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README.md
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**Quantitative Evaluation Benchmarks:**
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- Reasoning Evaluation using [LingoQA](https://github.com/wayveai/LingoQA): Lingo-Judge Score of 79.2.
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- Closed-Loop Evaluation using [AlpaSim](https://github.com/NVlabs/alpasim) on
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- Open-Loop Evaluation on 1434 challenging samples from the [PhysicalAI-AV Dataset](https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles): minADE_6 at 6.4s of 0.911m.
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Additional evaluations and comparisons to prior models are visualized below:
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**Quantitative Evaluation Benchmarks:**
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- Reasoning Evaluation using [LingoQA](https://github.com/wayveai/LingoQA): Lingo-Judge Score of 79.2.
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- Closed-Loop Evaluation using [AlpaSim](https://github.com/NVlabs/alpasim) on 913 scenarios from the [PhysicalAI-AV-NuRec Dataset](https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles-NuRec): AlpaSim Score of 1.50 ± 0.13.
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- Open-Loop Evaluation on 1434 challenging samples from the [PhysicalAI-AV Dataset](https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles): minADE_6 at 6.4s of 0.911m.
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Additional evaluations and comparisons to prior models are visualized below:
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