-type analysis of distance as the distance analysis.

7. The method of claim 4, wherein the sub-routine of updating the radar model comprises:

reading radar data from a range sensor of the vehicle sensors;

deleting static radar objects from data objects of the radar data;

determining whether there is a present lane-change maneuver for the vehicle;

assigning, if it is determined that there is not the present lane-change maneuver, a lane to each detected moving radar object based on a lateral offset from a centerline of a lane of the host vehicle; and

computing, if it is determined that there is the present lane-change maneuver, a vision observation model with respect to the radar data.

8. The method of claim 7, wherein determining whether there is the present lane-change maneuver for the vehicle comprises at least one act selected from a group consisting of:

evaluating an angular offset between a centerline of the vehicle and a lane centerline; and

evaluating a lateral offset between the vehicle and one or more lane boundaries and/or the lane centerline.

9. The method of claim 4, wherein the sub-routine of updating the map-attribute-matching observation model comprises:

determining yaw rate and speed from data of the vehicle sensors;

determining whether an overhead structure is present;

if the overhead structure is determined present;

determining a position of the overhead structure based on data, of the lane-level map, identifying the structure;

detecting the overhead structure using global-positioning data; and

computing an observation probability distribution associated with the map with respect to the overhead object;

determining whether a lane-change maneuver is present;

if it is determined that the lane-change maneuver is present;

computing a lane curvature;

computing a vehicle-path curvature; and

computing an observation probability distribution for curvature match based on the computed vehicle-path curvature; and

computing a combined probably distribution with respect to the lane-level map, including considering the probability distributions computed.

10. The method of claim 9, wherein:

computing the lane curvature comprises computing the lane curvature as a function of an arc length of the curve;

the arc length is discerned using global-positioning data identifying waypoints on the curve; and

computing the vehicle-path curvature is performed using the lane curvature.

11. The method of claim 1, wherein computing propagation of vehicle pose with respect to consecutive time instances comprises:

predicting state variables for the vehicle according to a dead-reckoning function;

considering whether a lane-change maneuver is present;

maintaining, if the lane-change maneuver is not present, a previously-assigned lane-index value;

increasing the previously-assigned lane-index value by one in response to a left lane change or decreasing the previously-assigned lane-index value by one in response to a right lane change;

considering whether there is a lane-split or lanes-merging condition; and

reassigning, if it is determined that either the lane-split or lanes-merging condition is present, the lane-index value for the vehicle based on information about the lane-split or lanes-merging condition.

12. The method of claim 11, wherein predicting the state variables comprises predicting as the state variables e, n, Θ

, and v;

s(t+1)=f(s(t),u_t)

where f denotes the dead-reckoning function using a previous state vector s(t) and a control input u_t (e.g., yaw rate and velocity) from sensors of the vehicle sensors.

13. The method of claim 11, wherein the dead-reckoning function comprises a constant velocity (CV) vehicle kinematic model and a constant turning (CT) vehicle kinematic model.

14. A transportation vehicle configured to determine a new pose of the vehicle using map matching, the vehicle comprising:

multiple vehicle sensors;

a satellite-positioning unit; and

an on-board computer comprising;

a processor; and

a memory comprising;

a lane-level map; and

computer-executable instructions that, when executed by the processor, cause the processor to perform operations comprising;

collecting measurements from the vehicle sensors;

computing compute propagation of vehicle pose with respect to consecutive time instances;

performing a curve-fitting process;

performing a sub-routine of updating at least one observation model based on results of the curve-fitting process;

performing a tracking sub-routine to update the vehicle pose in terms of data particles; and

performing a particle filtering sub-routine based on the data particles to compute the new vehicle pose, the particle-filtering sub-routine employing a sampling technique for identifying, from amongst the data particles being of a first distribution, more-applicable data particles and less-relevant particles.

15. The vehicle of claim 14, wherein the sub-routine of updating at least one observation model based on results of the curve-fitting process comprises:

generating vision observation model;

generating a radar observation model;

generating a global-positioning observation model;

generating a map-attribute-matching observation model;

updating the vision observation model subsequent to generating the vision observation model;

updating the radar observation model subsequent to generating the radar observation model;

updating, the global-positioning observation model subsequent to generating the global-positioning observation model; and

updating, the map-attribute-matching observation model subsequent to generating the map-attribute observation model.

16. The vehicle of claim 15, wherein the sub-routine of updating the radar model includes comprises:

reading radar data from a range sensor of the vehicle sensors;

deleting static radar objects from data objects of the radar data;

determining whether there is a present lane-change maneuver for the vehicle;

assigning, if it is determined that there is not the present lane-change maneuver, a lane to each detected moving radar object based on a lateral offset from a centerline of a lane of the host vehicle; and

computing, if it is determined that there is the present lane-change maneuver, a vision observation model with respect to the radar data.

17. The vehicle of claim 15, wherein the sub-routine of updating the map-attribute-matching observation model comprises:

determining yaw rate and speed from data of the vehicle sensors;

determining whether an overhead structure is present;

if the overhead structure is determined present;

determining a position of the overhead structure based on data, of the lane-level map, identifying the structure;

detecting the overhead structure using global-positioning data; and

computing an observation probability distribution associated with the map with respect to the overhead object;

determining whether a lane-change maneuver is present;

if it is determined that the lane-change maneuver is present;

computing a lane curvature;

computing a vehicle-path curvature; and

computing an observation probability distribution for curvature match based on the computed vehicle-path curvature; and

computing a combined probably distribution with respect to the lane-level map, including considering the probability distributions computed.

18. The vehicle of claim 14, wherein computing propagation of vehicle pose with respect to consecutive time instances comprises:

predicting state variables for the vehicle according to a dead-reckoning function;

considering whether a lane-change maneuver is present;

maintaining, if the lane-change maneuver is not present, a previously-assigned lane-index value;

increasing the previously-assigned lane-index value by one in response to a left lane change or decreasing the previously-assigned lane-index value by one in response to a right lane change;

considering whether there is a lane-split or lanes-merging condition; and

reassigning, if it is determined that either the lane-split or lanes-merging condition is present, the lane-index value for the vehicle based on information about the lane-split or lanes-merging condition.

19. A tangible computer-readable storage device, for use in a transportation vehicle, comprising computer-executable instructions that, when executed by a processor, cause the processor to perform operations comprising:

collecting measurements from the vehicle sensors;

computing propagation of vehicle pose with respect to consecutive time instances;

performing a curve-fitting process;

performing a sub-routine of updating at least one observation model based on results of the curve-fitting process;

performing, a tracking sub-routine to update the vehicle pose in terms of data particles; and

performing, a particle-filtering sub-routine based on the data particles to compute the new vehicle pose, the particle-filtering sub-routine employing a sampling technique for identifying, from amongst the data particles being of a first distribution, more-applicable data particles and less-relevant particles.

20. The tangible computer-readable storage device of claim 19, wherein the sub-routine of updating at least one observation model based on results of the curve-fitting process comprises:

generating vision observation model;

generating a radar observation model;

generating a global-positioning observation model;

generating a map-attribute-matching observation model;

updating the vision observation model subsequent to generating the vision observation model;

updating, the radar observation model subsequent to generating the radar observation model;

updating, the global-positioning an SPS observation model subsequent to generating the global-positioning observation model; and

updating the map-attribute-matching observation model subsequent to generating the map-attribute observation model.