|
|
Technology Convergence Evolution of Artificial Intelligence and Internet of Vehicles Based on Patentometrics |
Jia Yiwei1,Qi Yong1,2,Wu Lanfen1 |
(1.School of Intellectual Property, Nanjing University of Science and Technology;2.School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China) |
|
|
Abstract With the rapid development of emerging technologies such as artificial intelligence and big data, the industry is transforming towards digitization and intelligence, and technology integration has become an important trend in the development of global technology and industrial innovation. The Internet of Vehicles is a typical high-tech complex involving artificial intelligence, big data, cloud computing and other technologies. Through the evolution research on the convergence characteristics of artificial intelligence and Internet of Vehicles technology, it is likely to more accurately identify the technical opportunities in the field of Internet of Vehicles industry, and provide direction guidance for the innovative and integrated development of artificial intelligence and Internet of Vehicles industry. At present, the academic community has carried out extensive research on the application of artificial intelligence in the field of Internet of Vehicles, but it is focused on the derivation and verification of specific technical solutions, and less involved in the technology convergence characteristics, trends of artificial intelligence and the Internet of Vehicles. Besides, there is a lack of application of text mining methods such as machine learning in technology convergence research based on patent data, and there are still certain limitations in technology convergence analysis only from the aspect of citation network, patent index or using patent generic information. Therefore, this paper adopts Word2vec text mining algorithm and social network analysis method to reveal the technological development trend in the fusion field, the correlation pattern of technical topics, the overall structure of the technology co-occurrence network and the evolution of node functions.#br#The research framework is built with the help of data analysis methods and technology convergence theory. Firstly, relevant patent data of artificial intelligence and Internet of Vehicles field from 2000 to 2019 are collected as the research object and the data are preprocessed to obtain information feature extraction. Next, the technology life cycle of the fusion field is divided into budding period, slow development period and rapid development period. Finally,the technical indicators such as patent application volume, technology convergence degree and technology similarity are analyzed from the three dimensions of technology convergence identification and measurement, technology theme association and technology co-occurrence network. In order to display the results of data analysis more intuitively, the convergence characteristics and evolution process of artificial intelligence and Internet of Vehicles technology are visualized in stages.#br#The results show that the development of technology convergence presents significant phasic characteristics, and the degree of technology convergence has been markedly deepened. In the field of Internet of vehicles, there are various artificial intelligence algorithms emerging and actively applied in each period, and the application scope of artificial intelligence in the field of Internet of Vehicles continues to expand. In response to users' diversified comprehensive service needs such as vehicle safety management, intelligent reception and travel arrangements, artificial intelligence has gradually been applied to the technical research of the Internet of Vehicles intelligent platform, which is an important turning point in the development of the Internet of Vehicles industry. Furthermore, the relationship between technical fields is getting closer, and the technology co-occurrence network is still relatively sparse. Data recognition and data representation, computer system based on specific computing model and other technologies have strong control ability in the process of convergence evolution. Specifically, the rapid development of technologies such as wireless communication networks, digital information transmission and signaling devices requires breakthroughs in related artificial intelligence technologies.#br#This paper innovatively proposes an identification framework for the convergence mode of artificial intelligence across the field of Internet of Vehicle,and it can be generally applied to other industrial field for predicting the trend of technology convergence. Different from the previous literature, this paper comprehensively uses text mining and network analysis methods to empirically analyze the impact of artificial intelligence algorithms on the development of Internet of Vehicles technology from multiple dimensions, and explore the most potential and promising artificial intelligence algorithms and their application scope. In addition, this paper discovers new technology opportunities for the application of artificial intelligence in the field of Internet of Vehicles, and puts forward enlightenment and suggestions from the aspects of R&D direction, discipline construction and platform construction.#br#
