A Survey on Deep Learning-Based Vehicular Communication Applications

Chia Hung Lin, Yu Chien Lin*, Yen Jung Wu, Wei Ho Chung, Ta Sung Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
JournalJournal of Signal Processing Systems
DOIs
StateAccepted/In press - 2020

Keywords

  • Deep learning
  • Intelligent transportation systems
  • Traffic flow forecasting
  • Trajectory prediction
  • Vehicular communications

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