Parallel Intelligence for Smart Mobility in Cyberphysical Social System-Defined Metaverses: A Report on the International Parallel Driving Alliance

被引：18
作者：
Liu, Kunhua [1 ]
Li, Leixin [1 ]
Lv, Yisheng [2 ]
Cao, Dongpu [3 ]
Liu, Zhongmin [4 ]
Chen, Long [2 ]
机构：
[1] Sun Yat Sen Univ, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China
[2] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
[3] Tsinghua Univ, Sch Vehicle & Mobil, Beijing 100084, Peoples R China
[4] North Automat Control Technol Inst, Taiyuan 030000, Peoples R China
来源：
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE | 2022年 / 14卷 / 06期
基金：
中国国家自然科学基金;
关键词：
VEHICLES; MODEL;
D O I：
10.1109/MITS.2022.3202825
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：
On 27 June 2018, the International Parallel Driving Alliance (iPDA) inaugural conference was held in Changshu, China. The iPDA consists of 24 well-known institutions, e.g., the University of Cambridge, Purdue University Indianapolis, and the Royal Institute of Technology of Sweden. The iPDA aims to co-establish a common shared research platform for parallel driving and a timely exchange of the latest research results and data related to parallel driving. During the conference, participants discussed the definition, applications, and future challenges of parallel driving and generally agreed that it is a solution to the current autonomous driving problem Five keynote speakers presented parallel driving with intelligent vehicle theme talks to share their perspectives, field applications, and outlooks on industry trends and future research.
引用
收藏
页码：18 / 25
页数：8
相关论文
共 35 条
[1]   Learning Driving Models From Parallel End-to-End Driving Data Set [J].
Chen, Long ;
Wang, Qing ;
Lu, Xiankai ;
Cao, Dongpu ;
Wang, Fei-Yue .
PROCEEDINGS OF THE IEEE, 2020, 108 (02) :262-273
[2]   Parallel Motion Planning: Learning a Deep Planning Model Against Emergencies [J].
Chen, Long ;
Hu, Xuemin ;
Tang, Bo ;
Cao, Dongpu .
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE, 2019, 11 (01) :36-41
[3]   Parallel Planning: A New Motion Planning Framework for Autonomous Driving [J].
Chen, Long ;
Hu, Xuemin ;
Tian, Wei ;
Wang, Hong ;
Cao, Dongpu ;
Wang, Fei-Yue .
IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2019, 6 (01) :236-246
[4]   Dependent Randomization in Parallel Binary Decision Fusion [J].
Dong, Weiqiang ;
Kam, Moshe .
IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2021, 8 (02) :361-376
[5]   Parallel Driving in CPSS: A Unified Approach for Transport Automation and Vehicle Intelligence [J].
Wang, Fei-Yue ;
Zheng, Nan-Ning ;
Cao, Dongpu ;
Martinez, Clara Marina ;
Li, Li ;
Liu, Teng .
IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2017, 4 (04) :577-587
[6]   Semantic Monocular Depth Estimation Based on Artificial Intelligence [J].
Gurram, Akhil ;
Urfalioglu, Onay ;
Halfaoui, Ibrahim ;
Bouzaraa, Fahd ;
Lopez, Antonio M. .
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE, 2021, 13 (04) :99-103
[7]   From Software Defined Vehicles to Self-Driving Vehicles: A Report on CPSS-Based Parallel Driving [J].
Han, Shuangshuang ;
Cao, Dongpu ;
Li, Li ;
Li, Lingxi ;
Li, Shengbo Eben ;
Zheng, Nan-Ning ;
Wang, Fei-Yue .
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE, 2019, 11 (01) :6-14
[8]  
Han SS, 2017, IEEE INT VEH SYM, P20, DOI 10.1109/IVS.2017.7995693
[9]   Stability Control of Autonomous Ground Vehicles Using Control-Dependent Barrier Functions [J].
Huang, Yiwen ;
Yong, Sze Zheng ;
Chen, Yan .
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2021, 6 (04) :699-710
[10]   Target Vehicle Motion Prediction-Based Motion Planning Framework for Autonomous Driving in Uncontrolled Intersections [J].
Jeong, Yonghwan ;
Yi, Kyongsu .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 22 (01) :168-177
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