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Otto — Designing trust in semi-autonomous vehicles for Gen Z Drivers

The safest car means nothing if drivers don't trust it.

Industry:

Autonomous Vehicles

Role:

Service Designer

Type

Master's Final Dissertation

Contribution:

Research and synthesis, user interviews, design and business strategy, concept prototyping, user testing, and UX/UI design

The Problem

“It would freak me out if people have these cars on the road without fully knowing how to use them.”

– Interviewee Participant (U.S. Driver)

“It would freak me out if people have these cars on the road without fully knowing how to use them.”

– Interviewee Participant (U.S. Driver)

Gen Z drivers will inherit autonomous vehicles, but they don't understand them.

74% of Americans cite safety as their top AV concern, and 39% report confusion about existing driver-assist (ADAS) features.

The barrier to adoption isn't technology. It's anxiety.

Challenge

How might we reduce anxiety and build confidence in semi-autonomous vehicles for Gen Z drivers?

What I did and what I found

I led end-to-end service design across a complex ecosystem: drivers, vehicle manufacturers, in-car systems, regulatory frameworks, and emerging AI.

I started with 17 interviews (10 subject matter experts, 7 licensed U.S. drivers) and 41 survey responses, capturing 350+ data points on current experiences and expectations with semi-autonomous vehicles.

Synthesis confirmed two distinct user needs:

U.S. drivers seek assurance and optimism. They want to feel safe, in control, and satisfied.

Subject matter experts prioritize confidence and functional reliability. They want the system to be competent, cost-efficient, and innovative.

Synthesis confirmed two distinct user needs:

U.S. drivers seek assurance and optimism. They want to feel safe, in control, and satisfied.

Subject matter experts prioritize confidence and functional reliability. They want the system to be competent, cost-efficient, and innovative.

I built separate ideal experience models for each group to map these emotional and functional requirements.

That divergence shaped five design principles:

Prioritize safety and reliability
Promote control and transparency
Enhance accessibility and inclusivity
Empower users through education
Strive for aesthetic and functional excellence.

That divergence shaped five design principles:

Prioritize safety and reliability
Promote control and transparency
Enhance accessibility and inclusivity
Empower users through education
Strive for aesthetic and functional excellence.

These principles became the filter for every concept I generated and the criteria I used to evaluate solutions.

From there, the design direction split into two tracks: confidence-building outside the car and real-time reassurance inside it.

Two interventions survived: Otto Well (a mobile app) and Otto AI (an in-car voice assistant).

Otto — Designing trust in semi-autonomous vehicles for Gen Z Drivers

The safest car means nothing if drivers don't trust it.

Industry:

Autonomous Vehicles

Type:

Master's Final Dissertation

Role:

Service Designer

Contribution:

Research and synthesis, user interviews, design and business strategy, concept prototyping, user testing, and UX/UI design

What I designed

I designed two service interventions to address the challenge: Otto Well, a mobile app, and Otto AI, an in-car voice assistant.

Intervention 1

Otto Well (Mobile App)

Otto Well addresses the moment before and after driving: how do drivers build confidence when they're not in the car?

The Problem:

Gen Z drivers can't see their own safety performance. They lack feedback loops that tell them what "good driving" looks like in a semi-autonomous vehicle.

The Solution:

A mobile app that tracks safe driving habits and rewards confidence-building behavior.

Otto Well does three things:

Personalized Driving Tips — After each drive, the app delivers AI-powered feedback specific to that driver's actual decisions, not generic tips.

Safe Driving Rewards — A gamified rewards system reinforces safety habits: users earn SafeScore points for smooth acceleration, timely lane changes, and predictable braking, which unlock coupons and incentives.

Driving Insights — A visual dashboard shows improvement over time, giving drivers a clear narrative that they're getting better

Intervention 2

Otto AI (In-Car Voice Assistant)

Otto AI addresses the moment during driving: how do drivers understand what's happening and trust the system in real time?

The Problem:

Driving a semi-autonomous vehicle requires attention to a system the driver doesn't understand. When a semi-autonomous vehicle does something unexpected, drivers panic. They need clarity in the moment, not after the fact.

The Solution:

Otto AI is a voice assistant that explains what the car is doing and why, as it happens.

Otto Ai does three things:

Voice-Activated Commands — A driver asks "Why did the car slow down?" and Otto responds with context: traffic detected ahead, reducing speed to maintain safe distance.

Task Automation — Otto handles routine tasks like climate and navigation, freeing the driver to focus on the road and the car's behavior.

Proactive Safe Driving Alerts — Before unexpected events, Otto offers proactive alerts: "We're approaching a high-traffic area. I'm ready to assist if needed. Stay alert."

Image source: BMW AG

Results

I tested both interventions with 6 Gen Z drivers using usability testing methodology. All participants completed both tasks successfully, averaging under 2 minutes per task.

Otto Well performed stronger on satisfaction metrics:

Customer Satisfaction: 83
Customer Effort Score: 66
Net Promoter Score: 90

Across both interventions, average user satisfaction scored 4.25 out of 5 and ease of use averaged 4.08 out of 5.

One participant rated both measures at 5 out of 5. Driver 5 partially failed the driving habits task in Otto Well but completed the personalized tips task, suggesting the feedback loop concept resonated more than the habit-tracking feature.

Otto AI scored:

Customer Effort Score: 66

Customer Satisfaction: 66
Net Promoter Score: 50

Both interventions scored comparably on satisfaction and ease of use, suggesting the core value proposition, reducing anxiety through transparency and feedback, resonated regardless of whether the channel was a mobile app or an in-car voice assistant.

Voice interaction likely introduced more uncertainty than a visual interface.

The difference makes sense. A mobile app gives users control and time to absorb feedback.

A voice assistant requires trust in real time, which is harder to build in a single test session. Longer exposure would likely close that gap.

Three things changed: metrics are presented once instead of duplicated, the false difference is gone, and the partial failure is reframed as a design insight. The section is shorter and every sentence earns its place.

Potential Impact

This project targets 25.9 million licensed Gen Z drivers in the U.S. With the autonomous vehicle market projected to reach $31 billion by 2026, the potential addressable market for confidence-building services is estimated at $22.32 billion.

Learnings

Systems thinking is messy and necessary.

I designed for one person, but that person sits inside a car built by engineers, regulated by governments, insured by companies, and shaped by social attitudes toward automation. Holding all of those systems at once was harder than designing a single interface.

Systems thinking is messy and necessary.

Recruiting participants is harder than designing solutions

No budget, no brand, no user base. Finding 6 Gen Z drivers willing to test an AV prototype required relationship-building and persistence. User research isn't just about asking questions. It's about access and trust.

Recruiting participants is harder than designing solutions

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Contact:

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@2026 Tanuj Bisht • Designed & developed in Framer with ❤️ by me