Understanding the long-term emergence of autonomous vehicles technologies
Understanding the long-term emergence of autonomous vehicles technologies
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DOI：
10.1016/j.techfore.2021.120852
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发表时间：
2021-09
影响因子：
12
通讯作者：
Seokkyun Woo;J. Youtie;Ingrid Ott;Fenja Scheu
中科院分区：
文献类型：
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作者：
Seokkyun Woo;J. Youtie;Ingrid Ott;Fenja Scheu
识别新兴技术一直是许多学者和实践者感兴趣的问题。以前的研究已经介绍了从书目记录中捕捉涌现概念的方法，包括最近提出的技术涌现指标(Carley等人。2018年)。这种指示法已被证明适用于各种技术领域。然而，该指标使用了有限的时间窗口，这可能会忽视新兴技术潜在的长期演变。此外，现有方法存在可解释性的问题，因为它可能难以理解所确定的新兴术语使用的背景。在这篇文章中，我们提出了一种改进的技术涌现指标来解决这些问题。在这样做的过程中，我们在一个关于新兴技术主题的长期扩散的命题的指导下，研究了1991-2018年期间自动驾驶汽车技术领域的新兴主题。结果表明，在分析的三个10年期间，每个阶段都出现了不同的自动驾驶汽车技术主题，包括了解周围环境和路径规划的初始阶段，与城市环境和通信技术相关的DARPA大挑战激励因素的第二阶段，以及与机器学习和目标检测有关的第三阶段。这种与每个十年中的某些新兴技术主题的联系，也具有在几十年中持续或周期性延续的不同轨迹。这一结果表明，从业者可以使用一种方法来检查研究领域，以了解哪些主题可能会持续到未来。
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.