Understanding the long-term emergence of autonomous vehicles technologies

Identifying emerging technologies has been of long-standing interest to many scholars and practitioners. Previous studies have introduced methods to capture the concept of emergence from bibliographic records, including the recently proposed Technology Emergence Indicator (Carley et al. 2018). This indicator method has shown to be applicable to various technological fields. However, the indicator uses a limited time window, which can overlook the potential long-term evolution of emerging technologies. Moreover, the existing method suffers from interpretability, because it can be difficult to understand the context in which identified emerging terms are used. In this paper, we propose an improved version of the Technology Emergence Indicator that addresses these issues. In doing so, we examine emerging topics within the field of autonomous vehicles technologies during the period of 1991-2018, guided by a proposition about the long-term diffusion of an emerging technology topic. The results show that different autonomous vehicle technology topics emerge during each of the three 10-year periods under analysis, including an initial period of understanding the surrounding environment and path planning, a second period marked by DARPA Grand Challenge motivated factors associated with the urban environment and communication technologies, and a third period relating to machine learning and object detection. This association with certain emerging technology topics in each decade is also characterized by different trajectories of continued or cyclical carryover across the decades. The results suggest a methodology that practitioners can use in examining research areas to understand which topics are likely to persist into the future.


Author(s): Seokkyun Woo, Jan Youtie,Ingrid Ott, Fenja Scheu
Organization(s): Georgia Institute of Technology, Karlsruhe Institute of Technology
Source: Technological Forecasting and Social Change
Year: 2020

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