I envision the automotive design process to go beyond styling wrapped around boilerplate mechanical engineering, by leveraging contextual research and synthesis to design rich interactions and experiences for a future beyond self-driving cars but rather truly autonomous vessels.
We are fast approaching a mobility paradigm unlike ever before. Limiting ourselves to a historical archetype will hold us back from unlocking new ideas and methods.
How are cars designed today?
Car design fixes users (a.k.a occupants) around the Hip point (H-Point), an imaginary physiological parameter. The H-point is centered around the functional usage of the vehicle rather than the holistic user experience. It helps make the math of ergonomics in driving a car easy. I make the argument that it is an artifact of machine-led human factors that in a lot of ways controls our interactions with and within the technology.
From the perspective of a design process, the car design process lacks integrating (and most times conducting) in-depth contextual research to do design exploration since the product development cycle is typically evolution rather than a complete re-thinking of the underlying technological stack.
A User Experience Research Framework
Instead of following the typical automotive product planning process (here's a project I did based on that process), I did a lit review of different research methodologies to find a framework to help me breakdown interactions and capture experiences:
👍Activity Theory is a model to understand the interaction between a subject and an object
👍Experience Sampling (a.k.a Diary studies) to record experiences as we move through contexts
And so, I combined them by designing a questionnaire based on the key elements of Activity Theory to see what sort of data I could collect.
I studied 9 participants who commuted to their workplace by calling them during their commutes (2x a day and for 5 days) to capture differences (if any) in their real-time experiences and interactions.
What emerged was not just experiential desires but the understanding of how Motives and Forces were influencing participants' decisions regarding their interactions.
Experiences result from our activities so the effects of motives and forces are what I wanted to decode.
Due to the large set of complex qualitative data that was collected, I built a visualization using Nodebox Live to help myself understand relevant motives and forces for reported activities.
What sort of outcomes can one expect using data like this?
The visualization would be provided to designers. I recruited students at ArtCenter College of Design to design experiences based on what they saw from the experiential data. An interactive map of the data is available here.
To set the scene, I framed the brief to be from a mobility service called COMMYOUTE, which envisions improving commuting.
The ideas were not just looking at the physicality of the vessel but also at how it intelligently understands the passengers' goals of different experiences and activities.
We worked together for a design sprint and some of the outcomes are shared below.
So what's next? How would the language of motives and forces evolve to be part of a design process?
My experiment shows that using Experience Sampling and Activity Theory in the mobility experience design could lead to wildly different outcomes.
🤔As we evolve our understanding of the Motives and Forces, why should our systems be based on an understanding of us from the past (a.k.a dumb)?
🤔 Can autonomous vehicles learn about our experiences and interactions in the above manner?
🤔 Will they still be considered vehicles or should they be considered as something else?
🤔 How do designers work with an ever-evolving set of user data?
As a thought exercise, this led me to ideate a cyclical design process that takes into account enabling technologies, still very nascent, but could help bring to life the above combination of frameworks.
Here's a sneak peek into what that could look like: