Visual Analysis of In-Car Communication Networks

Analyzing, understanding and working with complex systems and large
datasets has become a familiar challenge in the information era.
The explosion of data worldwide affects nearly every part of
society, particularly the science, engineering, health, and
financial domains. Looking, for instance at the automotive
industry, engineers are confronted with the enormously increased
complexity of vehicle electronics. Over the years, a large number
of advanced functions, such as ACC (adaptive cruise control), rear
seat entertainment systems or automatic start/stop engines, has
been integrated into the vehicle. Thereby, the functions have been
more and more distributed over the vehicle, leading to the
introduction of several communication networks. Overlooking all
relevant data facets, understanding dependencies, analyzing the
flow of messages and tracking down problems in these networks has
become a major challenge for automotive engineers. Promising
approaches to overcome information overload and to provide insight
into complex data are Information Visualization (InfoVis) and
Visual Analytics (VA). Over the last decades, these research
communities spent much effort on developing new methods to help
users obtain insight into complex data. However, few of these
solutions have yet reached end users, and moving research into
practice remains one of the great challenges in visual data
analysis. This situation is particularly true for large company
settings, where very little is known about additional challenges,
obstacles and requirements in InfoVis/VA development and
evaluation. Users have to be better integrated into our research
processes in terms of adequate requirements analysis, understanding
practices and challenges, developing well-directed, user-centered
technologies and evaluating their value within a realistic context.
This dissertation explores a novel InfoVis/VA application area,
namely in-car communication networks, and demonstrates how
information visualization methods and techniques can help engineers
to work with and better understand these networks. Based on a
three-year internship with a large automotive company and the close
cooperation with domain experts, I grounded a profound
understanding of specific challenges, requirements and obstacles
for InfoVis/VA application in this area and learned that “designing
with not for the people” is highly important for successful
solutions. The three main contributions of this dissertation are:
(1) An empirical analysis of current working practices of
automotive engineers and the derivation of specific design
requirements for InfoVis/VA tools; (2) the successful application
and evaluation of nine prototypes, including the deployment of five
systems; and (3) based on the three-year experience, a set of
recommendations for developing and evaluating InfoVis systems in
large company settings. I present ethnographic studies with more
than 150 automotive engineers. These studies helped us to
understand currently used tools, the underlying data, tasks as well
as user groups and to categorize the field into application
sub-domains. Based on these findings, we propose implications and
recommendations for designing tools to support current practices of
automotive network engineers with InfoVis/VA technologies. I also
present nine InfoVis design studies that we built and evaluated
with automotive domain experts and use them to systematically
explore the design space of applying InfoVis to in-car
communication networks. Each prototype was developed in a
user-centered, participatory process, respectively with a focus on
a specific sub-domain of target users with specific data and tasks.
Experimental results from studies with real users are presented,
that show that the visualization prototypes can improve the
engineers’ work in terms of working efficiency, better
understanding and novel insights. Based on lessons learned from
repeatedly designing and evaluating our tools together with domain
experts at a large automotive company, I discuss challenges and
present recommendations for deploying and evaluating VA/InfoVis
tools in large company settings. I hope that these recommendations
can guide other InfoVis researchers and practitioners in similar
projects by providing them with new insights, such as the necessity
for close integration with current tools and given processes,
distributed knowledge and high degree of specialization, and the
importance of addressing prevailing mental models and time
restrictions. In general, I think that large company settings are a
promising and fruitful field for novel InfoVis applications and
expect our recommendations to be useful tools for other researchers
and tool designers.