Otwarty dostęp

Ego Vehicle Lane Detection and Key Point Determination Using Deep Convolutional Neural Networks and Inverse Projection Mapping


Zacytuj

Singh, S. (2015) Critical reasons for crashes investigated in the national motor vehicle crash causation survey (No. DOT HS 812 115).Washington, DC: National Highway Traffic Safety Administration. Search in Google Scholar

Bellis, E., & Page, J. (2008) National motor vehicle crash causation survey (NMVCCS) SAS analytical users manual (No. HS-811 053). Search in Google Scholar

Gayko, J. E. (2012) Lane departure and lane keeping. Handbook of intelligent vehicles, 1, 689-708. Search in Google Scholar

Visvikis, C., Smith, T. L., Pitcher, M., & Smith, R. (2008) Study on lane departure warning and lane change assistant systems. Transport Research Laboratory Project Rpt PPR, 374, 1-128. Search in Google Scholar

Neven, D., De Brabandere, B., Georgoulis, S., Proesmans, M., & Van Gool, L. (2018) Towards endto-end lane detection: an instance segmentation approach. In: 2018 IEEE intelligent vehicles symposium (IV), Changshu, China, June 2018, 286-291. Search in Google Scholar

Yan, X., & Li, Y. A method of lane edge detection based on Canny algorithm. In: 2017 Chinese Automation Congress (CAC), China, October 2017, pp. 2120-2124. Search in Google Scholar

Oliveira, M., Santos, V., & Sappa, A. D. (2015) Multimodal inverse perspective mapping. Information Fusion, Information Fusion, 24, 108-121. Search in Google Scholar

Muthalagu, R., Bolimera, A., and Kalaichelvi, V. (2021) Vehicle lane markings segmentation and keypoint determination using deep convolutional neural networks. Multimedia Tools and Applications, 80(7), 11201-11215. Search in Google Scholar

Amaradi, P., Sriramoju, N., Dang, L., Tewolde, G.S. & Kwon, J. (2016) Lane following and obstacle detection techniques in autonomous driving vehicles. In: 2016 IEEE International Conference on Electro Information Technology (EIT), Grand Forks, North Dakota, May 2016, pp. 0674-0679. Search in Google Scholar

Xing, Y. et al. (2018) Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision, in IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 3, pp. 645-661, May 2018, doi: 10.1109/JAS.2018.7511063. Open DOISearch in Google Scholar

Wang, J. Gao & Y. Yuan (2018) Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection, IEEE Transactions on Intelligent Transportation Systems, 19(1), 230-241, doi: 10.1109/TITS.2017.2749964. Open DOISearch in Google Scholar

Ronneberger, O., Fischer P., & Brox T. (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Munich, Germany, October 2015, pp. 234-241. Springer, Cham. Search in Google Scholar

Shelhamer, E., Long, J. & Darrell, T. (2017) Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640-651. doi: 10.1109/TPAMI.2016.2572683. Open DOISearch in Google Scholar

Badrinarayanan, V., Kendall, A. & Cipolla, R. (2017) Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, 39(12), 2481-2495. Search in Google Scholar

He, K., Zhang, X., Ren, S. & Sun, J. (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, USA, June 2016, 770-778. Search in Google Scholar

The tuSimple lane challenge, github. (2018). GitHub. Retrieved from http://benchmark.tusimple.ai/ Search in Google Scholar

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. Search in Google Scholar

Simonyan, K. & Zisserman, A. (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556. 1-14. Search in Google Scholar

He, K., Zhang, X., Ren, S. & Sun, J. (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, Santiago, Chile, December 2015, pp. 1026-1034. Search in Google Scholar

Bottou L. (2010) Largescale machine learning with stochastic gradient descent. In: Proceedings of COMPSTAT 2010, Paris, France, August 2010, pp. 177–186, Springer. Search in Google Scholar

Lucas, T., Rodrigo, B., Thiago, M. P., Claudine, B., Alberto, F. De Souza, & Thiago, O-S. (2020) PolyLaneNet: Lane Estimation via Deep Polynomial Regression. arXiv preprint arXiv: 2004.10924. 1-7. Search in Google Scholar

Pan, X., Shi, J., Luo, P., Wang, X., & Tang, X. (2018) Spatial as deep: Spatial CNN for traffic scene understanding. In: Association for the Advancement of Artificial Intelligence, New Orleans, Louisiana USA, February 2018, pp 1-8. Search in Google Scholar

Muthalagu, R., Bolimera, A. & Kalaichelvi, V. (2020) Lane detection technique based on perspective transformation and histogram analysis for self-driving cars. Computers & Electrical Engineering, Elseiver, 85, 106653, 1-17. Search in Google Scholar

eISSN:
1407-6179
Język:
Angielski
Częstotliwość wydawania:
4 razy w roku
Dziedziny czasopisma:
Engineering, Introductions and Overviews, other