| dc.description.abstract |
Path planning is a crucial task in autonomous vehicles for which real-time road segmentation is very important. Most existing road segmentation techniques are supervised but, in many cases, their performance may be limited by the availability of and variety in a large training dataset. In contrast, we propose a research direction on unsupervised road segmentation that does not need any training or adaption and can be utilized widely. We use K-means clustering and image processing techniques to segment roads in RGB images. The scheme works well on the KITTI Road dataset (urban), giving a maximum, mean, and minimum IoU score of 93.75 %, 66.64% and 32.21% respectively. The minimum, mean and maximum time taken for segmentation were 1.084 s, 1.999 s and 3.794 s respectively on an Intel Core i5-8th Gen.(8GB RAM) CPU. A major reason for low values of minimum accuracy is that the scheme may segment the sidewalk also as a road. Although the mean IoU score is lower and the processing time higher relative to existing schemes, the results are very promising as our scheme is completely unsupervised and the processing time can be reduced by leveraging the capabilities of GPUs, parallel execution, hardware acceleration and the like. |
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