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Play, observe and understand a full replay of a race.
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Analyse data like lap, sector, race, ... times to get better insights.
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Participate with your own car!
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Drive either from follow or cockpit perspective.
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Gain better understanding with color coded comparison.
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Compare lap times of all vehicles for current lap.
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Compare sector times for all vehicles.
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Get the 10 fastest laps for the current vehicle.
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See the racing results for deeper insights.
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Use 10 different cameras to get the perfect overview of the race track.
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Derive current standings from the overview.
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Use the birdview to get an even better look at competitors and their ghosts (aka ideal line positions)
Inspiration
Telemetry Rush demonstrates how data, visualization and interactivity can merge into a compelling racing analytics tool. It’s designed to be accessible: whether you’re a motorsport fan, a data scientist, or a game developer, you can explore racing telemetry in a fun, interactive way.
What it does
It integrates external Python servers to replay, visualize, challenge and analyze motorsport telemetry data. The Python servers are used to simulate live streaming data. They deliver live data from an actual race, which is used to recreate an already driven race. It combines live data streaming, racing game, vehicle visualization, weather effects, and leaderboard tracking into a single interactive Windows application.
If you're up for a challenge you can even spawn your own car and try to beat the times of the professional racers.
How we built it
Started with the Python data optimization, followed by the data streaming and building a visualization in Unity around that. What sounds pretty simple is actually very hard, as especially a good trade of for the data optimization was very hard to figure out. Building the live streaming with websockets was not too much effort for me, as I'm experienced with web streaming as well. My experience with Unity helped a lot to build the visualization part. After that, bringing everything together and make it a smooth experience was the most challenging part.
Challenges we ran into
A big challenge was the optimization of telemetry data, by reducing everything to an amount that could be handled by a default PC. Setting up a live streaming, that can be paused (and hopefully handle other options in future) was a big challenge too.
Accomplishments that we're proud of
Building this project, that I'm really proud of, as a solo developer, aside family and a full-time job is my biggest personal accomplishment. Another big thing would be, to actually find a way using the provided data, putting it into charts and have a way for users to actively participate in the whole analysis and data driven approach. I hope people like it and have fun challenging the real racers and get a good insight into real racing data and every aspect, where telemetry data is especially important.
What we learned
A big learning was the live streaming setup via Python, to read the csv data and pre-process it in a way, that the over 1 gigabytes of telemetry data (1.5GB) could actually be removed to around 200MB. Putting everything so together that, even if detailed GPS data was missing an interpolation of this data was still possible to get a visualization of the race, that people can enjoy.
What's next for Telemetry Rush
The next steps include better streaming handling, like rewind, restart and seek, to better analyze certain situations within a race. An additional preprocessing for other races, which provide GPS data is planned too. A multiplayer and observer integration is also on our list.
The big vision for the future would be to get live data from a race, preprocess it and live stream it to people, which instead of watching the race they can see all the data and even participate with their own car and try to be part of the race, instead of just watching it.
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