How generative adversarial networks promote the development of intelligent transportation systems: A survey
Artikel i vetenskaplig tidskrift, 2023

In current years, the improvement of deep learning has brought about tremendous changes: As a type of unsupervised deep learning algorithm, generative adversarial networks (GANs) have been widely employed in various fields including transportation. This paper reviews the development of GANs and their applications in the transportation domain. Specifically, many adopted GAN variants for autonomous driving are classified and demonstrated according to data generation, video trajectory prediction, and security of detection. To introduce GANs to traffic research, this review summarizes the related techniques for spatio-temporal, sparse data completion, and time-series data evaluation. GAN-based traffic anomaly inspections such as infrastructure detection and status monitoring are also assessed. Moreover, to promote further development of GANs in intelligent transportation systems (ITSs), challenges and noteworthy research directions on this topic are provided. In general, this survey summarizes 130 GAN-related references and provides comprehensive knowledge for scholars who desire to adopt GANs in their scientific works, especially transportation-related tasks.

generative adversarial network (GAN)

traffic flow

traffic anomaly inspection

Training

Generators

Generative adversarial networks

intelligent transportation system (ITS)

Inspection

Transportation

Autonomous driving

Deep learning

Surveys

Författare

Hongyi Lin

Tsinghua University

Yang Liu

Chalmers, Arkitektur och samhällsbyggnadsteknik, Geologi och geoteknik

Shen Li

Tsinghua University

Xiaobo Qu

Tsinghua University

IEEE/CAA Journal of Automatica Sinica

23299266 (ISSN) 23299274 (eISSN)

Vol. 10 9 1781-1796

Ämneskategorier

Datavetenskap (datalogi)

Datorsystem

DOI

10.1109/JAS.2023.123744

Mer information

Senast uppdaterat

2024-03-07