Abstract
Due to the demand for safety and convenience in traveling, self-driving technology has developed very fast in the past decades. In this paper, a novel technology forecasting model is developed. The topic-based text mining and expert judgment approaches are combined to forecast the technology trends efficiently and accurately. To improve the reliability of the results, multidimensional information including scientific papers, patents, and industry data is considered. Then, the model is utilized to forecast the development trends of self-driving technology in China. Data ranging from 2002 to 2019 are adopted with proper data cleaning. Topic clustering for papers and patents is performed, and the hierarchical structures are constructed. On this basis, the results of technology’s evolution based on papers and patents are compared and the development trends are obtained. With these results, it is speculated that technology on “Decision” will be the next hotspot in patents. The research results of this paper will provide reference and guidance for Chinese enterprises and government in decision-making on self-driving technology.
Highlights
Different from traditional technologies, self-driving technology is usually considered as disruptive technology [8]
Technology forecasting methods can be divided into two categories depending on the degree of dependence on data: qualitative methods and quantitative methods [12]. e qualitative methods are usually based on the experience and knowledge of experts, which are time-consuming and subjective [13]
Statistical tools [14,15,16] and text mining tools [17,18,19] are widely used in technology forecasting, which can deal with massive raw data
Summary
Determine the Feasibility and Necessity of Technology Forecasting. Web of Science (WOS) and Derwent Innovations Index (DII) databases are adopted as the data sources for collecting scientific papers and patents, and the industry data are obtained from iiMedia (an authoritative economic data agency in China) [25]. Different search queries related to the target technology (self-driving) are constructed. E results are compared in order to determine the final search queries. Time ranging from 2000 to 2019 is adopted with the step of a year. To determine the feasibility and necessity of technology forecasting, growth curves of patent data, paper data, and industry data are constructed. E curves are compared with the S-curve in the technology life cycle to identify the technology life periods To determine the feasibility and necessity of technology forecasting, growth curves of patent data, paper data, and industry data are constructed. e curves are compared with the S-curve in the technology life cycle to identify the technology life periods
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