Do Autonomous Vehicles Dream of Virtual Sheep? The Displacement of Reality in the Hyperreal Visions of Autonomous Vehicles

Wigley, Edward (2021). Do Autonomous Vehicles Dream of Virtual Sheep? The Displacement of Reality in the Hyperreal Visions of Autonomous Vehicles. Annals of the American Association of Geographers (Early Access).

DOI: https://doi.org/10.1080/24694452.2020.1838256

Abstract

Autonomous vehicles (AVs) have received a great deal of attention in recent years, with many commentators asking how these vehicles will “see” to navigate themselves and, more important, avoid colliding with people, other vehicles, and objects. This article analyzes how AVs see and the data sets they create through Jean Baudrillard’s framework of simulation and simulacra, paying attention to how these technologies purify and classify the real into a series of representations. In doing so, it extends the existing critique of data-generating “smart” technologies and automation and additionally draws on visual theory to understand how the hyperreality of AVs is constructed. Technologies employed on AV trials such as light detection and ranging videos and virtual reality produce data sets that create a version of the urban environment that then become the model for decisions regarding the operations and management of the city. Yet, as Baudrillard theorized, such models risk being mistaken for the reality that they represent and the data set itself confusing the nature–culture divide and created hybrid geographies of the city. As a simulacrum that operates on its own underlying logic, the article argues that this version of seeing or visioning the world risks rendering it as an object of scrutiny for state and nonstate actors that override the realities and people of the city. Indeed, techno-visions and testing of AVs can lead to situations in which reality itself is manipulated to conforming with the simulation, further complicating the simulacra of the urban environments in which AV testing takes place.

Viewing alternatives

Metrics

Public Attention

Altmetrics from Altmetric

Number of Citations

Citations from Dimensions

Item Actions

Export

About

Recommendations