Road departure warning system

1. A method of providing a road departure warning alert in a vehicle comprising:

creating a road texture model based on a vehicle image received from a vehicle camera, the road texture model classifying each of a plurality of portions of the vehicle image as road or non-road;

smoothing the road texture model by:

identifying outlier portions of the vehicle image, wherein each outlier portion comprises an image portion classified as non-road and surrounded by a threshold number of portions of the vehicle image classified as road; and

re-classifying the identified outlier portions as road;

retrieving map imagery based on an initial vehicle location estimate;

determining a refined vehicle location using visual egomotion based on the vehicle image and the map imagery by:

aligning 3D features between the vehicle image and each map image in the retrieved map imagery;

selecting a map image based on the aligned 3D features;

determining a displacement between a location associated with the selected map image and a corresponding location associated with the vehicle image; and

applying the determined displacement to the initial vehicle location estimate to determine the refined vehicle location;

creating a road boundary model describing the edges of the road on which the vehicle is located based on the smoothed road texture model and the refined vehicle location;

creating a road departure model based on the road boundary model and vehicle odometry information; and

warning a vehicle operator based on the road departure model.

2. The method of claim 1 , wherein the vehicle image comprises an image of the road on which the vehicle is located in the direction in which the vehicle is moving.

3. The method of claim 1 , wherein creating a road texture model further comprises:

processing the vehicle image using a filter bank to produce a texton for each pixel in the vehicle image; and

classifying each texton in vehicle image using a set of classifiers.

4. The method of claim 3 , further comprising:

training the set of classifiers using the filter bank and a set of training images with pre-classified pixels.

5. The method of claim 3 , wherein the set of classifiers comprise random forest classifiers.

6. The method of claim 3 , further comprising:

updating the set of classifiers based on the classification of textons representing pixels with known classifications.

7. The method of claim 1 , wherein the retrieved map imagery comprises one of: satellite map imagery, aerial map imagery, and street view map imagery.

8. The method of claim 1 , wherein the initial vehicle location estimate comprises location coordinates received from a GPS receiver.

9. The method of claim 1 , wherein the 3D features are identified using edge detectors to detect edges in images.

10. The method of claim 1 , wherein aligning the 3D features between the vehicle image and each map image comprises:

projecting the 3D features onto the ground plane; and

comparing the ground plane location of the vehicle image's 3D features to the ground plane location of each map image's 3D features.

11. The method of claim 1 , wherein the selected map image comprises the map image with the most 3D features in common with the vehicle image.

12. The method of claim 1 , wherein creating a road boundary model comprises:

identifying the edges of the road based on an image of the road;

identifying the width of the road based on the identified road edges;

reducing the identified width of the road by a safety margin; and

fitting one or more curves to the road edges and reduced width of the road to create a road boundary model.

13. The method of claim 12 , wherein the image of the road comprises one or more of the vehicle image, a satellite map image of the road, an aerial map image of the road, and a street view map image of the road.

14. The method of claim 12 , further comprising:

identifying one or both of the center line of the road and the shape of the road; and

fitting one or more curves to the center line of the road or the shape of the road.

15. The method of claim 1 , wherein creating a road departure model comprises identifying the probability of a road departure based on the road boundary model and the vehicle odometry information.

16. The method of claim 15 , wherein warning a vehicle operator based on the road departure model comprises warning the vehicle operator when the identified probability of a road departure exceeds a pre-determined threshold.

17. A system of providing a road departure warning alert in a vehicle comprising:

a non-transitory computer-readable storage medium storing computer executable instructions comprising:

a road texture module configured to create a road texture model based on a vehicle image received from a vehicle camera, the road texture model classifying each of a plurality of portions of the vehicle image as road or non-road;

a smoothing module configured to smooth the road texture module by:

identifying outlier portions of the vehicle image, wherein each outlier portion comprises an image portion classified as non-road and surrounded by a threshold number of portions of the vehicle image classified as road; and

re-classifying the identified outlier portions as road;

a map module configured to retrieve map imagery based on an initial vehicle location estimate;

a location module configured to determine a refined vehicle location using visual egomotion based on the vehicle image and the map imagery by:

aligning 3D features between the vehicle image and each map image in the retrieved map imagery;

selecting a map image based on the aligned 3D features;

determining a displacement between a location associated with the selected map image and a corresponding location associated with the vehicle image; and

applying the determined displacement to the initial vehicle location estimate to determine the refined vehicle location;

a road boundary module configured to create a road boundary model describing the edges of the road on which the vehicle is located based on the smoothed road texture model and the refined vehicle location;

a road departure module configured to create a road departure model based on the road boundary model and vehicle odometry information; and

a warning module configured to warn a vehicle operator based on the road departure model; and

a processor configured to execute the computer executable instructions.

18. A method of refining a vehicle location estimate comprising:

creating a road texture model based on a vehicle image received from a vehicle camera, the road texture model classifying each of a plurality of portions of the vehicle image as road or non-road;

smoothing the road texture model by:

identifying outlier portions of the vehicle image, wherein each outlier portion comprises an image portion classified as non-road and surrounded by a threshold number of portions of the vehicle image classified as road; and

re-classifying the identified outlier portions as road;

retrieving map imagery based on an initial vehicle location estimate; and

determining a refined vehicle location using visual egomotion based on the vehicle image, the smoothed road texture model, and the map imagery at least in part by:

aligning 3D features between the vehicle image and each map image in the retrieved map imagery;

selecting a map image based on the aligned 3D features;

determining a displacement between a location associated with the selected map image and a corresponding location associated with the vehicle image; and

applying the determined displacement to the initial vehicle location estimate to determine the refined vehicle location.

19. The method of claim 18 , wherein the vehicle comprises a GPS receiver, and wherein the initial vehicle location estimate comprises a GPS location estimate received from the GPS receiver.