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A Survey on Deep Learning-Based Vehicular Communication Applications

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Abstract

Besides the use of information transmission, vehicular communications also perform an essential role in intelligent transportation systems (ITS) for exchanging critical driving information among end users, vehicles, and infrastructures. Moreover, to enhance the understanding of the local environment, increasingly more data are collected by sensors, inducing an extensive use of deep learning (DL)-based algorithms in ITS. To further promote the development of DL-based algorithms in ITS, in this paper, we present a concise introduction of DL technologies. Then, we conduct an in-depth investigation on two popular DL-based applications used in ITS, traffic flow forecasting and trajectory prediction, focusing on when and how the authors employ different DL models and training schemes in these tasks. Finally, we raise two existing problems while employing DL-based algorithms in practical ITS and further discuss certain recent advances in DL-based research to tackle these challenges. To encourage more researchers to focus on the development of DL-based algorithms in ITS for a better world, we hope this paper can be treated as an informational material for prospective researchers, which contains the essential background knowledge of DL-based ITS applications; we also hope this paper will encourage experienced researchers to counter the open challenges and achieve a technical breakthrough to ITS.

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Acknowledgements

This work was partially supported by the “Center for mmWave Smart Radar Systems and Technologies” and the “Center for Open Intelligent Connectivity” under the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) of Taiwan, and partially supported by the Ministry of Science and Technology (MOST) of Taiwan under grant MOST 109-2634-F-009-030, MOST 109-2218-E-009-002.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, USA

    Chia-Hung Lin

  2. Center for mmWave Smart Radar Systems and Technologies, National Chiao Tung University, Hsinchu, Taiwan

    Yu-Chien Lin, Wu Yen-Jung, Wei-Ho Chung & Lee Ta-Sung

  3. Institute of Communications Engineering, National Chiao Tung University, Hsinchu, Taiwan

    Yu-Chien Lin, Wu Yen-Jung & Lee Ta-Sung

  4. Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan

    Wei-Ho Chung

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  1. Chia-Hung Lin
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Correspondence to Yu-Chien Lin or Wei-Ho Chung.

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Lin, CH., Lin, YC., Wu, YJ. et al. A Survey on Deep Learning-Based Vehicular Communication Applications. J Sign Process Syst 93, 369–388 (2021). https://doi.org/10.1007/s11265-020-01587-2

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