The development of autonomous driving technology: perspectives from patent citation analysis

Rico Lee-Ting Cho, John S. Liu and Mei Hsiu-Ching Ho

Transport Reviews, 2021, vol. 41, issue 5, 685-711

Abstract: Autonomous vehicles have been widely discussed recently due to the rapid advancement of related technologies and high growth potential in many markets around the world. To take a strategic position for coping with their fast development, business strategists have to find out the key players and how these players share technological information in the knowledge network. This research particularly explores the main technologies that have been developed and applied in this field by conducting a systematic and quantitative analysis to answer these questions. We analyse U.S. patents related to autonomous driving technology in a systematic manner through cross-citation analysis (CCA) and main path analysis (MPA). The former reveals knowledge flow among major players, while the latter uncovers the technology development trajectory and the associated players. The results of CCA indicate that several companies (e.g. Google and GM) in the U.S. utilise locational advantage to build an industrial chain and to facilitate knowledge exchange. MPA results reveal a development trajectory and the key players at different development phases. Our analysis infers four trends. First, more solutions in communication systems will be patented in the future as communication is crucial to realise vehicle-to-everything. Second, perception technologies are integrating with artificial intelligence to enhance vehicle autonomy. Third, players with roles as “technology developer”, “technology integrator”, and “technology implementer” in different development phases are together advancing autonomous driving technologies. Fourth, traditional vehicle makers are expected to strengthen their cooperation with Information and Communication Technology (ICT) companies for the purpose of obtaining communication and data technologies.

Date: 2021
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Citations: View citations in EconPapers (7)

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DOI: 10.1080/01441647.2021.1879310

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