Track Caddie

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  racing line view

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  Open AI coach

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  AI coach view

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  race line

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.

• 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.
• The overall mission of this hackathon is to use the provided race datasets to develop a project that provides novel insights or tools for the racing community.

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. This project falls under the Driver Training & Insights category of the Hack the Track competition. From this solution, we can able to do the following by analyzing the provided PDF datasets:

• Analyze unique, high-stakes racing data from professional motorsports to build innovative tools for drivers and engineers.
• 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: ◦ Speed Analysis: Use TOP_SPEED and average KPH data to compare maximum speeds achieved during different segments.
  • asking questions about their lap telemetry data, and receive intelligent analysis and coaching feedback

How we built it

To achieve the goal of providing novel insights and tools for driver training, we first had to Understand the Barber Racing Datasets. 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 itself. By synthesizing these various data points—the high-precision timing, the sector metric—we built a visual tool designed to help drivers optimize their racing line. This architecture allows integration of the AI Coach, 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.

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 TelemetryX

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

Built With

• gpt-oss
• numpy
• pandas
• python
• react