Autonomous vs. Self-Driving Vehicles: The Power of Language to Shape Public Perceptions

ABSTRACT

Public perception of the next generation of vehicles will affect their design, deployment, and ultimately their use. As the engineering terms of self-driving, driverless, fully automated, and autonomous were introduced to the social sciences and the public at large, the subtle differences among these terms have been lost. However, using them as synonyms even though they may not be interchangeable is problematic. To explore the semantics of different future vehicle terms we surveyed 963 Michigan residents on their understanding of “autonomous” and “self-driving.” We found significant differences in perceptions between the terms autonomous and self-driving vehicles. While the former invokes many more uncertain responses, the latter is laden with concerns. These results suggest that the language used to describe the next generation of vehicles may shape public reaction and acceptance. As new mobility options are introduced to the public, our understanding of them will be shaped, in part, by the language used to name and explain the technology. Far from being inconsequential, word choice plays a major, yet underappreciated, role in shaping public opinion.

KEYWORDS:

• Autonomous
• self-driving
• driverless
• automated
• semantics
• public opinion

Acknowledgments

The survey research was funded by the Center for Business and Social Analytics and the Institute for Public Policy and Social Research at Michigan State University.

Notes

1 We had to choose between the terms “autonomous,” “self-driving,” “automated,” and “driverless” vehicle to write about them in our article. We chose autonomous vehicles (AVs), because it is the most frequently used term in academia.

2 The methodological report and data of SOSS is available at http://ippsr.msu.edu/survey-research/state-state-survey-soss/soss-data/soss-75-fall-2017

Additional information

Notes on contributors

Eva Kassens-Noor

Eva Kassens-Noor is an associate professor in the School of Planning, Design, and Construction at Michigan State University focusing in her research on artificial intelligence, extreme events, and urban transformations.

Mark Wilson

Mark Wilson is professor of urban and regional planning in the School of Planning, Design and Construction at Michigan State University, and also serves as program director for the PhD in Planning, Design and Construction. Research and teaching interests address urban planning, disruptive technologies, mega-events, and economic development.

Meng Cai

Meng Cai is a PhD student in Planning, Design and Construction with a concentration on urban and regional planning at Michigan State University.

Noah Durst

Noah Durst is an assistant professor of urban and regional planning at Michigan State University. Durst employs mixed methods - both quantitative and qualitative - to examine the intended and unintended effects of planning and policy-making.

Travis Decaminada

Travis Decaminada is a graduate of MSU Master in Urban and Regional Planning Program, and an aspiring academic. His research interests include autonomous mobility and resilience against extreme events.