LaneNet++: Uncertainty-Aware Lane Detection for Autonomous Vehicle

Author:

Basavaraj Meghana,Suddamalla Upendra,Xu Shenxin

Publisher

Springer Nature Switzerland

Reference20 articles.

1. Baek, S., Kim, M., Suddamalla, U., et al.: Real-time lane detection based on deep learning. J. Electr. Eng. Technol. 17, 655–664 (2022)

2. Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural networks. in Proceedings of the 32nd International Conference on International Conference on Machine Learning, vol. 37, pp. 1613–1622 (2015)

3. Brabandere, B.D., Neven, D., Gool, L.V.: Semantic instance segmentation with a discriminative loss function. ArXiv abs/1708.02551 (2017)

4. Report - concepts of design assurance for neural networks (CoDANN). https://www.easa.europa.eu/en/document-library/general-publications/concepts-design-assurance-neural-networks-codann

5. Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: International Conference on Machine Learning, pp. 1050–1059 (2016)

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