This is the final presentation for the design of Autobot, a smart phone app and built-in dashboard interface that provides information about your driving habits and promotes positive behavior change. This presentation details our design process, AutoBot's functions, and some of the theoretical framework used to explain our design decisions.
2. Introduction
Mission of Autobot
Influencer Matrix
Research
Solution
Autobot Demonstration
Video, Design defense, Relevance to course
concepts, UI Standards
3. Mission of Autobot
Problem: People give little Mission:
thought to how well they are 1) Design a system that informs
driving because it is people of their driving habits
ingrained, habitual behavior. 2) Leverage
Lack of awareness and gamification, information
overconfidence prevents people visualization, and social influence
from improving their driving to promote positive behavior
habits, which can lead to change
unsafe, costly, and
environmentally harmful driving.
7. Survey
SI Distributed
126 responses
Thanks!
What direction
should we take?
Driving behavior?
Maintenance
reminder?
What do people want
to know about?
8. Survey: Important Results
89 % 43 % 40 %
Know
Whats Improve
driving more
wrong with
habits about their
my car?
habits
9. Comparative Analysis
Competition thought
of many ways to
provide driving
feedback
Easy to understand
metrics for any driver
Acceleration
Braking
Cornering
10. Testing the Competition
5 volunteers drove
around with Driver
Feedback
Impressed with the
simplicity of app
Enjoyed seeing where on
their driving path they
made mistakes
Did not feel motivated to
improve
22. Record a Trip
App records
driving data using
the phones
accelerometer, gy
roscope and GPS
sensors.
The device must
remain stationary
while recording.
23. Self-Report
Shows perception
vs reality to reveal
misperceptions
29. Trip Log
Users can toggle
Routes to see how
History tracking: routine trips change
User can pick and over time
choose previous
trips to view
Users can retrieve old
data and reflect on past
mistakes
37. Green
Eco Status Level
Progress
Visualization
Eco Points History
38. Tips
Simple
instructions
Benefits clearly
laid out
Social influence
Clear monetary
incentive to
partake in this Earning points
activity
39. Conclusion
Gamification
Users receive points based
on their driving behavior and
car maintenance
Leaderboards and groups
provide an
engaging, competitive
environment
Visualizations display
progress toward goals and
new levels
40. Conclusion
Information Visualization
Stats pages display both
broad and specific views of
users data
Maps and Tips contextualize
data to be more compelling
Graphs allow the user to
compare variables
Self-reporting reveals
misconceptions about
behavior
41. Conclusion
Social Influence
Users can customize their
profiles, giving them ownership
of their data and progress
Leaderboard publishes the
users data to their friends
pages, making them more
conscious of their driving
#7: Everyone interviewed someone on their driving habits, which helped us create the following personas. We wanted to think about people who may use this app to improve their understanding of their own driving habits and also have a desire to learn more about being a conscious driver, to people who already take good care of their cars, and want a way to track information. We accounted for a large range of domain knowledge around cars by allowing users to choose how engaged in the system they want to be.
#12: Multi-PlatformA mobile and in-car application that informs drivers of their driving behavior based on three primary metrics: acceleration, braking, and cornering3 Key MetricsAcceleration, Braking and Cornering and fuel economy to a lesser extentBehavioral Change TechniquesThe mobile app informs and motivates users by taking advantage of behavioral change techniques. The app provides structure where was none before incentivizing positive driving behavior using a points system, offering status titles, easy to understand personalized feedback, and utilizing social influence through the AutoBot online community and other social networks.
#15: This technology is still emerging, but we wanted to integrate into cars existing dashboard functions. As you can see, this is how two of the major players in the automotive world present their consoles.
#17: We limited the functions to summarizing the information listed, and to minimize the score so that the driver could use other functions. We didnt include other tools available on the phone application because we didnt want to distract the driver any more than was necessary, especially since that was a lot of the criticism levied at current car console interfaces. Thats why we included the more interactive and engaging functions directly in our phone application.Here, we begin directing the rider by displaying the current score and giving the users a glimpse of how well theyre doing. This provides structural support in the form of points and motivation in terms of giving the user a guage of how well theyre doing on a 100 point scale.
#24: Nessers Ecological Self: Self-reporting shows how you perceive yourself, and then Autobot juxtaposes your perceived driving performance with your actual driving performance
#26: Nessers Ecological Self: Self-reporting shows how you perceive yourself, and then Autobot juxtaposes your perceived driving performance with your actual driving performance
#27: Nessers Ecological Self: Self-reporting shows how you perceive yourself, and then Autobot juxtaposes your perceived driving performance with your actual driving performance
#33: Personal Motivation (Influencer matrix component)The graphs address the personal ability barrier. These data visualizations allow the user to determine whether or not changes in her driving behavior are having an effect on the quality of her driving, be it positive or negative. For example, if a user decides to follow the advice provided with one of her alerts she can determine if that change in behavior has had an impact on any, or all, of the driving metrics by referring to the line graph.Direct the Rider (Rider and path)Although the graphs do not direct the rider in and of themselves, they are a key part of that goal. Telling someone to drink 2% milk, or in this case slow down as they enter a turn, is important but it is also important to give that person feedback on their progress toward change. So Ive been drinking 2% milk for a month now, did that really make a difference and if so, how much of a difference? Graphs, and other data visualizations, can help answer these questions and in doing so motivate a person to continue to change their behavior.Simple and Clean (UI standards)For the graphs I wanted the data to be presented to the user as clearly as possible. Although a more artistic representation would have done more to motivate the elephant I was focused on directing the rider. If you want to make a data visualization easy for a person to decipher you need to make the visualization simple and clean. This is a common practice in the field of psychology, to avoid using things like 3-D bar graphs and other more complex visualizations when a simple 2 dimensional graph could have presented the information just as well. Something to keep in mind is that our user base will be rather broad and that all of them will not be able to find meaning in more complex visualizations.That being said weve given the user a line graph that allows her to observe changes and trends in her driving behavior over an extended period of time. The scatter plot below allows the user to identify correlations between her scores on the three metrics and her fuel economy. She can determine the impact of a score of 70 on acceleration versus a score of 95 in terms of fuel economy, something that should be meaningful to most users. It is our hope that when the user observes the negative impact of her poor scores on her fuel economy she will be further motivated to improve her driving behavior.
#38: Structural Motivation (Influencer matrix component)This visualization is meant to help break down the barrier in structural motivation. Watching her tree grow provides the user with an immediate, and visible, reward for her eco friendly behavior. Aside from watching her tree grow the user can also track her progress toward obtaining the next level of proficiency in her eco friendly behavior.Motivate the Elephant (Rider and path)The tree visualization is meant to motivate the elephant. Unlike the graphs, with visualization we wanted to appeal to the users emotional side and therefore went with a more artistic visualization. Simply providing the user with numbers to demonstrate her environmental impact didnt seem like it was sufficient to quantify diverse data. A common unit by which environmental impact is often measured are pounds of carbon saved. Now I dont know about you but that unit is meaningless to me. So for our visualization we chose something familiar to people, one that they immediately identify with the environment, a tree.Design to Evoke Emotion (UI standards)For this visualization the I felt that the most appropriate choice would be a more visually appealing and somewhat atypical design. When trying to evoke emotion in a observer it is more important to provide an interesting visualization than one that is clean and simple, such as the graphs presented earlier.