Lane marking localization and fusion
Various embodiments of the present disclosure provide a system and method for iterative lane marking localization that may be utilized by autonomous or semi-autonomous vehicles traveling within the lane. In an embodiment, the system comprises a locating device adapted to determine the vehicle's geographic location; a database; a region map; a response map; a plurality of cameras; and a computer connected to the locating device, database, and cameras, wherein the computer is adapted to receive the region map, wherein the region map corresponds to a specified geographic location; generate the response map by receiving information from the camera, the information relating to the environment in which the vehicle is located; identifying lane markers observed by the camera; and plotting identified lane markers on the response map; compare the response map to the region map; and iteratively generate a predicted vehicle location based on the comparison of the response map and the region map.
1. A method for determining a vehicle's location, comprising:
approximating the vehicle's region;
receiving a first region map from a database, wherein the first region map corresponds to the vehicle's approximated region and comprises a plurality of first region points indicating an expected roadway lane;
receiving a second region map from the database, wherein the second region map corresponds to the vehicle's approximated region and comprises a plurality of second region points indicating the expected roadway lane;
receiving a first response image generated by a first imaging device, the first response image comprising information relating to the vehicle's environment;
receiving a second response image generated by a second imaging device, the second response image comprising information relating to the vehicle's environment;
generating a first response map from the first response image, the first response map comprising a plurality of first response points indicating the vehicle's location;
generating a second response map from the second response image, the second response map comprising a plurality of second response points indicating the vehicle's location;
generating a first corrected location based on comparing the first response map to the first region map;
generating a second corrected location based on comparing the second response map to the second region map; and
predicting the vehicle's location based on the first corrected location and the second corrected location.
2. The method of claim 1 , further comprising:
receiving a third region map from the database, wherein the third region map corresponds to the vehicle's approximated region and comprises a plurality of third region points indicating the expected roadway lane;
receiving a third response image generated by a third imaging device, the third response image comprising information relating to the vehicle's environment;
generating a third response map from the third response image, the third response map comprising a plurality of third response points indicating the vehicle's location; and
generating a third corrected location based on comparing the third response map to the third region map,
wherein predicting the vehicle's location is further based on the third corrected location.
3. The method of claim 1 , wherein the first region map comprises information from a previously generated vehicle location.
4. The method of claim 1 , wherein the vehicle's region is approximated using a Global Positioning System (GPS) device or an inertial measurement unit (IMU).
5. The method of claim 1 , wherein generating the first response map comprises:
detecting lane markers in the first response image, the lane markers pertaining to physical aspects contained in the first response image;
plotting the plurality of first response points on the first response map, the plurality of first response points indicating locations of the lane markers.
6. The method of claim 1 , further comprising:
generating a first confidence score based on comparing the first response map to the first region map.
7. The method of claim 1 , wherein the response image is generated from radar sensing equipment, light detection and ranging (LIDAR) sensing equipment, Global Positioning System (GPS) sensing information, and/or images.
8. The method of claim 1 , wherein the first region map and the first response map are compared at a selected frequency.
9. The method of claim 1 , further comprising:
outputting the vehicle's predicted location to an advanced driver-assistance system (ADAS).
10. The method of claim 2 , wherein the first imaging device, the second imaging device, and the third imaging device are each adapted to perceive different aspects of the vehicle's environment.
11. A system for iteratively determining a vehicle's location on a roadway, comprising:
a processor configured to:
receive, from a locating device adapted to determine a vehicle's geographic region, the vehicle's predicted geographic region;
receive, from a database, a first region map comprising a plurality of first region points corresponding to the vehicle's predicted geographic region;
receive, from the database, a second region map comprising a plurality of second region points corresponding to the vehicle's predicted geographic region;
receive, from a first imaging device, a first perceived information relating to the vehicle's environment;
receive, from a second imaging device, a second perceived information relating to the vehicle's environment;
generate a first response map based on the first perceived information, the first response map comprising a plurality of first response points corresponding to lane markers detected within the first response map;
generate a second response map based on the second perceived information, the second response map comprising a plurality of second response points corresponding to lane markers detected within the second response map;
generate a first corrected location based on comparing the first response map to the first region map;
generate a second corrected location based on comparing the second response map to the second region map; and
determine a fused vehicle location based on the first corrected location and the second corrected location.
12. The system of claim 11 , wherein the fused vehicle location is further based on a previously determined vehicle location.
13. The system of claim 11 , wherein the processor is further configured to:
receive, from the database, a third region map comprising a plurality of third region points corresponding to the vehicle's predicted geographic region;
receive, from a third imaging device, a third perceived information relating to the vehicle's environment;
generate a third response map based on the third perceived information, the third response map comprising a plurality of third response points corresponding to lane markers detected within the third response map; and
generate a third corrected location based on comparing the third response map to the third region map,
wherein the fused vehicle location is further based on the third corrected location.
14. The system of claim 11 , wherein the locating device comprises a Global Positioning System (GPS) device or an inertial measurement unit (IMU).
15. The system of claim 11 , wherein the imaging device comprises a camera or a light detection and ranging (LIDAR) device.
16. The system of claim 11 , wherein the processor is further configured to output the vehicle's predicted location to an advanced driver-assistance system (ADAS).
17. The system of claim 11 , wherein the processor is further configured to determine a confidence score.
18. The system of claim 13 , wherein the first imaging device, the second imaging device, and the third imaging device are each adapted to perceive different aspects of the vehicle's environment.
19. The system of claim 17 , wherein the confidence score comprises a variance that is computed using a logistic function.
20. The system of claim 19 , wherein the variance is computed as:
variance
=
x
min
+
(
x
max
-
x
min
)
1
+
e
S
(
Gx
-
m
)
,
wherein x is a matching score of a corresponding response map, x min is a minimum value of the corresponding response map, x max is a maximum value of the corresponding response map, S is a steepness parameter, G is a growth rate parameter, and m is a midpoint of the corresponding response map.