Automotive User Interfaces and Interactive Vehicular Applications

media, content, and sociality will be present in the car, while the
connections and pathways technology can enable diminish the
displacement of the car, creating new locations and new
experiential landscapes. The heterogeneity of automobility future
experiences is at the core of our research approach. The goal of
the LEAM Project is to explore the diversity of automobility
experiences in Brazil, China, Germany, the United States, and
Russia. The research is currently in field, but here we discuss our
processes we are developing to understand the relationships
among practices, cars, and technology.
2. THE ETHNOGRAPHIC (U)TURN:
Methodologically speaking, this project represents a full return to
ethnographic research as practiced during the initial florescence of
anthropology by scholars like Boas, Malinowski, Benedict, and
Radcliffe-Brown. Ethnography in this era was characterized by an
array of systematic techniques that combined quantitative and
qualitative approaches. The diversity of the LEAM team:
anthropologists, computer scientists, designers, and an
experimental psychologist; allows us to combine disparate data
types and analyses in service of multi-faceted portraits of
automobility grounded in an ethnographic perspective that
privileges lived experiences of mobility.
The team approached the challenge of fieldwork and analysis by
employing multiple modes of data gathering, each only useful in
combination with the others. One lesson learned from the CTO
project is while the physical objects in the car represented traces
of past behaviors and routines, as well as people’s values, they
were not enough to provide insight into movement, insularity vs.
embeddedness in emergent social milieus, nor how the car extends
into social space. Like archaeologists, we were looking for
different types of traces of the past; we needed to see the things
we could not see - cars in use, in movement, and located in space.
We needed to render more direct, comprehensive and visible
traces of the ephemerality of movement, temporality, the isolation
and the embeddedness of automobility in the local landscape.
We supplement the in-depth ethnographic interviews and car turn
out exercises with approximately 30 days of data from in-car light,
sound, acceleration and temperature sensors, GPS tracking for
routes, speeds and stops (complimented by participants logging
stops and creating short videos describing their car use), and
smart phone usage tracking. Each method probes on different
aspects of automobility, from lived experiences of owning, using
and being in a car in motion, to car use mobility patterns over
time, to in-car environmental characteristic, to the
interplay/tension between built-in and brought-in technologies.
The data only fully comes alive when we begin to overlay the sets,
and analyze the data in combination. For example, questions
immediately emerged around behaviors such as, when is a stop
not a stop based on the GPS data? What is routine car use? What
is atypical? And how are those patterns experienced? We could
not begin to answer these without also looking at the smart phone
tracking data (in Brazil phone use appears to cluster around the
start and finish of journeys and during very slow movement). The
smart phone data shed light on the spatially-based GPS data and
vice versa. Additionally, knowing that cars are deeply embedded
in people’s social relationships, we wanted to understand the car
as a fundamentally social technology. The data collected from
peoples’ smart phones allowed us to understand mobility relative
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to sociality as well as complex social networks – multiple users,
shared usage, other interdependencies but it did not provide
enough insight into the social nature of cars. This required a
follow-up in-depth interviews with participants armed with
various visualizations of the GPS and smart phone usage data. We
are currently refining representations of the data as we reinterview
the respondents, using layers of maps, timelines, route,
behaviors, experiences, and stops. The result is an extraordinarily
rich combination of social, spatial, experiential and temporal data.
3. CONCLUSIONS
The movement and traceability of people, objects, and cars are
bound up in larger social meanings and structures that are not
always at the surface of lived daily experience. The LEAM project
seeks to trace the outline of these relationships and provide a path
forward for the meaningful infusion of technology in the
car/human equation.
We believe this is necessary and important perspective if we are to
build successful smart transportation solutions. Returning to some
of the challenges around smart transportation solutions, selfdriving
cars will need to be differently realized to be successful in
places like Brazil. In São Paulo drivers often leave large distances
between themselves and the car in front of them at traffic lights
(enabling evasive action from carjackers or thieving Motoboys)
and they roll through stop signs and traffic lights at night to avoid
stopping - another opportunity to be robbed. A self-driving car
that forces complete stops at red lights at night, or “efficiently”
packs cars on roads with no space between them makes no sense
in urban Brazil, doing potentially more harm than good to drivers.
The LEAM project creates a design space encompassing the car
and the infrastructure and ecosystem around varied smart
transportation futures. This space is informed by locality and
context dependent conditions of how, when, and why cars are
used, by whom, and under what conditions. This research,
preliminary as it is, opens a new design space for thinking about
cars as part of a living network of people, places, and things in
motion.
4. REFERENCES
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MIT Press Cambridge MA
[4] Zafiroglu, A., Bell, G. and Healey, J. (C)archeology: Car
Turn Outs and Automobility. Automotive UI 2011, Salzburg,
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[5] Urry, J. 2003. Automobility, Car Culture and Weightless
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[6] de Certeau, M. 1984. The Practice of Everyday Life
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