Research on the Application of Deep Learning in Automatic Vehicle

Jiarui Bai

doi:10.1051/shsconf/202214403021

All issues

Volume 144 (2022)

SHS Web Conf., 144 (2022) 03021

References

Open Access

Issue		SHS Web Conf. Volume 144, 2022 2022 International Conference on Science and Technology Ethics and Human Future (STEHF 2022)


Article Number		03021
Number of page(s)		5
Section		Application of Artificial Intelligence Technology and Machine Learning Algorithms
DOI		https://doi.org/10.1051/shsconf/202214403021
Published online		26 August 2022

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[1] Huang W, Song G, Hong H, et al. Deep architecture for traffic flow prediction: deep belief networks with multitask learning[J]. IEEE Transactions on Intelligent transportation Systems, 2014, 15(5):2191-2201. [CrossRef] [Google Scholar]

[2] Liu Quan, Zhai Jianwei, Zhang Zongchang, Zhong Shan, etc. A summary of deep reinforcement learning. Journal of computer science, 2018 [Google Scholar]

[3] Zhou Z H, Feng J. Deep forest: towards an alternative to deep neural networks [C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2017:3553-3559 [Google Scholar]

[4] Caselitz T, Steder B, Ruhnke M, et al. Monocular camera localization in 3D LiDAR maps[C]. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea, October 9-14, 2016. [Google Scholar]

[5] Zheng Fuyu Automatic mobile robot based on lowcost two-dimensional lidar [D] Guangzhou: Guangdong University of technology, 2018 [Google Scholar]

[6] Wang Yingfeng, Xu Yansong, Chen Yanhai Overview of intelligent vehicle multi-target detection technology based on multi-sensor fusion [J] Journal of automotive safety and energy conservation, 2021, 12 (04): 440-455 [Google Scholar]

[7] Zijiang Zhu, Zhenlong Hu, Weihuang Dai, Hang Chen, Zhihan Lv. Deep learning for autonomousvehicle and pedestrian interaction safety. Safety Science Volume 145, January 2022, 105479 [CrossRef] [Google Scholar]

[8] Wangq, Gaoj, Yuan Y. Embedding structured contour and location prior in siamesed fully convolutional networks for road detection[J]. IEEE TransIntell, 2018:230-241. [Google Scholar]

[9] Tayarah, Kim G S, Kil T C. Vehicle detection and counting in high-resolution aerial images using convolutional regression neural network[J]. IEEE Access, 2017(6): 2220-2230. [Google Scholar]

[10] Melotti G, Premebida C, GonÇalves N. Multimodal deep-learning for object recognition combining camera and LIDAR data[J]. In 2020 IEEE International Conference on Autonomous Robot Systems and Competitions(ICARSC). 2020:177-182. [Google Scholar]

[11] Barba-Guaman L, Jose E N, Anthony O. Deep learning framework for vehicle and pedestrian detection in rural roads on an embedded gpu[J]. Electronics, 2020(4): 1-17. [Google Scholar]

[12] Ucar A, Demir Y, Gjzeli C. Object recognition and detection with deep learning for autonomous driving applications[J]. 2017, 93(9): 759-769. [Google Scholar]