Subject Area

Computer and Control Systems Engineering

Article Type

Original Study


The lane detecting algorithm plays a major role in advanced driver assistance systems (ADAS) and autonomous driving systems. In recent years, deep learning-based lane detection techniques have shown encouraging results; nonetheless, the quality and size of the training data set has a significant impact on how effective these techniques are. Active learning is a technique that can improve the capacity of deep learning-based lane identification systems to repeatedly choose and classify valuable samples from a large body of unlabeled data. In this research, a novel 1-dimensional deep learning approach is used to present an augmented Active Learning based Lane Detection Algorithm (ALDA) that picks informative samples based on diversity- and uncertainty-based criteria. A number of benchmark datasets, including the CUlane, have been used to assess the suggested technique, In terms of accuracy and robustness, the suggested method (ALDA) performs better than four cutting-edge lane detecting algorithms. The findings show that active learning can significantly reduce the quantity of labelled data required for training while preserving good performance. The suggested method may improve the dependability and security of ADAS and autonomous driving systems. When compared to other distinct Deep Learning approaches, the proposed Active Learning based Lane Detection Algorithm (ALDA) obtains an accuracy of 98.01%, Precision of 98.5173%, Recall of 95.2296%, F1 score of 96.845%, mAP of 92.7%, and MSE of 0.0097


Driver Assistance Systems; Active Learning; Deep Learning; Lane Detection

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.