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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (2/3): 55-66    DOI: 10.11925/infotech.2096-3467.2021.0926
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Identifying Emerging Technology with LDA Model and Shared Semantic Space——Case Study of Autonomous Vehicles
Zhou Yunze,Min Chao()
School of Information Management, Nanjing University, Nanjing 210023, China
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Abstract  

[Objective] The paper proposed a new method to identify emerging technologies using shared semantic model and multi-source data. [Methods] We used the LDA model to detect the topics of multi-source data. Then, we utilized the Word2Vec model to create vectors for these topics based on the representative words and their weights. Third, we merged the topics, and used topic strength and novelty to identify emerging technologies. [Results] We found seven emerging technoligies from the field of Autonomous Vechicles, including Driver Switching, Selection and Control of Travel Path, Lane Change Safety, Motion Estimation and Risk Aversion, Structure Design, Perception of the Environment, as well as Communication Technology and Communication Security. [Limitations] More research is needed to explore better ways to determine the threshold and find fine-grained topics. [Conclusions] The proposed method is able to detect emerging topics using data from multiple sources, which optimizes the exisiting methods.

Key wordsIdentification of Emerging Technology      Latent Dirichlet Allocation      Autunomous Vehicles      Semantic Space      Word2Vec     
Received: 28 August 2021      Published: 14 April 2022
ZTFLH:  TP393  
Fund:Social Science Fund of Jiangsu Province(18TQC005);Fundamental Research Funds for the Central Universities(14380005)
Corresponding Authors: Min Chao,ORCID:0000-0002-0627-995X     E-mail: mc@nju.edu.cn

Cite this article:

Zhou Yunze, Min Chao. Identifying Emerging Technology with LDA Model and Shared Semantic Space——Case Study of Autonomous Vehicles. Data Analysis and Knowledge Discovery, 2022, 6(2/3): 55-66.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0926     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I2/3/55

Research Framework
The Number of Papers and Patents with Time
The Classification Criteria of Topics
主题 类别
仅存在于论文中的主题 理论端新兴技术主题、理论端具有潜力的主题、理论端较为成熟的主题、理论端不确定的主题等4个种类
仅存在于专利中的主题 应用端新兴技术主题、应用端具有潜力的主题、应用端较为成熟的主题、应用端不确定的主题等4个种类
论文和专利中共同存在的主题 理论端新兴技术主题且应用端新兴技术主题、理论端新兴技术主题且应用端具有潜力的主题、理论端新兴技术主题且应用端较为成熟的主题等16个种类
The Classification of Topics
The Coherence of WOS Data in Different Topic Numbers
The Coherence of DII Data in Different Topic Numbers
主题名称 对应的最相关词汇及概率
WOS-1 vehicle(0.047) driver(0.031) automate(0.026)
drive(0.022) study(0.018) autonomous(0.017)
car(0.016) safety(0.014) human(0.013)
technology(0.012)
WOS-2 control(0.062) vehicle(0.057) autonomous(0.026)
model(0.024) base(0.022) propose(0.019) controller(0.018) path(0.018) trajectory(0.017) method(0.015)
WOS-9 autonomous(0.065) system(0.051) vehicle(0.039)
environment(0.023) test(0.016) paper(0.016) design(0.015) real(0.014) base(0.012) implementation(0.012)
The Most Related Words and Their Probability of WOS-1 to WOS-9
主题名称 对应的最相关词汇及概率
DII-1 method(0.080) determine(0.063) step(0.061) vehicle(0.053) base(0.038) involve(0.035) action(0.024)
motion(0.019) autonomous(0.018) risk(0.015)
DII-2 vehicle(0.094) route(0.058) location(0.050) road(0.047) lane(0.041) traffic(0.035) method(0.031)
determine(0.031) travel(0.027) autonomous(0.019)
DII-12 vehicle(0.090) autonomous(0.056) user(0.036) network(0.026) service(0.026) communication(0.022) method(0.021) receive(0.018) request(0.017) server(0.016)
The Most Related Words and Their Probability of DII-1 to DII-12
DII主题

