Motional Self-Driving Car Displays

The Challenge

Self-driving cars process a staggering amount of information every second. They parse objects, predict trajectories, evaluate risks, and make judgment calls about how to navigate the world around them. But while the technology is still learning, humans play a crucial role in keeping those vehicles—and everyone around them—safe.

Motional, the autonomous vehicle company founded as a joint venture between Hyundai and Aptiv, runs thousands of hours of on-road and track testing in Boston, Los Angeles, Pittsburgh, Singapore, and Las Vegas. If you’ve taken a Lyft in Vegas or LA, you may have unknowingly ridden in one of their vehicles already. 

During testing, safety operators sit in the vehicle, monitoring what the car “sees” through an in-vehicle laptop display. In the early test-track days, it was acceptable for operators to use the same dense, data-heavy interface that engineers used at their desks. But once testing transitioned to public roads, the stakes—and the cognitive load—changed.

To make real-time decisions, operators needed a display that surfaced only the information critical to safety. Nothing more. Nothing less.

That’s where ZeroDegrees stepped in.

As Motional’s testing environments expanded from controlled tracks to unpredictable public streets, operators needed a way to quickly understand vehicle intent without being buried in engineering data.

The original interface displayed everything—sensor windows, diagnostic panels, system logs, experimental views—all of which were invaluable for engineers, but overloaded for operators who needed to react instantly. Even a split-second delay caused by visual clutter could undermine safety.

Operators needed:

• A cleaner, simpler visual hierarchy
• Clarity on what the car “thinks” is happening around it
• A way to understand intent at a glance
• The ability to take over instantly if something seemed off

And because different roles (safety drivers, co-pilots, visiting engineers) all interacted with the system, the interface needed to be flexible without becoming complicated.

• Before
• After

Our Approach

We embedded ourselves alongside Motional’s operators, engineers, and test teams. We rode along in real test vehicles, interviewed safety drivers, observed workflows, and ran rapid design studios to map what information truly mattered and what was getting in the way. 

Key steps included:

1. Get inside the vehicle, literally.

We conducted in-vehicle research to see how operators monitored the system while moving through real streets. Watching them juggle attention between the road, the display, and the car’s behavior gave us a precise picture of cognitive load.

2. Separate “engineer data” from “operator data.”

Working with the Motional team, we identified only those elements that affected real-time safety decisions. These stayed, and we amplified them visually. Everything else was minimized, tucked away, or removed entirely.

3. Build for different roles without creating complexity.

Instead of layering on more screens, we created operator profiles that could save personalized settings, preferred overlays, and custom data views for later reference.

4. Prototype, test, and refine at speed.

This work required extremely fast iteration. We sketched concepts, built mid-fidelity prototypes, and pressure-tested them with operators in weekly cycles. We made adjustments in real time based on direct in-vehicle feedback.

The Solution

The final operator display is a dramatically simplified, safety-first interface built for clarity, confidence, and control.

What we created:

• A clean, glanceable display that surfaces only safety-critical data
• Clear visual cues showing what the car “thinks” is around it and how it plans to act
• Reduced visual noise, with diagnostic information hidden unless explicitly needed
• Profile-based customization, allowing advanced users to access more detail when appropriate
• Quick-pivot tools to support diverging from test routes on the fly
• Consistent visual design language, built for split-second interpretation

We designed the system so operators could instantly detect anomalies—the moments when an autonomous vehicle must be overridden—without digging or deciphering. We didn’t overcomplicate a complicated interface. We distilled it down to exactly what mattered, and nothing that didn’t.

The Impact

The new operator display reduced cognitive load, improved real-time comprehension, and helped operators make faster, more confident decisions. It strengthened Motional’s safety workflows and set a higher standard for how autonomous-vehicle interfaces should support the humans behind emerging AI.

By giving operators a calmer, clearer view of the vehicle’s brain, ZeroDegrees helped make the future of autonomous driving a little safer and a lot more intuitive.