BeamNG/impactgen

Python script and Lua extension using BeamNG.tech to generate low impact crash scenarios and ground truth data for imitation learning.

37
/ 100
Emerging

This tool helps researchers and engineers create a diverse dataset of vehicle crash scenarios for training AI models. It takes in vehicle models and crash parameters like impact speed and angle, then generates detailed image sequences of the crashes (both regular and semantically annotated) along with vehicle damage data. This is ideal for those developing computer vision systems for autonomous vehicles, insurance claim assessment, or accident reconstruction.

No commits in the last 6 months.

Use this if you need large-scale, high-quality synthetic data for training machine learning models that analyze vehicle impacts and damage.

Not ideal if you need to simulate complex, multi-vehicle pile-ups or highly customized crash environments not covered by the predefined scenarios.

autonomous-driving collision-simulation computer-vision vehicle-damage-assessment synthetic-data-generation
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 15 / 25

How are scores calculated?

Stars

19

Forks

5

Language

Python

License

MIT

Last pushed

Apr 10, 2025

Commits (30d)

0

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