Automotive User Interfaces and Interactive Vehicular Applications

As a first step, we believe that we need to understand with a fair
amount of specificity exactly what people are doing – and not
doing - on their smart phones while in their cars.
2. SPECIFYING “SOMETIMES” and “IT
DEPENDS”
Over the course of the past year, we have interviewed car owners
in the US, UK, Singapore, Malaysia, China, Australia, Italy,
Brazil and Germany. As part of these interviews, we have asked
car owners to systematically empty the contents of their cars and
explain why various items – from ang pau envelopes to shopping
cart tokens to handguns – are in their cars. While we‟ve found this
exercise a useful way to elicit information on important activities,
routines, and social relationships that take place in/with/through/
around/ because of the car[1], we‟ve found smart phones to be
frustratingly opaque and mute artifacts. Many of these drivers
admitted to using their smart phones during and adjacent to their
routine car journeys, and their descriptions of their behavior were
maddeningly vague. While some were sheepish about admitting
to even touching their phones while driving and others bragged
about their multi-tasking skills, almost all descriptions they gave
of what they were actually doing on and with their phones were
generic (“I make calls”, “I browse”, “I look at maps”) and took
place in hazily defined locations (“while I‟m driving”, „while I‟m
going slow”) and in a temporal frequencies (“sometimes”, “it
depends”).
In our current study, Local Experiences of Automobility (LEAM),
we are exploring the heterogeneity of automobility futures
through classic anthropological and ethnographic methods
combining disparate data types and analyses in service of multifaceted
portraits of automobility in Brazil, Germany, China,
Russia and the US. Our methods address the fundamental
ethnographic imperative to understand and reconcile the
differences among what people say they do (“I browse . . . while
I‟m going slow . . . . sometimes”) and their actual behavior. We
want to know not just what they think they are doing, and what
their actual behaviors indicate, but most importantly how they
make sense of their actions when provided with information about
their behaviors over time. We want to transform our
understanding of smart phone use in cars from a quite limited
questioning of opaque and mute artifacts to a rich conversation
with research participants about transparent and voluble artifacts
they use in their cars.
Research participants in the LEAM project are limited to car
owners who have bought a new car within the last three years that
has some level of OEM or aftermarket entertainment or
communication technology regularly used (ranging from stereo
system with USB or Smartphone connectivity, to DVD players, to
aftermarket GPS). All participants also regularly use their smart
phones while in their car – at least 3-5 times a week for at least
one feature or activity beyond making or receiving phone calls.
While the LEAM research includes a broader range of methods,
here we will focus on how we are exploring the interplay/tension
between built-in and brought in technologies. After initial indepth,
in car/in home interviews, we outfit participants‟ cars with
passive GPS devices that track their cars‟ routes, stops and speeds
for one month and install an application on their smart phones
that allows us to track how they are using their phones during this
same period. The application, called the Program Utility Manager
(or PUM) Tool, was developed by Intel researchers and has so far
mostly been used in large panel studies of mobile phone and PC
use [2]. In the LEAM project, the PUM Tool allows us to track
when participants use their smart phones. Specifically, it allows
us to track the state of the smart phone at 10 second intervals (the
state of the screen, orientation, 3-axis accelerometer) and the
current focal app (Facebook, What‟s Up?, Maps) or feature (text,
phone call, etc.) at 50 second intervals. For privacy reasons, we
do not have access to any phone numbers or website names that
are used on the phone.
To make phones speak to us in ways their owners can‟t, we
analyzed phone usage for fifteen minutes before, continuously
during and for fifteen minutes after each car journey, as defined
by GPS data. We chose this time cushion around car journeys
based on initial ethnographic interviews with drivers who
mentioned activities done on their phones in preparation for a trip
(looking up an address or getting directions), and at the
conclusion of a trip (activities that cannot be done during the trip
for any reason they defined (frequently mentioned were checking
and or sending email or text messages). Synchronizing timestamped
GPS data (location, route, speed) with time-stamped
PUM Tool data (feature or app name), gave us detailed behavioral
data about in-vehicle smart phone use.
With this data we have created a series of visualizations of
automobility routines that we share with participants to facilitate a
discussion about phone use, car use and their overlap. While the
tracking isn‟t perfect (was the person in the car for a particular
trip? was the GPS perfectly aligned to the second with the PUM
data?), the combination of tracking and interviews provides a rich
set of data for exploring the messy middle ground of brought
in/built in technology use.
As this conference takes place, we have just finished our research
in Brazil and have GPS and Smartphone tracking ongoing in
Germany. We do not yet have a definitive set of recommendations
or findings. We are experimenting with the most effective ways to
visualize the data to optimize discussion with participants.
Currently we are working with two approaches.
1. Privileging routes and geographic location through
Google-Earth powered maps showing routes and speeds
with pushpins to indicate the location of smart phone
use. We can visualize anywhere from a single journey to
an entire month of journeys at once.
2. Privileging times and schedules through timeline of car
use and app use over a series of single days.
Each visualization lets us investigate different aspects of the
intersection of automobility and smart phone use. The maps, in
particularly, are proving valuable in exploring the dynamic
relationships and the reciprocal influences between smart phone
use and the various transportation landscapes as the car moves
through places, both displaced and intimately connected to its
physical (and legal, cultural, regulatory . . ) environments. Our
initial engagement with our data has us thinking about how the
experience of mobility is being recursively reconstituted through
the presence and use of internet and telecommunications service
connected smart phones that are also concentrated archives of rich
multi-media content. Ultimately our visualizations and our data
are a means to address more basic questions around how people
conceptualize time, routines, environments and activities enabled
by automobility, and how these experiences are in flux with the