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
  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]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.