Navigating AI–nature frictions: Autonomous vehicle testing and nature-based constraints

In cities, the application of artificial intelligence (AI) is being directed towards transforming different aspects of urban life. These applications take material form in urban spaces, with autonomous vehicles (AVs) providing a prominent example. AI systems rely on large volumes of data on their surroundings to refine the algorithms and enhance the accuracy of prediction for operational efficiency and safety. However, such algorithmic learning and execution can present challenges when dealing with the unpredictable, complex, and dynamic aspects of urban spaces. Nature is a paradigmatic example of such unpredictability, because natural phenomena usually defy consistent patterns and precise data-based modeling. This paper introduces the idea of “frictional urbanisms” to examine the tensions between the smooth operational demands of AI and the inherent roughness of urban environments. In Southeast Asia, Singapore stands out as a first mover in smart city innovation. Despite Singapore's reputation as a regional and global leader in digital transformation, the testing of AVs has faced considerable challenges, particularly due to nature-based factors. By drawing on semistructured interviews with diverse stakeholders in the AI and AV testing landscape in Singapore, the paper shows how these challenges manifest in practice and examines their broader implications in and for the field of AI urbanism. Our study reveals that integration of AI in urban spaces is fundamentally shaped by persistent frictions, which are not exceptional circumstances but constitutive of everyday urban life in autonomizing cities.