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.

除了使用信息传输之外，车辆通信还在智能运输系统（ITS）中发挥重要作用，以在最终用户，车辆和基础设施之间交换重要的驾驶信息。此外，为了增强对本地环境的了解，越来越多的传感器收集了数据，从而在ITS中广泛使用了基于深度学习（DL）的算法。为了进一步促进ITS中基于DL的算法的发展，在本文中，我们对DL技术进行了简要介绍。然后，我们对ITS中使用的两种流行的基于DL的应用程序（交通流预测和轨迹预测）进行了深入研究，重点关注何时以及如何作者在这些任务中采用了不同的DL模型和培训方案。最后，在实际ITS中采用基于DL的算法时，我们提出了两个现存的问题，并进一步讨论了基于DL的研究中的最新进展以应对这些挑战。为了鼓励更多的研究人员专注于ITS中基于DL的算法的开发，以创造更美好的世界，我们希望本文可以作为准研究人员的信息材料，其中包含基于DL的ITS应用的基本背景知识；我们也希望本文能够鼓励有经验的研究人员应对开放的挑战，并实现ITS的技术突破。