A Survey on Deep Learning-Based Vehicular Communication Applications
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A Survey on Deep Learning-Based Vehicular Communication Applications Chia-Hung Lin 1 & Yu-Chien Lin 2,3
&
Yen-Jung Wu 2,3 & Wei-Ho Chung 2,4 & Ta-Sung Lee 2,3
Received: 1 November 2019 / Revised: 20 April 2020 / Accepted: 3 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
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 DLbased ITS applications; we also hope this paper will encourage experienced researchers to counter the open challenges and achieve a technical breakthrough to ITS. Keywords Vehicular communications . Deep learning . Intelligent transportation systems . Traffic flow forecasting . Trajectory prediction
1 Introduction Recently, there is an increasing focus on vehicular communications [1, 2]. In addition to the transmission of information, vehicular communications also perform an important role in achieving a safe road environment [3]. Specifically, the increasing population in cities causes a significantly dynamic * Yu-Chien Lin [email protected] * Wei-Ho Chung [email protected] Chia-Hung Lin [email protected]
and complicated road environment. To support the sharing of critical driving information of the dynamic and complicated environment between vehicles, vehicular communications require an extensively strong connectivity between vehicles and roadside units, along with sensing technologies and novel onboard computing of data collected by numerous sensors. Owing to these advancements, vehicular communications 1
Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, USA
2
Center for mmWave Smart Radar Systems and Technologies, National Chiao Tung University, Hsinchu, Taiwan
3
Institute of Communications Engineering, National Chiao Tung University, Hsinchu, Taiwan
4
Department of Electrical Engineering, National Tsing Hua University,
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