Cooperative Intelligence for Autonomous Driving

doi:10.12142/ZTECOM.201902007

ZTE Communications ›› 2019, Vol. 17 ›› Issue (2): 44-50.DOI: 10.12142/ZTECOM.201902007

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Cooperative Intelligence for Autonomous Driving

CHENG Xiang¹, DUAN Dongliang², YANG Liuqing³, ZHENG Nanning⁴

^1. State Key Laboratory of Advanced Optical Communication Systems and Networks, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
^2. Department of Electrical and Computer Engineering, University of Wyoming, Laramie, WY 82071, USA
^2. Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA
^2. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China

Received:2019-03-17 Online:2019-06-11 Published:2019-11-14
About author:CHENG Xiang (xiangcheng@pku.edu.cn) is currently a professor at Peking University, China. His general research interests are in areas of channel modeling, wireless communications and data analytics, subjects on which he has published more than 200 journal and conference papers, 5 books and 6 patents. Dr. CHENG was the recipient of the IEEE Asia Pacific (AP) Outstanding Young Researcher Award in 2015, the co-recipient for the 2016 IEEE JSAC Best Paper Award: Leonard G. Abraham Prize, the NSFC Outstanding Young Investigator Award, the both First-Rank and Second-Rank Award in Natural Science, Ministry of Education in China. He has also received the Best Paper Awards at IEEE ITST’12, ICCC’13, ITSC’14, ICC’16, ICNC’17, GLOBECOM’18, and ICCS’18. He has served as Symposium Leading-Chair, Co-Chair, and a Member of the Technical Program Committee for several international conferences. He is currently an Associate Editor for IEEE Transactions on Intelligent Transportation Systems and Journal of Communications and Information Networks, and an IEEE Distinguished Lecturer. He is a senior member of IEEE|DUAN Dongliang received the B.S. degree from Huazhong University of Science and Technology, China in 2006, the M.S. degree from the University of Florida, USA in 2009, and the Ph.D. degree from the Colorado State University, USA in 2012, all in electrical engineering. Since graduation, he joined the Department of Electrical and Computer Engineering at the University of Wyoming, USA and is now an associate professor. His research interests include signal processing techniques for wireless communications and power systems, estimation and detection theory, and energy resource management in wireless communication systems. He is a member of IEEE|YANG Liuqing received the Ph.D. degree from the University of Minnesota, USA in 2004. Her main research interests include communications and signal processing. Dr. YANG has been actively serving in the technical community, including the organization of many IEEE international conferences, and on the editorial boards of a number of journals, including the IEEE Transactions on Communications, the IEEE Transactions on Wireless Communications, the IEEE Transactions on Intelligent Transportation Systems, and the IEEE Transactions on Signal Processing. She received the Office of Naval Research Young Investigator Program Award in 2007, the National Science Foundation Career Award in 2009, the IEEE GLOBECOM Outstanding Service Award in 2010, the George T. Abell Outstanding Mid-Career Faculty Award and the Art Corey Outstanding International Contributions Award at CSU in 2012 and 2016 respectively, and Best Paper Awards at IEEE ICUWB’06, ICCC’13, ITSC’14, GLOBECOM’14, ICC’16, WCSP’16, GLOBECOM’18 and ICCS’18. She is a Fellow of IEEE|ZHENG Nanning received his B.S. degree from the Department of Electrical Engineering, Xi’an Jiaotong University, China in 1975, and M.S. degree in information and control engineering from Xi’an Jiaotong University in 1981, and Ph.D. degree in electrical engineering from Keio University, Japan in 1985. In 1975, he jointed Xi’an Jiaotong University, where he is currently a professor and the Director of the Institute of Artificial Intelligence and Robotics. His research interests include computer vision, pattern recognition and image processing, and hardware implementation of intelligent systems. Prof. ZHENG became a member of the Chinese Academy of Engineering in 1999, and he is the Chinese Representative on the Governing Board of the International Association for Pattern Recognition. He also is the President of the Chinese Association of Automation. He is a Fellow of IEEE
Supported by:
This work was in part supported by the Ministry National Key Research and Development Project under Grant(2017YFE0121400);The Major Project from Beijing Municipal Science and Technology Commission under Grant(Z181100003218007);The National Natural Science Foundation of China under Grants(61622101);The National Natural Science Foundation of China under Grants(61571020)

Abstract

Abstract:

Autonomous driving is an emerging technology attracting interests from various sectors in recent years. Most of existing work treats autonomous vehicles as isolated individuals and has focused on developing separate intelligent modules. In this paper, we attempt to exploit the connectivity among vehicles and propose a systematic framework to develop autonomous driving techniques. We first introduce a general hierarchical information fusion framework for cooperative sensing to obtain global situational awareness for vehicles. Following this, a cooperative intelligence framework is proposed for autonomous driving systems. This general framework can guide the development of data collection, sharing and processing strategies to realize different intelligent functions in autonomous driving.

Key words: autonomous driving, cooperative intelligence, information fusion, vehicular communications and networking

CHENG Xiang, DUAN Dongliang, YANG Liuqing, ZHENG Nanning. Cooperative Intelligence for Autonomous Driving[J]. ZTE Communications, 2019, 17(2): 44-50.

Figures/Tables 5

Figure 1. Illustration of different sensing strategies.

Figure 2. The information sharing among different entities.

Figure 3. The hierarchical information fusion procedure to obtain the global situational awareness.

Figure 4. Cooperative simultaneous localization and mapping with moving objects tracking (SLAM-MOT) using our proposed framework.

Figure 5. The illustration of our proposed cooperative intelligence framework.

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Cooperative Intelligence for Autonomous Driving

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