F1 Race Simulation Project

F1.ANIM.720.mp4

Overview

This project showcases a high-fidelity 3D race simulation system, built to process real telemetry data and generate accurate visualizations of vehicle movement on a track. By integrating real-world spatial data, photorealistic rendering, and automation workflows, I developed a scalable pipeline that reconstructs race laps with precision and realism.

The core principles of this project—data-driven visualization, terrain modeling, and real-time simulation—are highly adaptable to broader applications, including planetary exploration and autonomous navigation systems.

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Key Components

1. Data Extraction & Processing (Python & FastF1 API)

• Implemented a data pipeline to fetch live and historical telemetry data via the FastF1 API.
• Extracted precise positional (X, Y), velocity, and timing data (4Hz sampling) to reconstruct vehicle motion.
• Organized the processed data into structured formats (CSV & GeoJSON) for simulation input.

This structured approach ensures a high level of accuracy, allowing seamless integration with game engines and real-time rendering environments.

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2. Terrain & Track Modeling (GIS, QGIS & Blender)

Realistic environment modeling requires high-resolution terrain data and accurate circuit layouts:

• QGIS & GIS Data Processing:

  • Extracted satellite imagery and digital elevation models (DEMs) to model the real-world track.
  • Converted geographic data to GeoJSON format for 3D processing.

• 3D Model Generation in Blender:

  • Used parametric curves and array modifiers to generate a continuous race track model.
  • Applied real-world textures for photorealistic rendering.

This workflow allows the creation of high-fidelity virtual terrains, an approach that can be adapted for lunar and planetary surface simulations.

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3. Vehicle Motion & Path Generation (Blender Python Scripting)

Accurate simulation of vehicle movement requires a data-driven animation system:

• Path Generation:

  • Parsed X, Y coordinates from telemetry data to create a motion path inside Blender.
  • Utilized Bezier curve smoothing for realistic transitions.

• Physics-Based Motion Simulation:

  • Developed a Follow Path constraint system to control vehicle movement.
  • Keyframed velocity adjustments based on real-world acceleration & braking data.

By automating data-to-motion conversion, this system can be extended to simulate rover movement across planetary terrains.

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4. Animation & Real-Time Simulation (Blender Python & Unreal Engine 5)

To bring the simulation to life, I implemented a time-synchronized animation system that accurately represents real-world vehicle dynamics:

• Speed-Based Keyframing:

  • Normalized telemetry speed data to adjust vehicle acceleration and deceleration dynamically.
  • Ensured smooth transitioning between high-speed straights and braking zones.

• Rendering & Realism (Unreal Engine 5 Integration):

  • Experimented with real-time rendering for interactive visualization.
  • Used ray tracing & dynamic lighting to enhance environmental realism.

The ability to integrate real-world telemetry into a high-fidelity simulation pipeline is crucial for applications in robotic navigation and planetary mission planning.

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Challenges & Solutions

1. Steering Angle Approximation

• Unlike traditional racing simulations, telemetry data lacks explicit steering angle values.
• I initially tried a 5-step look-ahead/behind tanh-based angle approximation, but it led to errors in sharp corners.
• Solution:
  • Implemented a curve-based orientation system in Blender that dynamically adjusts vehicle heading.
  • Used Follow Path constraints to ensure natural cornering behavior.

This method allows realistic vehicle orientation without direct steering inputs, a technique applicable to autonomous navigation simulations.

2. Large-Scale Terrain Accuracy

• GIS-to-3D model conversion introduced distortions in high-detail track models.
• Solution:
  • Used QGIS spatial corrections to refine topographical accuracy before importing into Blender.
  • Developed custom interpolation scripts to enhance surface continuity.

The same methods can be applied to lunar or Martian terrain modeling, improving simulation accuracy for off-world exploration.

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Future Enhancements & Broader Applications

This project serves as a foundational prototype for real-time visualization systems that merge scientific data with high-fidelity rendering. Possible extensions include:

✅ Real-Time Simulation

• Expand the system to process live telemetry data, allowing real-time vehicle visualization.
• Similar approaches could be used for rover navigation telemetry playback.

✅ Dynamic Terrain Adaptation

• Automate topographical adjustments using DEMs & LiDAR datasets.
• Extend methods for moon/Mars terrain simulation & obstacle avoidance testing.

✅ Expanding Physics-Based Motion Models

• Enhance the simulation with suspension dynamics & real-world physics constraints.
• Investigate real-time integration with autonomous driving algorithms for planetary mobility applications.

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Conclusion

This project demonstrates the power of data-driven simulation, high-fidelity visualization, and real-time rendering to create realistic, interactive environments.

The core techniques—GIS data processing, procedural 3D modeling, and animation automation—can be extended beyond motorsports, finding applications in robotic exploration, planetary navigation, and space mission visualization.

As I continue refining this pipeline, I’m excited about its potential applications in space exploration, research, and real-time planetary simulations.