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drivefusion
drivefusion PublicDriveFusion is an open-source multimodal Vision–Language–Action model for autonomous driving that fuses visual perception, language understanding, and driving context (GPS and speed) to describe dr…
Python 2
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carla-data-collection
carla-data-collection PublicAutonomous-driving data pipeline for DriveFusion project built on the CARLA Simulator, generating cleaned multi-modal sensor data and VQA annotations for training vision-language action models.
Python 1
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data-preprocessing
data-preprocessing PublicData preprocessing pipeline for the DriveFusionQA. It converts multiple autonomous-driving QA datasets into unified LLaMA and LLaVA-style instruction formats, with modular dataset preprocessors, JS…
Python
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Evaluate-Models
Evaluate-Models PublicEvaluation framework for the DriveFusion DriveFusionQA vision-language model, benchmarking Q&A performance on driving datasets using metrics like Lingo-Judge, BLEU, and BERTScore.
Python
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drivefusion-train
drivefusion-train PublicTraining framework for the DriveFusion project that fine-tunes and train LLMs and multimodal vision-language models for driving tasks. Built on LLaMAFactory, it adds dataset processing, distributed…
Python
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car-deployment
car-deployment PublicAutonomous vehicle deployment system for DriveFusion combining ROS-based control with AI vision powered by the Qwen 2.5 vision-language model, enabling real-time driving, single-shot decisions, and…
Python
Repositories
- drivefusion Public
DriveFusion is an open-source multimodal Vision–Language–Action model for autonomous driving that fuses visual perception, language understanding, and driving context (GPS and speed) to describe driving scenes and predict future trajectories and target speeds.
- carla-data-collection Public
Autonomous-driving data pipeline for DriveFusion project built on the CARLA Simulator, generating cleaned multi-modal sensor data and VQA annotations for training vision-language action models.
- drivefusion-train Public
Training framework for the DriveFusion project that fine-tunes and train LLMs and multimodal vision-language models for driving tasks. Built on LLaMAFactory, it adds dataset processing, distributed training workflows, optimization for scalable autonomous-driving model development.
- car-deployment Public
Autonomous vehicle deployment system for DriveFusion combining ROS-based control with AI vision powered by the Qwen 2.5 vision-language model, enabling real-time driving, single-shot decisions, and video/image processing.
- data-preprocessing Public
Data preprocessing pipeline for the DriveFusionQA. It converts multiple autonomous-driving QA datasets into unified LLaMA and LLaVA-style instruction formats, with modular dataset preprocessors, JSON creators, and validation tools to support training and evaluation of vision-language models.
- Evaluate-Models Public
Evaluation framework for the DriveFusion DriveFusionQA vision-language model, benchmarking Q&A performance on driving datasets using metrics like Lingo-Judge, BLEU, and BERTScore.
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