AI/ML-based services and applications for 6G-connected and autonomous vehicles

被引:0
作者
Casetti, Claudio [1 ]
Chiasserini, Carla Fabiana [1 ]
Dressler, Falko [2 ]
Memedi, Agon [2 ]
Gasco, Diego [1 ]
Schiller, Elad Michael [3 ]
机构
[1] Politecn Torino, Turin, Italy
[2] TU Berlin, Berlin, Germany
[3] Chalmers Univ Technol, Gothenburg, Sweden
关键词
5G; 6G; Connected autonomous vehicles; Intelligent services; Machine learning; CHALLENGES; SAFETY;
D O I
10.1016/j.comnet.2024.110854
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
AI and ML emerge as pivotal in overcoming the limitations of traditional network optimization techniques and conventional control loop designs, particularly in addressing the challenges of high mobility and dynamic vehicular communications inherent in the domain of connected and autonomous vehicles (CAVs). The survey explores the contributions of novel AI/ML techniques in the field of CAVs, also in the context of innovative deployment of multilevel cloud systems and edge computing as strategic solutions to meet the requirements of high traffic density and mobility in CAV networks. These technologies are instrumental in curbing latency and alleviating network congestion by facilitating proximal computing resources to CAVs, thereby enhancing operational efficiency also when AI-based applications require computationally-heavy tasks. A significant focus of this survey is the anticipated impact of 6G technology, which promises to revolutionize the mobility industry. 6G is envisaged to foster intelligent, cooperative, and sustainable mobility environments, heralding a new era in vehicular communication and network management. This survey comprehensively reviews the latest advancements and potential applications of AI/ML for CAVs, including sensory perception enhancement, real-time traffic management, and personalized navigation.
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页数:12
相关论文
共 118 条
  • [31] DEEP LEARNING FOR RELIABLE MOBILE EDGE ANALYTICS IN INTELLIGENT TRANSPORTATION SYSTEMS An Overview
    Ferdowsi, Aidin
    Challita, Ursula
    Saad, Walid
    [J]. IEEE VEHICULAR TECHNOLOGY MAGAZINE, 2019, 14 (01): : 62 - 70
  • [32] Value Sensitive Design and Information Systems
    Friedman, Batya
    Kahn, Peter H., Jr.
    Borning, Alan
    [J]. EARLY ENGAGEMENT AND NEW TECHNOLOGIES: OPENING UP THE LABORATORY, 2013, 16 : 55 - 95
  • [33] A Survey of Driving Safety With Sensing, Vehicular Communications, and Artificial Intelligence-Based Collision Avoidance
    Fu, Yuchuan
    Li, Changle
    Yu, Fei Richard
    Luan, Tom H.
    Zhang, Yao
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (07) : 6142 - 6163
  • [34] A Decision-Making Strategy for Vehicle Autonomous Braking in Emergency via Deep Reinforcement Learning
    Fu, Yuchuan
    Li, Changle
    Yu, Fei Richard
    Luan, Tom H.
    Zhang, Yao
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2020, 69 (06) : 5876 - 5888
  • [35] The role of vehicular applications in the design of future 6G infrastructures
    Gallego-Madrid, Jorge
    Sanchez-Iborra, Ramon
    Ortiz, Jordi
    Santa, Jose
    [J]. ICT EXPRESS, 2023, 9 (04): : 556 - 570
  • [36] Gao JY, 2020, PROC CVPR IEEE, P11522, DOI 10.1109/CVPR42600.2020.01154
  • [37] Garlichs K, 2019, IEEE VEHIC NETW CONF, DOI [10.1109/VNC48660.2019.9062827, 10.1109/vnc48660.2019.9062827]
  • [38] A generalized approach to automotive forensics
    Gomez Buquerin, Kevin Klaus
    Corbett, Christopher
    Hof, Hans-Joachim
    [J]. FORENSIC SCIENCE INTERNATIONAL-DIGITAL INVESTIGATION, 2021, 36
  • [39] Gordon C., 2021, Driverless cars and AI ethics
  • [40] INTELLIGENT TASK OFFLOADING IN VEHICULAR EDGE COMPUTING NETWORKS
    Guo, Hongzhi
    Liu, Jiajia
    Ren, Ju
    Zhang, Yanning
    [J]. IEEE WIRELESS COMMUNICATIONS, 2020, 27 (04) : 126 - 132