ROAD DEFECT LEVEL PREDICTION

20240354921

18/616396

March 26, 2024

Systems and methods for road defect level prediction. A depth map is obtained from an image dataset received from input peripherals by employing a vision transformer model. A plurality of semantic maps is obtained from the image dataset by employing a semantic segmentation model to give pixel-wise segmentation results of road scenes to detect road pixels. Regions of interest (ROI) are detected by utilizing the road pixels. Road defect levels are predicted by fitting the ROI and the depth map into a road surface model to generate road points classified into road defect levels. The predicted road defect levels are visualized on a road map.

1. A computer-implemented method for road defect level prediction employing a processor device comprising: obtaining a depth map from an image data received from input peripherals by employing a vision transformer model; obtaining a plurality of semantic maps from the image data by employing a semantic segmentation model to give pixel-wise segmentation results of road scenes to detect road pixels; detecting a region of interest (ROI) utilizing the road pixels; predicting road defect levels by fitting the ROI and the depth map into a road surface model; and outputting the predicted road defect levels on a road map.

2. The computer-implemented method of claim 1, wherein the vision transformer model is a dense prediction transformation model (DPT).

3. The computer-implemented method of claim 1, wherein the semantic segmentation model is a Universal Segmentation (UniSeg) model.

4. The computer-implemented method of claim 1, wherein the ROI is selected by employing a ROI detection module based on the road pixels and road distances obtained by utilizing the semantic segmentation model.

5. The computer-implemented method of claim 1, wherein the road surface model predicts road defect levels by calculating a severity of differences of road points that are beyond a road surface threshold.

6. The computer-implemented method of claim 5, wherein the road surface model utilizes the depth map and the ROI to filter road points up to a certain distance to determine the road defect level above or below the road surface threshold.

7. The computer-implemented method of claim 1, wherein the depth map obtained from the image data is converted to a three-dimensional point cloud to calculate a road surface plane equation.

8. The computer-implemented method of claim 3, wherein the semantic segmentation model generates semantic maps that include road scene attributes and road categories that are employed to select the ROI.

9. A non-transitory computer-readable storage medium comprising a computer-readable program for road defect level prediction wherein the computer-readable program when executed on a computer causes the computer to perform: obtaining a depth map from an image data received from input peripherals by employing a vision transformer model; obtaining a plurality of semantic maps from the image data by employing a semantic segmentation model to give pixel-wise segmentation results of road scenes to detect road pixels; detecting a region of interest (ROI) utilizing the road pixels; predicting road defect levels by fitting the ROI and the depth map into a road surface model; and outputting the predicted road defect levels on a road map.

10. The non-transitory computer-readable storage medium of claim 9, wherein the vision transformer model is a dense prediction transformation model (DPT).

11. The non-transitory computer-readable storage medium of claim 9, wherein the semantic segmentation model is a Universal Segmentation model.

12. The non-transitory computer-readable storage medium of claim 9, wherein the ROI is selected by employing a ROI detection module based on the road pixels and road distances obtained by utilizing the semantic segmentation model.

13. The non-transitory computer-readable storage medium of claim 9, wherein the road surface model predicts road defect levels by calculating a severity of differences of road points that are beyond a road surface threshold.

14. The non-transitory computer-readable storage medium of claim 13, wherein the road surface model utilizes the depth map and the ROI to filter road points up to a certain distance to determine the road defect level above or below the road surface threshold.

15. The non-transitory computer-readable storage medium of claim 9, wherein the depth map obtained from the image data is converted to a three-dimensional point cloud to calculate a road surface plane equation.

16. The non-transitory computer-readable storage medium of claim 12, wherein the semantic segmentation model generates semantic maps that includes road scene attributes and road categories that are employed to select the ROI.

17. A system for road defect level prediction, the system comprising: a memory; and one or more processors in communication with the memory configured to: obtain a depth map from an image data from an image dataset received from input peripherals by employing a vision transformer model; obtain a plurality of semantic maps from the image data by employing a semantic segmentation model to give pixel-wise segmentation results of road scenes to detect road pixels; detect a region of interest (ROI) utilizing the road pixels; predict road defect levels by fitting the ROI and the depth map into a road surface model; and output the predicted road defect levels on a road map.

18. The system for road defect level prediction of claim 17, wherein the input peripherals are mounted on a vehicle.

19. The system for road defect level prediction of claim 17, wherein coordinates of the predicted road defect levels are broadcast to other vehicles when a vehicle implementing the system for road defect level prediction approaches the coordinates.

20. The system for road defect level prediction of claim 17, wherein the road surface model predicts road defect levels by calculating a severity of differences of road points that are beyond a road surface threshold.

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