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Machine Learning and Place: the journey from the smart phone to the automated vehicle as experienced by the blind and partially sighted

Authors: Sofia Shwayri*,
Topics: Urban and Regional Planning, Human-Environment Geography
Keywords: Automated Vehicles, Machine Learning,Automation, Disable
Session Type: Paper
Presentation File: No File Uploaded

Inclusivity and accessibility are essential features for the emergence of a truly smart city. Despite the widespread efforts by governments to advance these through the use of Information and Communication Technologies infrastructure, success has been limited. The evolving nature of technology and potential to disrupt established urban processes constitutes both a challenge and an opportunity. In the last decade, Big Data honed by those interested in automating mundane activities gained momentum, with machine learning increasingly applied to address everyday urban challenges. From racing self-driving cars in the early-2000s, programmers and car manufacturers progressed to the automated vehicle (AV) on freeways and streets as a solution for congestion and safety. Though still in the nascent phase, local governments have opened up their cities to testing, at the same time formulating policies. As the software edges towards 100% safety, spatial domains and scales are being reimagined in ways that we are only just starting to comprehend; a process that, I argue, has been unfolding for over a decade with the launching of the smartphone and continues with the AV. To understand such a process, this paper will trace this journey through the experiences of the blind, from the adoption of the smartphone for opening up the virtual world, to the automated vehicle, as a platform for independent mobility. As governments continue to struggle with questions of autonomy and relevance, how will the development of AV’s and the urban environment be shaped by issues of accessibility and inclusivity for all citizens?

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