UCLA

As autonomous vehicle companies like Waymo become increasingly commonplace on the streets of cities around the US, greater attention must be paid to the impacts these vehicles have on the spaces they navigate through. While autonomous vehicle deployment is relatively recent, much of the scholarly and political discussions remain focused on studying their relative road safety, user experience, and the potential for transformation of the transportation sector. Little attention, however, has been paid to the surveillance capacity of these vehicles and the wider implications this could have on our cities. As urban surveillance practices create and reinforce racial, as well as class, gender, sexuality, and location boundaries and hierarchies, attention to the ways that new technologies are designed and enabled to produce and reproduce these dynamics is critical. In this thesis, I conduct empirical research to examine the capacity of autonomous vehicles for their unique and comprehensive urban surveillance practices based upon the types of data collected, the method of collection, and whether/how that collection is regulated. This thesis seeks to broaden an understanding of the ways that autonomous vehicles extract and capture urban spatial data through its navigation in a moment where autonomous vehicles are still a novel and vastly misunderstood technology. I approach this from a lens of concern for the potential for privacy violations and increased street-level surveillance of urban neighborhoods and an awareness of the potential for urban transformation at the hands of the widening deployment of autonomous vehicles within a context of the descent into global fascism supported in large part by the advancement of automated technologies and artificial intelligence. I am using Google-owned Waymo autonomous vehicles as a case study to help answer these questions.