CONCLUSIONS

This paper has been a case study of the challenges and successes of a small social science research group in an engineering laboratory dedicated to the development of autonomous car. We have shown how we are attempting to make an impact on the design of the automobile of the future by considering the social organization of road use, with the ultimate goal of helping create socially acceptable autonomous vehicles.

The focus of our lab is on the software for the AV to drive in city traffic. We have struggled to have a direct impact on this software development, in part because our observations of traffic often hinge on the recognition that road users make decisions based on their own social assessment of other road users and their intentions. While we have been successful to present these observations to the engineers in a compelling manner, it is far from obvious what the implications of our research should be given the limitations of the sensory systems of AVs that cannot detect the social world of traffic in all the subtle detail road users do. It is therefore no surprise that the engineering teams have a tendency to set aside our observations as compelling yet rather irrelevant, unless they can be translated into state-based diagrams, the onus for the production of which they put on us. This is challenging for several reasons, among them that the production of formal models has not been the focus of our education, that the formal models are difficult to produce without intimate knowledge of the relevant categories of the AVs software—what social actions can it recognize—and that formal models very quickly become only a poor, watered-down representation of the richness of the social world.

Nevertheless, our ethnographically inspired studies of traffic and road users have born some fruit. Not only have they resulted in actual hardware on Nissan’s concept autonomous vehicle, but our research has also led to various observations that are becoming part of how the organization talks about autonomous vehicles, both within the company and to the public. We may indeed help redefine and challenge some of the fundamental categories that may seem natural from an engineering perspective, but have limited use when considered against a backdrop of the actual social reality of the methods people use to navigate the city streets. However, the interest and desire to integrate understandings of the social nature of road use into the design of autonomous vehicles remains high within the organization. That work is challenging, but it is a challenge we accept eagerly.

Erik Vinkhuyzen is a Senior Researcher at Nissan Research Center in Silicon Valley and specializes in video-based studies of people and technology. The focus of his current research is driving, walking and bicycling in city and suburban environments, with the goal of identifying social practices that can aid in the development of socially acceptable autonomous vehicles. Before joining Nissan Erik was a principal researcher at the Xerox Palo Alto Research Center (PARC). He also worked at NASA Ames Research Center, and the Institute for Research on Learning (IRL). He received his Ph.D. in cognitive psychology from the University of Zurich.

Melissa Cefkin is a Principal Scientist & Design Anthropologist at Nissan Research in Silicon Valley where she explores the potential of having autonomous vehicles as interactive agents in the world. Her work is at the intersection of ethnographic and anthropological research and the worlds of business, design, and technical system development. Melissa is the author of numerous publications including the Ethnography and the Corporate Encounter (editor, Berghahn Books 2009) and served in a wide range of the leadership roles, including president and conference co-chair, for the EPIC (Ethnographic Praxis in Industry Conference) organization. She worked previously at IBM Research, Sapient and the Institute for Research on Learning (IRL).

2016 Ethnographic Praxis in Industry Conference Proceedings, p. 522–534, ISSN 1559-8918, https://www.epicpeople.org

NOTES

1. Machine learning techniques can be used, of course, making the engineering a little easier perhaps, but that is not the same as using judgment, as Button et al. (1995) have argued forcefully.

2. One of the side benefits of these experiments was that they engaged our colleagues throughout the lab in our work. Given that our facility hosts people from a number of groups and divisions across Nissan and Renault, many people had only been vaguely familiar with the nature and direction of our work until then. These testing activities provided them with a sense of our approach and the direction of our thinking, and allowed as to sense how people might react more generally.

3. Although it should be noted that when the engineers implement how the car moves, they don’t do so in the first instance with an eye to what such movement communicates to other road users, a consideration that we have always stressed in our presentations to them about social acceptability of road users.