SVL Simulator – Desktop App

SVL Simulator – Desktop App

SVL Simulator – Desktop App

SVL Simulator – Desktop App

An autonomous vehicle simulator built to test and verify self-driving cars and robots, throughout the entire development lifecycle.

An autonomous vehicle simulator built to test and verify self-driving cars and robots, throughout the entire development lifecycle.

COMPANY

LG Electronics

LG Electronics

DURATION

3 months

MY ROLE

UX/UI Designer

ACTIVITIES

User interviews / Information architecture design / UI design / Prototyping / Usability testing

OVERVIEW

The SVL Simulator is made up of two apps that work together to run simulations. The first one is a web app – it's where you create and manage your simulations and assets. You can also use it to review results and share completed simulations. The second is a desktop app, designed to run and visualize simulations in real time on your computer. In current case study, I’ll walk you through the process of designing the desktop application.

Web application

Desktop application

User Interviews
Once I had a better understanding of our users, I set up one-to-one interviews with each respondent. I prepared a list of questions directly related to the functionality of the future app. I asked users what problems they wanted to solve with our app, and how they want to interact with it. All answers were carefully documented and meticulously processed later by me.
User Interviews

I kicked off user research by interviewing autopilot developers already working in the field. I came prepared with a focused list of questions about the app’s potential functionality. I asked them what problems they hoped the app would solve, and how they envisioned interacting with it. I carefully documented every answer and took the time to thoroughly analyze all the feedback afterward.

User Stories

Based on the interviews, I put together a set of user stories that could be easily translated into real, actionable app features.

As an autopilot developer I want to be able to link my PC to web app, so I can run simulations remotely.
As an autopilot developer I want to be able to cache simulations files on my PC, so I can run simulations offline.
As an autopilot developer I want to be able to switch between all vehicles during the simulation, so I can easily explore their behaviour.
As an autopilot developer I want to be able to switch camera modes during the simulation, so I can observe simulation from different points of view.
As an autopilot developer I want to be able to see vehicle`s sensors data during the simulation, so I can use it for debugging.
As an autopilot developer I want to be able to control environment during the simulation, so I can explore how autopilot behaves in different conditions.
As an autopilot developer I want to be able to see simulator events logs, so I can fix simulation in case something is broken.
As an autonomous vehicles developer I want to link multiple PC into one cluster, so I can run distributed simulations.
Information Architecture

The user stories clearly highlighted the core functionality our users needed, which allowed me to quickly map out the information architecture of the future app. The overall structure turned out to be fairly simple – which is a big win. One key feature is that the simulation must be launched from the web app the first time (since that’s where it’s created), but after that, as long as all simulation files are cached locally, it can be run directly from the desktop app.

UI Design and Prototyping

With the information architecture in place, I moved straight into designing and prototyping the desktop application. I broke down the functionality into individual user flows and designed each one step by step. Since the desktop app is just one piece of the larger SVL Simulator ecosystem, it was important to maintain a consistent visual style across all interfaces.


Luckily, I had already spent a year and a half designing the SVL Simulator web app, so I had a nearly complete design system ready to go – with all the essential components and assets in place.

USER FLOW
Linking

The first user flow I tackled was linking the desktop app to the web app. This connection is essential for running simulations on a PC directly from the web interface – as long as the machine is powered on and connected to our server, users can launch simulations from anywhere. Multiple PCs can also be grouped into a cluster, either local or cloud-based – that part of the setup lives in the Clusters section of the web app.

USER FLOW

Simulation Starting

Simulation Starting

Simulation Starting

Simulation Starting

Once the two apps are linked, starting a simulation on the desktop app is as easy as a single click from the web app. Of course, the simulation has to be created beforehand – but that’s a whole other story.

USER FLOW

Simulation Running

Simulation Running

Simulation Running

Simulation Running

During the simulation, users can control several parameters in real time. They can switch between cars (if the simulation includes more than one), change camera views, access sensor data, adjust the environment, and simulate different weather conditions. There’s also a log showing all simulation notifications and a handy keyboard shortcut guide.

USER FLOW

Cache Control

Cache Control

Cache Control

Cache Control

Since all simulation assets (3D car models, environment models, sensors, etc.) are stored on the web server, they’re downloaded to the user’s PC during the first simulation launch. To help users keep track of which assets are currently stored on their PC, I designed a dedicated feature for managing assets.

USER FLOW

Profile & Settings

Profile & Settings

Profile & Settings

Profile & Settings

At the very end, I added features for account management, profile settings, graphics options, the ability to unlink from cluster, and more.

Outcome

Outcome

Outcome

Outcome

After rolling out the updated design of the SVL Simulator Desktop App, I saw significant improvements in key metrics:

– Setup and simulation launch times dropped by 25%, based on A/B testing with 8 engineers comparing the new interface to the previous version.

– Testing accuracy increased by 20%, thanks to the ability to simulate more complex road scenarios – this was confirmed by analyzing the number of bugs detected in test simulations over a month.

– Additionally, the improved intuitiveness and usability of the new interface boosted user satisfaction by 60%, measured through NPS surveys and post-use interviews.

This comprehensive testing approach clearly demonstrated the effectiveness of my UX decisions and their real impact on business outcomes.

launch
time

-0%

Testing accuracy

+0%

User
satisfaction

+0%

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