With the development of science and technology, digital technology is gradually applied in all walks of life to improve efficiency and function. The automobile industry is also involved in this digital transformation, and the networked automobile is the product of automobile digitalization. Vehicle to Cloud (V2C), Vehicle to Infrastructure (V2I), and Vehicle to Vehicle (V2V) technologies emerging in digitalization constantly improve people's daily travel, but there is also a problem of low popularity. In order to deal with this problem, this paper reasonably assumes that the existing methods and technologies will be integrated into a system. Since the number of road accidents is still numerous, the V2V communication technique needs to be adopted in a brand-new driving system that is compulsory to be installed into every car. The driving system is supported by three methods: sensors, cloud computing, and 5G. Those methods can provide functionalities like information transmissions or data collection. Market situations of these three main techniques are analyzed in this article, and data shows substantial and increasing demands in those markets by estimated Compound Annual Growth Rate (CAGR) and market sizes. In addition, issues of private security and the future of intelligent car technology will also be discussed in this paper.
Conditional Random Field (CRF) has been applied widely in information extraction and natural language processing. However, according to corpus types, it has not been made much use of on corpus about science and technology declarations. In this paper, we extract word-level information from amounts of science and technology announcements corpus, and analyze the performance of CRF, comparing with Naïve Bayes as a baseline. According to our experiments, we show that CRF has much high precision except for a few unknown data. Also, Naïve Bayes model is satisfactory in closed domains, but it always makes mistakes when the data belong to a less weighted class.
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