Integrating the needs and preferences of young drivers into the design of autonomous vehicle dashboards
See Prototype
This project aimed to address the challenge:
"How might we cultivate trust between young drivers (aged 16–25) and autonomous vehicles?"
Through comprehensive user research, ideation, prototyping, and testing, we developed solutions focused on improving communication, reliability, and personalized features for autonomous vehicle (AV) systems. Our goal was to design an interface that cultivates trust, ensuring a safe driving experience for young drivers.
Research highlights that 75% of traffic accidents are caused by human factors such as fatigue, poor health, attention loss, negative mental states, and lack of driving skills. Trust in AV systems improves with high technical performance, user-friendly interfaces, and aesthetic quality, while distrust arises from poor usability or unattractive designs. Systems integrating autonomous braking, automatic speed control, sleepiness detection, and driver health monitoring can mitigate human errors and improve passenger safety.
Inspired by our research, we integrated features like AI co-pilot assistance, optimized alerts, and customizable modes.
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We conducted user interviews in the initial phase of our research to gain insights into user needs, behaviors, and expectations. As our focus was on the acceptance of AVs in young drivers and thereby leveraging AV capabilities to reduce crashes, we recruited individuals in the age group 16 to 25, including both first-time drivers and experienced drivers.
Participants were recruited based on age criteria. Before conducting the interviews, we sent out a consent form that outlined the purpose of the research. We then scheduled meetings with them to conduct the interview, either through Zoom or in person.
9 individuals in total participated in the structured interview process.
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We engaged in a collaborative brainstorming session, used the "Crazy 8s" method to sketch 8 ideas in 8 minutes for each theme, and responded to 4 problem prompts. Anonymous voting was conducted for idea selection. We facilitated a discussion that focused on top-voted ideas. Similar ideas were consolidated through merging, and a final selection was achieved through team consensus.
We explored creative solutions using Crazy 8s, focusing on:
AI Assistive Co-Pilot: Tailored guidance and suggestions based on travel history.
Customizable Modes: Voice-activated settings for seat position, climate control, lighting, and more.
Clear Communication: Alerts with audio, visual, and ambient lighting cues to ensure safe transitions between manual and autonomous modes.
Infotainment System: Enhancements like route suggestions, driver-to-car communication, and optimized vehicle efficiency.
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Built high-fidelity prototypes using Figma to refine user interactions for both the infotainment system and a companion mobile app. Then, we conducted usability testing to evaluate operational capabilities, user-friendliness, and design efficiency. We surveyed users with 15 questions, including a mix of Likert scale and open-ended responses. Over 70% of users preferred a combination of infotainment and mobile app features, highlighting the value of a cohesive system.
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For the evaluations, we used quantitative analysis methods like a survey with 15 open-ended questions, and we used qualitative analysis methods like think-aloud testing. Check out our prototype below.👇
The survey highlights a strong preference for customizable modes and robust alert systems (both audio and visual) in autonomous vehicle infotainment, suggesting users value personalized and safety-centric features.
The mobile application's vehicle statistics feature emerged as particularly valuable, indicating a user interest in accessing real-time data and insights about their vehicle through the app.
A significant majority (70%) believe that combining both the infotainment system and the mobile app would be more effective, suggesting a synergistic relationship that contributes to building trust in autonomous vehicle technology.
Users rated the overall user interface design of autonomous vehicle infotainment systems as highly satisfactory (5 on a 1 to 5 scale), indicating a positive reception of current design implementations.
This project underscores the importance of designing user-centered solutions for emerging technologies like autonomous vehicles.
In the future, I would like to be able to include dynamic light changes in my prototype. The Wizard of Oz technique would have also allowed me to capture eye movement data and facial expressions if I had used it in my testing. I should've conducted testing with multiple users in person. The team's excitement made us eager to move from ideation to tangible development, but we should have kept up the user-centric approach. Instead of rushing to build the prototype, we should've collected more user input through think-aloud testing to gain a better understanding of the product's drawbacks.
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