Systems and Methods for Precise Sub-Lane Vehicle Positioning

• US 20120150437A1
• Filed: 12/13/2010
• Published: 06/14/2012
• 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
• Granted Patent: US 8,452,535 B2
• Time in Patent Office: Days
• US Class Current: 701/456
• 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, the method comprising:

the on-board computer of the vehicle receiving new data from at least one of the vehicle sensors;

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

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

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

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;

the on-board computer of the vehicle performing a tracking sub-routine including using a probability distribution to update the vehicle pose in terms of data particles; and

the on-board computer of the vehicle performing a particle filtering sub-routine based on the data particles to compute the new vehicle pose.

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

(i) obtaining vehicle'"'"'s GPS coordinates;

(ii) identifying the location of the vehicle within the lane-level map;

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

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

(iii) identifying a closest point on the curve from the vehicle.

3. The method of claim 1, wherein the sub-routine includes:

re-sampling the data particles to identify applicable particles of the data particles; and

computing the new vehicle pose as a mean of the applicable particles.

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 includes:

(1) updating a vision observation model;

(2) updating a radar observation model;

(3) updating an SPS observation model; and

(4) updating a map-attribute-matching observation model.

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

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,

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 computed propagation;

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

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

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;

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

if it is determined that there is the present lane-change maneuver, computing 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 includes at least one of:

evaluating an angular offset of a centerline of the vehicle a lane centerline,

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

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

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

the arc length is discerned using SPS 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 includes:

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

considering whether a lane-change maneuver is present;

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

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

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

if it is determined that either the lane-split or lanes-merging condition is present, reassigning the lane index 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 includes predicting as the state variables e, n, □

, 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 includes 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 including;

a processor; and

a memory comprising;

a lane-level map; and

instructions that, when executed by the processor, cause the processor to;

receive new data from at least one of the vehicle sensors;

collect measurements from the vehicle sensors;

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

perform a curve-fitting process;

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

perform a tracking sub-routine including using a probability distribution to update the vehicle pose in terms of data particles; and

perform a particle filtering sub-routine based on the data particles to compute the new vehicle pose.

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 includes:

(1) updating a vision observation model;

(2) updating a radar observation model;

(3) updating an SPS observation model; and

(4) updating a map-attribute-matching observation model.

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

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;

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

if it is determined that there is the present lane-change maneuver, computing 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 includes:

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 SPS 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 includes:

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

considering whether a lane-change maneuver is present;

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

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

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

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

19. A tangible, non-transitory computer-readable medium for use in a transportation vehicle and storing instructions, that, when executed by a processor, cause the processor to:

receive new data from at least one of the vehicle sensors;

collect measurements from the vehicle sensors;

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

perform a curve-fitting process;

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

perform a tracking sub-routine including using a probability distribution to update the vehicle pose in terms of data particles; and

perform a particle filtering sub-routine based on the data particles to compute the new vehicle pose.

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

(1) updating a vision observation model;

(2) updating a radar observation model;

(3) updating an SPS observation model; and

(4) updating a map-attribute-matching observation model.