|
Received: 23 November 2021
|
|
|
|
|
[1]王宏起, 夏凡, 王珊珊. 新兴产业技术融合方向预测: 方法及实证[J]. 科学学研究, 2020, 38(6): 1009-1017, 1075.[2]CURRAN, CLIVE-STEVEN. The anticipation of converging industries[M]. London:Springer London, 2013.[3]ROSENBERG N. Technological change in the machine tool industry[J]. Journal of Economic History, 1963, 23(4): 414-443.[4]GILL T.Convergent products: what functionalities add more value to the base[J]. Journal of Marketing, 2008, 72(2): 46-62.[5]OETTINGER A G. Knowledge and power in the 21st century[J]. Science, 1980, 209(4452): 191-198.[6]PHILP J C, RITCHIE R J, ALLAN J. Biobased chemicals: the convergence of green chemistry with industrial biotechnology[J]. Trends in Biotechnology, 2013, 31(4): 219-222.[7]DURRESI M.(Bio)Sensor integration with ICT tools for supplying chain management and traceability in agriculture[J]. Comprehensive Analytical Chemistry, 2016, 74: 389-413.[8]MOORE GE. Cramming more components onto integrated circuits[J]. Proceedings of the IEEE, 1998, 86(1): 82-85.[9]陈钰芬, 王科平. 基于专利技术共现网络的人工智能跨领域融合模式识别[J]. 情报杂志,2021, 40(7): 8-15, 29.[10]KIM M, KIM C. On a patent analysis method for technological convergence[J]. Procedia-Social and Behavioral Sciences, 2012, 40(3): 657-663.[11]BHATIA J, DAVE R, BHAYANI H, et al. SDN-based real-time urban traffic analysis in VANET environment[J]. Computer Communications, 2020, 149(1): 162-175.[12]CHEN M, WANG T, OTA K, et al. Intelligent resource allocation management for vehicles network: an A3C learning approach[J]. Computer Communications, 2020, 151(2): 485-494.[13]ALOQAILY M, OTOUM S, RIDHAWI I A, et al. An intrusion detection system for connected vehicles in smart cities[J]. Ad Hoc Networks, 2019, 90(7): 101842.[14]BEDI P, MEWADA S, VATTI R A, et al. Detection of attacks in IoT sensors networks using machine learning algorithm[J]. Microprocessors and Microsystems, 2021, 82(4): 103814.[15]CALTAGIRONE L,BELLONE M,SVENSSON L,et al.LIDAR-camera fusion for road detection using fully convolutional neural networks[J]. Robotics and Autonomous Systems, 2019, 111(1): 125-131.[16]KHAN M N, AHMED M M. Trajectory-level fog detection based on in-vehicle video camera with TensorFlow deep learning utilizing SHRP2 naturalistic driving data[J]. Accident Analysis & Prevention, 2020, 142(7): 105521.[17]HAO H, MA W, XU H. A fuzzy logic-based multi-agent car-following model[J]. Transportation Research Part C: Emerging Technologies, 2016, 69(8): 477-496.[18]HU J, HUANG M C, YU X. Efficient mapping of crash risk at intersections with connected vehicle data and deep learning models[J]. Accident Analysis & Prevention, 2020, 144(9): 105665.[19]KANAPRAM D T, MARIN-PLAZA P, MARCENARO L, et al. Self-awareness in intelligent vehicles: feature based dynamic Bayesian models for abnormality detection[J]. Robotics and Autonomous Systems, 2020, 134(12): 103652.[20]SHAKARAMI A, SHAHIDINEJAD A, GHOBAEI-ARANI M. An autonomous computation offloading strategy in mobile edge computing: a deep learning-based hybrid approach[J]. Journal of Network and Computer Applications, 2021, 178(3): 102974.[21]MOHAMMADNAZAR A, ARVIN R, KHATTAK A J. Classifying travelers′ driving style using basic safety messages generated by connected vehicles: application of unsupervised machine learning[J]. Transportation Research Part C: Emerging Technologies, 2021, 122(1): 102917.[22]王玏, 吴新年. 新兴技术识别方法研究综述[J]. 图书情报工作, 2020, 64(4): 125-135.[23]吴晓燕, 胡雅敏, 陈方. 基于专利共类的技术融合分析框架研究——以合成生物学领域为例[J]. 情报理论与实践, 2021, 44(10): 179-184.[24]PARK I, YOON B. Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network[J]. Journal of Informetrics, 2018, 12(4): 1199-1222.[25]HAN E J, SOHN S Y. Technological convergence in standards for information and communication technologies[J]. Technological Forecasting and Social Change, 2016, 106: 1-10.[26]王友发, 张茗源, 罗建强,等. 专利视角下人工智能领域技术机会分析[J]. 科技进步与对策, 2020, 37(4): 19-26.[27]翟东升, 张京先. 基于专利技术共现网络的无人驾驶汽车技术融合演化研究[J].情报杂志, 2020, 39(4): 60-66, 19.[28]刘颖琦, 周菲, 席锐. 后疫情时期中国智能网联汽车产业技术研究与合作网络: 国际专利视角[J]. 中国科技论坛, 2021, 37(5): 32-45, 66.[29]LEE M, HE G. An empirical analysis of applications of artificial intelligence algorithms in wind power technology innovation during 1980-2017[J]. Journal of Cleaner Production, 2021, 297(2): 126536.[30]吕一博, 韦明, 林歌歌. 基于专利计量的技术融合研究: 判定、现状与趋势——以物联网与人工智能领域为例[J]. 科学学与科学技术管理, 2019, 40(4): 16-31.[31]BRESCHI S, LISSONI F, MALERBA F. Knowledge-relatedness in firm technological diversification[J]. Research Policy, 2003, 32(1): 69-87.[32]MIKOLOV T, CORRADO G, KAI C, et al. Efficient estimation of word representations in vector space[C]. Proceedings of the International Conference on Learning Representations (ICLR 2013), 2013.[33]逯万辉, 谭宗颖. 基于深度学习的期刊分群与科学知识结构测度方法研究[J].情报学报, 2020, 39(1): 38-46.[34]FENG S, AN H, LI H, et al. The technology convergence of electric vehicles: exploring promising and potential technology convergence relationships and topics[J]. Journal of Cleaner Production, 2020, 260(7): 120992. |
|
|
|