Inspiration
The world of professional motorsports demands strategy precision ,and power of data to separate a podium finish from the rest of the pack.To shape the future of racing, drivers and engineers require innovative tools that go beyond mere speed.Racing isn’t just about speed — it’s about mindset in motion
-There is a clear need for tools that could help a driver identify areas for improvement. It is paramount to develop applications that allow drivers to optimize their racing line. -We are called upon to dig into the telemetry, lap times, and sensor data from past Toyota victories to build something extraordinary.
What it does
This solution is a data-driven application designed to enhance driver performance analysis by providing detailed visualization and insight into racing lines, optimizing speed and strategy on the track. The app takes in high-frequency telemetry data — throttle, brake, steering, lap distance, and GPS — and . Each driver’s lap becomes a zoom lens into their decision-making under pressure. Frankenstein - mixing live Reddit data with GPS telemetry from Toyota racing and an embedded racing app all in one spooky interface AI commentary!
🧩 Data Ingestion Reads a 1.5GB telemetry CSV - from Toyota Gazoo ,R1 and R2 Barber sessions containing over a million time-stamped sensor readings per session dataset
Utilize the Lap Timing Telemetry Data which including high precision event timestamps, lap times, and vehicle IDs, essential for mapping detailed movement and performance.
Integrate the Analysis Endurance With Sections dataset, extracting key columns such as LAP_NUMBER, LAP_TIME, sector times (S1/S2/S3), average lap speed (KPH), and max speed during the lap (TOP_SPEED) to determine optimal performance segments
AI coaching module designed to analyze specific driver mechanics based on lap timing and telemetry data. This module will provide actionable feedback by: AI Analysis
How we built it
We built a Perfect Frankenstein combo of racing telemetry + spooky aesthetics + Reddit feed + embedded racing app. To achieve the goal of providing novel insights and tools for driver training, we first had to Understand the Barber Racing Datasets dataset . We studied the supplied R1/R2 dataset excerpts to learn how to access and process the complex, high-stakes information from past Toyota victories. We learned how to extract the relevant information from several crucial datasets: Lap Timing Telemetry Data: We focused on extracting data for each lap to csv.This is the what that allows us to map the precise path and timing of the vehicle, which is essential for visualizing the racing line. This architecture allows integration of the AI Coach using Open AI gpt-oss, enabling it to correlate data to answer questions about speed, braking, acceleration, gear changes, and much more!
Challenges we ran into
Converting GPS (VBOX_Lat_Min, VBOX_Long_Minutes) to pixels for Racing line visualization ** Selecting open AI model ** that works with the telemetry data
Accomplishments that we're proud of
Interactive AI Coach, to correlate data to answer questions about speed, braking, acceleration, gear changes.
RacingLineVisualizer that allows users to dynamically load CSV files containing racing telemetry data.
Reddit Feed. live reddit data subreddits
What we learned
Telemetry data: support for creating racing line visual tool we built a visual tool designed to help drivers optimize their racing line and identify areas for improvement.
What's next for RaceSenseAI
Future development for the Racing Line Visualization App will focus on leveraging the remaining high-stakes racing data to evolve the tool for strategic decision-making and prediction
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