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SCIENCE CHINA Information Sciences, Volume 64 , Issue 5 : 156101(2021) https://doi.org/10.1007/s11432-020-3171-4

Future vehicles: interactive wheeled robots

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  • ReceivedOct 14, 2020
  • AcceptedDec 8, 2020
  • PublishedApr 12, 2021

Abstract

There is no abstract available for this article.


Acknowledgment

This work was partially founded by National Natural Science Foundation of China (Grant Nos. 61871038, 61931012, 61803034), Generic Preresearch Program of Equipment Development Department of Military Commission (Grant No. 41412040302), Consulting Project of Chinese Academy of Engineering (Grant No. 2019-XY-69), and Premium Funding Project for Academic Human Resources Development in Beijing Union University (Grant No. BPHR2020AZ02). We would like to thank Dianen ZHANG from Beijing Union University and Guilin PANG from Beijing Jiaotong University for the help on this work. We really thank anonymous reviewers' constructive suggestions.


Supplement

Appendixes A–C. Appendixes A is an introduction about the intelligent interaction team of Beijing Union University. Appendixes B and C cover the research achievements of the intelligent interaction team of Beijing Union University.


References

[1] Ma N, Gao Y, Li J H, et al. Interactive cognition in self-driving (in Chinese). Sci Sin Inform, 2018, 48: 125--138. Google Scholar

[2] Silver D H, Leigh A, Ingram B, et al. Detecting and responding to sirens. US Patent 10 319 228[P], 2019-6-11. Google Scholar

[3] Ahmad B I, Murphy J K, Langdon P M. Intent Inference for Hand Pointing Gesture-Based Interactions in Vehicles. IEEE Trans Cybern, 2016, 46: 878-889 CrossRef Google Scholar

[4] Yan C, Tu Y, Wang X. STAT: Spatial-Temporal Attention Mechanism for Video Captioning. IEEE Trans Multimedia, 2020, 22: 229-241 CrossRef Google Scholar

[5] Chen J, Zhang X, Xin B. Coordination Between Unmanned Aerial and Ground Vehicles: A Taxonomy and Optimization Perspective. IEEE Trans Cybern, 2016, 46: 959-972 CrossRef Google Scholar

[6] Ding Y, Xin B, Chen J. A Review of Recent Advances in Coordination Between Unmanned Aerial and Ground Vehicles. Un Sys, 2021, 2019: 1-21 CrossRef Google Scholar

[7] Zhou M, Luo J, Villela J, et al. SMARTS: scalable multi-agent reinforcement learning training school for autonomous driving. 2020,. arXiv Google Scholar

[8] Garc'ıa J, Fernández F. A comprehensive survey on safe reinforcement learning. J Mach Learn Res, 2015, 16: 1437--1480. Google Scholar

[9] Li J, Deng F, Chen J. A Fast Distributed Variational Bayesian Filtering for Multisensor LTV System With Non-Gaussian Noise. IEEE Trans Cybern, 2019, 49: 2431-2443 CrossRef Google Scholar

[10] Silva F L D, Costa A H R. A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems. jair, 2019, 64: 645-703 CrossRef Google Scholar

[11] Molina-Masegosa R, Gozalvez J. LTE-V for Sidelink 5G V2X Vehicular Communications: A New 5G Technology for Short-Range Vehicle-to-Everything Communications. IEEE Veh Technol Mag, 2017, 12: 30-39 CrossRef Google Scholar

[12] Li L, Wang X, Wang K. Parallel testing of vehicle intelligence via virtual-real interaction. Sci Robot, 2019, 4: eaaw4106 CrossRef Google Scholar

[13] Chen S, Jian Z, Huang Y. Autonomous driving: cognitive construction and situation understanding. Sci China Inf Sci, 2019, 62: 81101 CrossRef Google Scholar

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