TY - GEN
T1 - Advancing ITS Applications with LLMs
T2 - International Joint Conference on Rough Sets, IJCRS 2024
AU - Zhang, Dingkai
AU - Zheng, Huanran
AU - Yue, Wenjing
AU - Wang, Xiaoling
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - In the past two years, large language models (LLMs) have shown extensive attention in the applications of intelligent transportation systems (ITS). Despite the huge potential, there is still a lack of comprehensive understanding of the advantages, challenges, and future efforts of LLMs in the transportation field. In this paper, we present a systematic investigation in this field, underlining their approaches and performance in improving forecasting accuracy, decision-making capability, and sim-to-real tasks. We first explore the current applications of LLMs in traffic management, transportation safety, and autonomous driving, as well as analyze their advantages and limitations. Then we also list some typical datasets employed within this domain. Challenges and prospects of the development of LLMs for ITS applications are discussed, encompassing technological, security, and policy aspects. We aim to offer a holistic overview of the transformative impact of LLMs in the transportation field, highlight their significance, and provide some possible views for future research and development.
AB - In the past two years, large language models (LLMs) have shown extensive attention in the applications of intelligent transportation systems (ITS). Despite the huge potential, there is still a lack of comprehensive understanding of the advantages, challenges, and future efforts of LLMs in the transportation field. In this paper, we present a systematic investigation in this field, underlining their approaches and performance in improving forecasting accuracy, decision-making capability, and sim-to-real tasks. We first explore the current applications of LLMs in traffic management, transportation safety, and autonomous driving, as well as analyze their advantages and limitations. Then we also list some typical datasets employed within this domain. Challenges and prospects of the development of LLMs for ITS applications are discussed, encompassing technological, security, and policy aspects. We aim to offer a holistic overview of the transformative impact of LLMs in the transportation field, highlight their significance, and provide some possible views for future research and development.
KW - Autonomous Driving
KW - Intelligent Transportation Systems
KW - Large Language Model
UR - https://www.scopus.com/pages/publications/85200700829
U2 - 10.1007/978-3-031-65668-2_20
DO - 10.1007/978-3-031-65668-2_20
M3 - 会议稿件
AN - SCOPUS:85200700829
SN - 9783031656675
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 295
EP - 309
BT - Rough Sets - International Joint Conference, IJCRS 2024, Proceedings
A2 - Hu, Mengjun
A2 - Lingras, Pawan
A2 - Cornelis, Chris
A2 - Zhang, Yan
A2 - Ślęzak, Dominik
A2 - Yao, JingTao
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 17 May 2024 through 20 May 2024
ER -