Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

被引:4
|
作者
Khalil, Ruhul Amin [1 ]
Safelnasr, Ziad [1 ]
Yemane, Naod [1 ]
Kedir, Mebruk [1 ]
Shafiqurrahman, Atawulrahman [1 ]
Saeed, Nasir [1 ]
机构
[1] United Arab Emirates Univ UAEU, Dept Elect & Commun Engn, Al Ain 15551, U Arab Emirates
关键词
Intelligent transportation systems; Autonomous vehicles; deep learning; large language models; explainable AI; traffic flow prediction; TRAFFIC SIGN RECOGNITION; PRESERVING AUTHENTICATION SCHEME; TO-INFRASTRUCTURE COMMUNICATION; CONVOLUTIONAL NEURAL-NETWORKS; FLOW PREDICTION; PEDESTRIAN DETECTION; VEHICLE; PRIVACY; SAFETY; MANAGEMENT;
D O I
10.1109/OJVT.2024.3369691
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic environment characterized by complex spatial and temporal dynamics at various scales, further compounded by fluctuating conditions influenced by external factors such as social events, holidays, and weather. Navigating the intricacies of modeling the intricate interaction among these elements, creating universal representations, and employing them to address transportation issues. Yet, these intricacies comprise just one facet of the multifaceted trials confronting contemporary ITS. This paper offers an all-encompassing survey exploring Deep learning (DL) utilization in ITS, primarily focusing on practitioners' methodologies to address these multifaceted challenges. The emphasis lies on the architectural and problem-specific factors that guide the formulation of innovative solutions. In addition to shedding light on the state-of-the-art DL algorithms, we also explore potential applications of DL and large language models (LLMs) in ITS, including traffic flow prediction, vehicle detection and classification, road condition monitoring, traffic sign recognition, and autonomous vehicles. Besides, we identify several future challenges and research directions that can push the boundaries of ITS, including the critical aspects, including transfer learning, hybrid models, privacy and security, and ultra-reliable low-latency communication. Our aim for this survey is to bridge the gap between the burgeoning DL and transportation communities. By doing so, we aim to facilitate a deeper comprehension of the challenges and possibilities within this field. We hope that this effort will inspire further exploration of fresh perspectives and issues, which, in turn, will play a pivotal role in shaping the future of transportation systems.
引用
收藏
页码:397 / 427
页数:31
相关论文
共 50 条
  • [31] Intelligent transport systems: emerging technologies and methods in transportation and traffic
    Taylor, MAP
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2004, 12 (3-4) : 167 - 169
  • [32] SPECIAL ISSUE MODELS AND TECHNOLOGIES FOR INTELLIGENT TRANSPORTATION SYSTEMS Preface
    Fusco, Gaetano
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2013, 29 : 115 - 116
  • [33] Intelligent Public Transportation Systems: A Review of Architectures and Enabling Technologies
    Elkosantini, Sabeur
    Darmoul, Saber
    2013 INTERNATIONAL CONFERENCE ON ADVANCED LOGISTICS AND TRANSPORT (ICALT), 2013, : 233 - 238
  • [34] Continual driver behaviour learning for connected vehicles and intelligent transportation systems: Framework, survey and challenges
    Li, Zirui
    Gong, Cheng
    Lin, Yunlong
    Li, Guopeng
    Wang, Xinwei
    Lu, Chao
    Wang, Miao
    Chen, Shanzhi
    Gong, Jianwei
    GREEN ENERGY AND INTELLIGENT TRANSPORTATION, 2023, 2 (04):
  • [35] At the Core of Intelligent Transportation Technologies
    Angel Sotelo, Miguel
    IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE, 2018, 10 (02) : 3 - +
  • [36] Challenges in defense conversion: intelligent transportation systems as a case study
    SRI Int, Arlington, United States
    Transportation Research Record, 1995, (1516): : 70 - 77
  • [37] Data fusion in intelligent transportation systems: Progress and challenges - A survey
    El Faouzi, Nour-Eddin
    Leung, Henry
    Kurian, Ajeesh
    INFORMATION FUSION, 2011, 12 (01) : 4 - 10
  • [38] Security and Privacy Issues in Intelligent Transportation Systems: Classification and Challenges
    Hahn, Dalton
    Munir, Arslan
    Behzadan, Vahid
    IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE, 2021, 13 (01) : 181 - 196
  • [39] Meeting privacy challenges while advancing intelligent transportation systems
    Fries, Ryan N.
    Gahrooei, Mostafa Reisi
    Chowdhury, Mashrur
    Conway, Alison J.
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2012, 25 : 34 - 45
  • [40] Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey
    Haydari, Ammar
    Yilmaz, Yasin
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (01) : 11 - 32