Parallel Driving with Big Models and Foundation Intelligence in Cyber-hysical-ocial Spaces

作     者：Xiao Wang Jun Huang Yonglin Tian Chen Sun Lie Yang Shanhe Lou Chen Lv Changyin Sun Fei-Yue Wang 

作者机构：School of Artificial IntelligenceAnhui UniversityHefeiChina Macao University of Science and TechnologyMacaoChina State Key Laboratory for Management and Control of Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijingChina MVSLabDepartment of Mechanical and Mechatronics EngineeringUniversity of Waterloo200 University Ave WestWaterlooON N2L3G1Canada School of Mechanical and Aerospace EngineeringNanyang Technological UniversitySingaporeSingapore 

出 版 物：《Research》 (研究（英文）)

年 卷 期：2024年第2024卷第2期

页      面：1-17页

核心收录：

学科分类：07[理学] 0835[工学-软件工程] 0814[工学-土木工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 070101[理学-基础数学] 

基　　金：the National Natural Science Foundation of China (62173329) the University Scientifc Research Program of Anhui Province (2023AH020005) Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (A Unified Approach for Transport Automation and Vehicle Intelligence: Parallel Driving) the National Natural Science Foundation of China (grant number 62173329, 2022, Prediction and Guidance Effect of Social Media on Traffic Congestion and Its Derivative Events) Guangdong Key Area R&D Plan (grant number 2020B0909050003, 2020) 

主　　题：breakthrough integrate corners 

摘      要：Recent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation systems (ITSs);on the other hand, these new traffic participants introduce more complex and uncertain elements to ITSs from the social space. Digital twins (DTs) provide real-time, data-driven, precise modeling for constructing the digital mapping of physical-world ITSs. Meanwhile, the metaverse integrates emerging technologies such as virtual reality/mixed reality, artificial intelligence, and DTs to model and explore how to realize improved sustainability, increased efficiency, and enhanced safety. More recently, as a leading effort toward general artificial intelligence, the concept of foundation model was proposed and has achieved significant success, showing great potential to lay the cornerstone for diverse artificial intelligence applications across different domains. In this article, we explore the big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces, which integrate metaverse and DTs to construct a parallel training space for CAVs, and present a comprehensive elucidation of the crucial characteristics and operational mechanisms. Beyond providing the infrastructure and foundation intelligence of big models for parallel driving, this article also discusses future trends and potential research directions, and the ?S?goals of parallel driving.