WOS主题
WOS-1 WOS-2 WOS-3 WOS-4 WOS-5 WOS-6 WOS-7 WOS-8 WOS-9
DII-1 0.662 0.872 0.764 0.684 0.871 0.607 0.664 0.576 0.836
DII-2 0.663 0.762 0.633 0.841 0.736 0.574 0.680 0.786 0.767
DII-3 0.831 0.911 0.555 0.738 0.877 0.468 0.824 0.671 0.939
DII-4 0.459 0.487 0.295 0.422 0.509 0.332 0.624 0.353 0.453
DII-5 0.639 0.743 0.429 0.602 0.786 0.488 0.824 0.506 0.912
DII-6 0.915 0.798 0.625 0.728 0.718 0.442 0.707 0.697 0.722
DII-7 0.186 0.467 0.403 0.154 0.602 0.559 0.549 0.243 0.477
DII-8 0.596 0.781 0.491 0.565 0.842 0.521 0.776 0.508 0.806
DII-9 0.484 0.736 0.636 0.493 0.908 0.734 0.678 0.445 0.845
DII-10 0.235 0.509 0.503 0.272 0.736 0.581 0.423 0.308 0.572
DII-11 0.593 0.704 0.405 0.451 0.690 0.348 0.686 0.459 0.672
DII-12 0.749 0.715 0.468 0.751 0.802 0.599 0.899 0.560 0.853
Similarity Between Paper Topics and Patent Topics
主题 主题强度 主题新颖度
WOS-1 851.647 2 017.480
WOS-2 742.136 2 016.700
WOS-3 289.145 2 012.600
WOS-4 283.708 2 014.320
WOS-5 404.021 2 015.700
WOS-6 178.103 2 016.260
WOS-7 204.402 2 013.640
WOS-8 304.254 2 017.380
WOS-9 601.317 2 012.740
The Topic Strength and Topic Novelty of Paper Topics
主题 主题强度 主题新颖度
DII-1 314.193 2 017.320
DII-2 309.766 2 016.600
DII-3 662.714 2 016.340
DII-4 389.802 2 017.600
DII-5 308.398 2 016.460
DII-6 195.374 2 017.480
DII-7 251.033 2 017.460
DII-8 239.167 2 015.860
DII-9 564.597 2 017.120
DII-10 220.184 2 016.840
DII-11 291.068 2 016.160
DII-12 518.442 2 017.780
The Topic Strength and Topic Novelty of Patent Topics

主题强度

主题新颖度
WOS-1,WOS-2,WOS-5,
WOS-8,DII-1,DII-4,DII-9,DII-12
WOS-9,DII-2,DII-3
WOS-6,DII-6,DII-7 WOS-3,WOS-4,WOS-7,DII-5,DII-8,DII-10,DII-11
The Classification Result of Topics
等级 名称 定义
L0 无自动化 系统无任何辅助
L1 驾驶支援 系统对(1)方向盘、(2)加减速中的一项提供驾驶支援
L2 部分自动化 系统对(1)方向盘、(2)加减速中的多项提供驾驶支援
L3 有条件自动化 系统完成所有驾驶操作,驾驶者需根据系统请求,提供适当应答
L4 高度自动化 在一定的道路和环境下,系统完成所有驾驶操作,驾驶者不一定要对系统请求提供应答
L5 完全自动化 在所有的道路和环境下,系统完成所有人类驾驶者可以完成的驾驶操作
SAE Automation Levels[33]
主题 类型 命名 阐释
WOS-6 理论端具有潜力的主题 性能提升 对自动驾驶汽车在识别汽车转向行为、寻找最优停车位等操作上提出模型,以提升其性能,研究者常选取早高峰时间段的交通作为研究样例
DII-7 应用端具有潜力的主题 光信号的发出与接收 通过发出光信号(通常为激光信号)、检测和接受光信号,自动驾驶汽车可以对周遭环境进行识别
WOS-3 理论端不确定的主题 混合车流控制 当多辆自动驾驶汽车在同一路段行驶时,即构成汽车排(Vehicle Platoons)。该主题探讨在车流中存在不同比例的自动驾驶汽车时,如何通过调控,使车流运行高效、安全
DII-5 应用端不确定的主题 信息计算 以历史信息、感知环境信息等作为输入,进行计算
DII-8 应用端不确定的主题 目标检测设备 检测到周围的目标主体(如其余的自动驾驶汽车),并准确定位其位置
DII-10 应用端不确定的主题 环境捕获与图像识别 使用车载相机,对环境进行捕获,存储为图像,并对图像进行识别
DII-11 应用端不确定的主题 控制模块 自动驾驶汽车的控制模块设计(通常包含设计图)
Classification of Non-Emerging Research Topics
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