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| Title | Artificial Intelligence |
|---|---|
| ID_Doc | 10386 |
| Authors | Guo Z.; Yu K. |
| Year | 2022 |
| Published | Internet of Things |
| DOI | http://dx.doi.org/10.1007/978-3-030-92054-8_3 |
| Abstract | The Autonomous transportation systems will become the mainstream in the future; it is particularly important to design effectively automatic control modules. This calls for the algorithms with highly adaptive ability of perception and computation. By actively interacting with the surroundings, optimal control decision strategies can be automatically calculated. Although research efforts have been devoted to this domain for many years, the general mathematical optimization methods can only find the approximated solutions inside systems, which makes the performance of the transportation systems difficult to achieve perfectly optimal. In this context, introduction of intelligent technologies represented by artificial intelligence has been regarded as a promising perspective. Artificial intelligence (AI), a branch of computer science, attempts to understand the essence of intelligence and simulates a novel intelligent machine that can respond in the ways similar to human intelligence. Since the birth of AI, its theory and technology have become increasingly mature, expanding applications in many fields such as robotics, language recognition, image recognition, natural language processing, and expert systems. It can be imagined that the scientific and technological products brought by artificial intelligence in the future will be the “container” of human intelligence. Generalized into transportation systems, the AI abstracts the complex system processes as black boxes and then uses the idea of statistical learning to model the complex system processes. By discovering potential and unobservable patterns, the AI provides more opportunities to solve the uncertainty problems hidden in the transportation systems. Predictably, the AI technology will be the key breakthrough in the evolution from conventional transportation systems to autonomous transportation systems. Therefore, this chapter is organized via three aspects of contents: (1) Overview of AI; (2) Need and Evolution of AI; and (3) AI for Transportation Systems. In this chapter, the wide AI conception is described by dividing it into three branches: cognitive AI, machine learning AI, and deep learning AI. Further, the need and evolution of AI are surveyed via three parts. The first part describes origin and development of AI, the second part describes common approaches and technologies about AI, and the third part describes successive cross-field applications of AI. Finally, application of AI for transportation systems is introduced via three parts: motivation, application status, and challenges. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
| Author Keywords | Artificial intelligence; Artificial neural networks; Autonomous transportation system; Cognitive AI; Computer vision; Deep learning AI; Expert systems; Human intelligence; Image recognition; Language recognition; Machine learning; Machine learning AI; Natural language processing; Reinforcement learning; Semi-supervised learning; Smart care; Smart city; Smart education; Smart manufacturing; Smart workflow; Supervised learning; Transportation systems; Unsupervised learning |
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