ABSTRACT
Section 10.1 talks about artificial intelligence (AI) techniques used in autonomous vehicles (AVs). It points out that as AVs become more common, society will need to confront a new set of risks which, for the first time, will include the ability of socially embedded forms of AI to make complex risk mitigation decisions with life and death consequences. The section explains that AI decisionality is inherently different from human decision-making processes, and society, policy, and end users need to understand these differences. While AI decisions can be contextualized to specific situations, significant challenges remain in terms of the technology of AI decisionality, the conceptualization of AI decisions, and the extent to which various actors understand them. This is particularly acute in terms of analyzing the benefits and risks of AI decisions. AVs are often presented as significant risk mitigation technologies, but there is also a need to understand the risks which an AV’s driving decisions may present, as artificial driving intelligence will necessarily lack certain decisional capacities. Thus, the section indicates the need to scrutinize how AV decisional capacity is conceptually framed and how this, in turn, impacts a wider grasp of the technology in terms of risks and benefits.
Section 10.2 analyzes the possibility of autonomous inspection and maintenance using AI with the objective of achieving more effective maintenance inspection. Section 10.3 mentions the application areas and current developments of AVs with AI in maintenance. These range from intelligent maintenance optimization models to more practical applications such as cost budgeting of maintenance projects and selecting optimal repair methods. The section gives an overview of the applications of AI techniques in maintenance, identifying specific applications and discussing the extent of their use. The chapter concludes with a discussion of recent trends in AI.
