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The Artificial Intelligence (AI) is a "program with human intelligence" (Naoki Mitsumura, 2018). It must be able to perform tasks as humans do and have the same consciousness as humans. In recent years, Artificial Intelligence has become a prominent industry application in the filed such as finance, insurance, manufacturing, transportation, urban security, hotels and restaurants, transportation and storage, retail, medical, entertainment, real estate, agriculture, mining, education, energy , administration, etc. However, the actual implementation of products and services still needs to meet market demand or create new commercial value. Currently, the main application of AI is to simulate and perform specific functions, focusing on replacing repeatable field tasks, data collection and analysis, and general communication (with low emotional level involved). Through deep learning and increasingly big data collection and analysis, technology is moving towards the next stage of "understanding things and phenomena", "capturing language meaning", and "ability to solve things".
In the past, video surveillance was limited by the computing power of terminal equipment. Most data analysis is relied on cloud computing. The transmission of large amount image data was restricted by the network bandwidth, so that real-time image analysis capabilities were also limited. The recommended real-time feedback action is even unfeasible . With the improvement of hardware computing power, network transmission bandwidth and the large progress on artificial intelligence algorithms, the false alarms and malfunctions criticized by the surveillance industry in the past have been greatly improved. In the past, commercial vehicles used different operation systems for video surveillance and GPS route tracking. Recently, in response to market demand, they have gradually integrated into a single operation system. Commercial vehicles focus on improving the cost efficiency of operations, reducing driving risks, and improving vehicle safety and reliability. It is required for immediate feedback for driving safety to reduce risks. With integration of Artificial Intelligence, Internet of Things(IoT) (ADAS, surveillance cameras, etc. are also classified as IoT), 5G transmission, and substantially increasing in hardware computing power, it is feasible to achieve real-time monitoring and action feedback to meet commercial vehicles requirements, and even develop new applications to generate new business models.
The research uses the innovation diffusion theory, customer value model, technology versus market matrix and other related literature to explore, to form the basis of the research theory and research tools of the research. The research method is to conduct interviews and questionnaire surveys with upstream and downstream in the commercial vehicle value chain to understand the short, medium and long-term perceptions of market demand and expectation on the supply side and demand side. Then analyze the challenges faced by artificial intelligence in the commercial vehicle industry and the opportunities for future development.
The study found that the introduction of artificial intelligence into the commercial vehicle industry is at the stage of innovative adoption. The supply side and the demand side have the same long-term expectations for the introducing artificial intelligence into the market. However, the perceptions of short-term demand on both sides are quite different. Especially, on the expected cost and function, variant demands of fragmented vertical markets, and the lack of artificial intelligence research and development capability, national regulations and market safety considerations are the main factors caused to low market penetration of AI. This research uses the technology market matrix and customer value model to analyze the challenges and difficulties faced by the adoption of new products/technologies in the market in terms of innovation diffusion theory and practice, and explores the market gap that needs to be overcome from the initial stage of innovative adoption to the early adoption stage. No matter commercial self-driving car or a commercial vehicle equipped with artificial intelligence devices, it must meet the short-term needs of the market to improve driving safety and operational efficiency. Then it is possible for artificial intelligence to enter the early adoption stage, develop new markets and create new business model in the long-term.
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