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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
AlpaSim is an open-source autonomous vehicle simulation platform designed for development and testing of end-to-end AV policies
| Date | Stars |
|---|---|
| 2026-07-24 | 1140 |
| 2026-07-25 | 1139 |
| 2026-07-28 | 1143 |
| 2026-07-30 | 1143 |
| 2026-08-06 | 1143 |
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# AlpaSim: A modular, lightweight, and data-driven research simulator for autonomous driving <div align="center"> <img src="docs/assets/images/thumbnail.gif" alt="AlpaSim Simulation Demo" width="600"> </div> ## What is AlpaSim? AlpaSim is an open-source autonomous vehicle simulation platform designed specifically for research and development. It allows users to test end-to-end AV policies in a closed-loop setting by simulating realistic sensor data, vehicle dynamics, and traffic scenarios within a modular and extensible testbed. Suitable use cases include: - **Algorithm Validation**: Test new autonomous driving algorithms in realistic environments - **Safety Analysis**: Evaluate vehicle behavior in edge cases and challenging scenarios - **Performance Benchmarking/Regression Testing**: Compare different models and configurations - **Debugging**: Understand and debug complex autonomous driving behaviors ### **Sensor Fidelity** - Pluggable renderer service with default [NuRec](https://docs.nvidia.com/nurec/) support and [OmniDreams](https://github.com/nv-tlabs/omni-dreams) video-model rendering through [FlashDreams](https://github.com/NVIDIA/flashdreams) - High-fidelity camera feeds with configurable field-of-view, resolution, and frame rates - Stateful video-model rendering for stronger dynamic-object and non-rigid visual fidelity - Realistic sensor noise and environmental conditions ### **Research Hackability** - Python-based implementation built for rapid prototyping and experimentation - Modular grpc interface design allows researchers to swap out components with custom implementations - Extensive configuration options and debugging tools ### **Horizontal Scalability** - Microservices architecture enabling distributed computing - Scale individual components for optimal load balancing - Support for multi-node deployments To learn more about the design principles and architecture, check out the [system design docs](docs/DESIGN.md). ## Driving Policies AlpaSim currently supports the following driver policies: - [Alpamayo-R1](https://github.com/NVlabs/alpamayo) - NVIDIA Alpamayo, a VLA driving policy with chain-of-causation reasoning - [Alpamayo 1.5](https://github.com/NVlabs/alpamayo1.5) - Public Alpamayo 1.5 checkpoint support is also available - [VaVAM](https://github.com/valeoai/VideoActionModel) - an autoregressive video-action driving policy - [Transfuser](https://github.com/autonomousvision/lead?tab=readme-ov-file#beyond-carla-cross-benchmark-deployment) \- Latent TransFuser v6 ([LTFv6](<(https://huggingface.co/ln2697/tfv6_navsim)>)) policy developed for [NAVSIM](https://github.com/autonomousvision/navsim) (provisional) Stay tuned for additional model support. [Contributions](#contributing) from the community are appreciated. ## Support 📣 **Usage questions and discussion about AlpaSim**: please join us on the [Alpamayo NV Developer Forum](https://forums.developer.nvidia.com/c/autonomous-vehicles/alpamayo/766). 🐛 **Code-level bugs, documentation issues, and feature requests**: file a [GitHub issue](../../issues/new/choose) using the appropriate template (Bug report, Documentation request, or Feature request). The relevant NVIDIA responder is auto-assigned via the `assignees:` field on the template. 🚨 **Security vulnerabilities**: please use [NVIDIA's Vulnerability Disclosure Program](https://app.intigriti.com/programs/nvidia/nvidiavdp/detail). Do not file security issues publicly here. ## Getting Started To run simulations locally (Docker Compose, single machine), see the [Tutorial](docs/TUTORIAL.md). The default tutorial path uses NuRec; for OmniDreams as the renderer backend, see the [Video Model Renderer guide](docs/VIDEO_MODEL.md). For cluster or SLURM deployment, see `src/tools/run-on-slurm`. ## Documentation & Resources - **[Onboarding Guide](docs/ONBOARDING.md)**: Initial setup and access instructions - **[Tutorial](docs/TUTORIAL.md)**: Step-by-step usage guide - **[Manual Dr
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c97ae412a2982528, topic:robotics, topic:autonomous-driving, topic:self-driving-car