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Sharp Curve Detection of Autonomous Vehicles using DBSCAN and Augmented Sliding Window Techniques

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Abstract

Deviation of the car from the lane is very dangerous and it leads to crashes. Hence, lane detection is one of the most important features that helps in maintaining the car stay in the respective lane. The main goal of this paper is to detect the lanes with road lane markings with higher accuracy under sharp curve scenarios. In this paper, lane points are extracted using image processing techniques, lane detection of solid lines and dashed lines using a combination of Augmented sliding windows and Clustering technique is discussed. Lanes are simulated in a laboratory for an input data set of 1388 images. The input data set consists of curved lines of dashed and solid lines which split and merge. This combined technique (Augmented sliding windows + clustering) is mainly used to detect the split lanes scenario. Further partially obscured lanes are also tested with the algorithm. The center of the lanes throughout the lane is calculated. Missing lane markings of obscured lanes are also estimated using the relative position of adjacent lanes based on lane width. The detection accuracy of Clustering and Augmented sliding window for the considered input dataset of 1388 frames is 97.70%. The algorithm is showing reliable accuracy in detecting the sharp curves.

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  1. Northern Illinois University, Dekalb, IL, 60115, USA

    Keerti Chand Bhupathi & Hasan Ferdowsi

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  1. Keerti Chand Bhupathi
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Correspondence to Keerti Chand Bhupathi.

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Bhupathi, K.C., Ferdowsi, H. Sharp Curve Detection of Autonomous Vehicles using DBSCAN and Augmented Sliding Window Techniques. Int. J. ITS Res. 20, 651–671 (2022). https://doi.org/10.1007/s13177-022-00317-1

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