From senses to sensors: autonomous cars and probing what machine learning does to mobilities studies

Dalia Mukhtar-Landgren and Alexander Paulsson, Distinktion: Journal of Social Theory, 2023

Cars are nowadays being programmed to learn how to drivethemselves. While autonomous cars are often portrayed as thenext step in the auto-motive industry, they have already begunroaming the streets in some US cities. Building on a growingbody of critical scholarship on the development of autonomouscars, we explore what machine learning is in open environmentslike cities by juxtaposing this to thefield of mobilities studies. Wedo  so  by  revisiting  core  concepts  in  mobilities  studies:movement,  representation  and  embodied  experience.  Ouranalysis of machine learning is centred around the transition fromhuman senses to sensors mounted on cars, and what this impliesin terms of autonomy. While much of the discussions related tothis transition are already foregrounded in mobilities studies, dueto thisfield’s emphasis on complexities and the understanding ofautomobility as a socio-technological system, questions aboutautonomy still emerge in a slightly new light with the advent ofmachine learning. We conclude by suggesting that in mobilitiesstudies, autonomy has always been seen as intertwined withtechnology,  yet  we  argue  that  machine  learning  unfoldsautonomy as intrinsic to technology, as the space between thecar, the driver and the context is collapsing with autonomous cars.

📂 From senses to sensors: autonomous cars and probing what machine learning does to mobilities studies
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