BACKGROUND

Vehicle systems, such as autonomous driving and Advanced Driver-Assist Systems (ADAS), often need highly-accurate positioning information to operate correctly. To provide such accurate positioning, ADAS systems may utilize positioning technologies from a variety of sources. For example, Global Navigation Satellite Systems (GNSS), such as Global Positioning System (GPS) and/or similar satellite-based positioning technologies can be used to provide positioning data, which may be enhanced with (or substituted by, where necessary) Visual Inertial Odometery (VIO), which uses data from motion sensors (e.g., accelerometers, gyroscopes, etc.) and one or more cameras to track vehicle movement. These systems can be used to provide a position estimate of the vehicle in a global coordinate system (or “global frame”).

BRIEF SUMMARY

Techniques provided herein are directed toward accurately associating observed lane markings from camera images to counterpart lane boundaries within a 3-D map. Embodiments include obtaining a “stripe” indicative of an observed lane marking captured in a from a camera image, obtaining map data for the road on which the vehicle is located, determining the plane of the lane from coordinates of lane boundaries within the map data, projecting the stripe from 2-D image to 3-D world based on the plane, and comparing the projected stripe with lane boundaries from the map data and associating the observed lane markings with the closest lane boundary. Differences between the projected stripe and the associated lane boundary can then be used for error correction in the position estimate of the vehicle.

An example method of associating vehicle lane markings on a road with 3-D map data for vehicle position estimation, according to the disclosure, comprises obtaining location information comprising Global Navigation Satellite System (GNSS) information, Visual Inertial Odometery (VIO) information, or both, and obtaining one or more stripes, wherein each of the one or more stripes comprises a 2-D representation of a corresponding lane marking on the road, the 2-D representation derived from a camera image of the corresponding lane marking taken from a vehicle. The method further comprises obtaining the 3-D map data, wherein the 3-D map data comprises a plurality of lane boundaries for an area of the road in which the vehicle is located, determining a vehicle position estimate based at least in part on the location information, the one or more stripes, and the 3-D map data, and providing the vehicle position estimate to a system or device of the vehicle.

An example device for associating vehicle lane markings on a road with 3-D map data for vehicle position estimation, according to the disclosure, comprises a memory and a processing unit communicatively coupled with the memory. The processing unit is configured to obtain location information comprising Global Navigation Satellite System (GNSS) information, Visual Inertial Odometery (VIO) information, or both, and obtain one or more stripes, wherein each of the one or more stripes comprises a 2-D representation of a corresponding lane marking on the road, the 2-D representation derived from a camera image of the corresponding lane marking taken from a vehicle. The processing unit is further configured to obtain the 3-D map data, wherein the 3-D map data comprises a plurality of lane boundaries for an area of the road in which the vehicle is located, determine a vehicle position estimate based at least in part on the location information, the one or more stripes, and the 3-D map data, and provide the vehicle position estimate to a system or device of the vehicle.

Another example device, according to the disclosure, comprises means for obtaining location information comprising Global Navigation Satellite System (GNSS) information, Visual Inertial Odometery (VIO) information, or both, and means for obtaining one or more stripes, wherein each of the one or more stripes comprises a 2-D representation of a corresponding lane marking on a road, the 2-D representation derived from a camera image of the corresponding lane marking taken from a vehicle. The device further comprises means for obtaining 3-D map data, wherein the 3-D map data comprises a plurality of lane boundaries for an area of the road in which the vehicle is located, means for determining a vehicle position estimate based at least in part on the location information, the one or more stripes, and the 3-D map data, and means for providing the vehicle position estimate to a system or device of the vehicle.

An example non-transitory computer-readable medium, according to the disclosure, comprises instructions embedded thereon for associating vehicle lane markings on a road with 3-D map data for vehicle position estimation. The instructions, when executed by one or more processing units, cause the one or more processing units to obtain location information comprising Global Navigation Satellite System (GNSS) information, Visual Inertial Odometery (VIO) information, or both, and obtain one or more stripes, wherein each of the one or more stripes comprises a 2-D representation of a corresponding lane marking on the road, the 2-D representation derived from a camera image of the corresponding lane marking taken from a vehicle. The instructions, when executed by one or more processing units, further cause the one or more processing units to obtain the 3-D map data, wherein the 3-D map data comprises a plurality of lane boundaries for an area of the road in which the vehicle is located, determine a vehicle position estimate based at least in part on the location information, the one or more stripes, and the 3-D map data, and provide the vehicle position estimate to a system or device of the vehicle.

