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Openemma
Overview :
OpenEMMA is an open-source project that replicates Waymo's EMMA model, providing an end-to-end framework for motion planning of autonomous vehicles. This model utilizes pre-trained Vision-Language Models (VLMs) such as GPT-4 and LLaVA to integrate text and forward-facing camera inputs, achieving precise predictions of future path points and providing decision rationale. OpenEMMA aims to provide accessible tools for researchers and developers to advance research and applications in autonomous driving.
Target Users :
The target audience includes researchers and developers in the field of autonomous driving who need an end-to-end framework to implement and test driving algorithms. The open-source tools provided by OpenEMMA enable them to swiftly build their own autonomous driving systems and accelerate the R&D process through pre-trained models.
Use Cases
Researchers use OpenEMMA to test new autonomous driving algorithms on the nuScenes dataset.
Developers utilize the framework provided by OpenEMMA to build their own autonomous driving decision systems.
Educational institutions employ OpenEMMA as a teaching tool to demonstrate practical applications of autonomous driving technology.
Features
? Integrate text and visual inputs using pre-trained Vision-Language Models (VLMs)
? Accurately predict future path points for autonomous vehicles
? Provide rationale and explanations for model decisions
? Support external tools like YOLO-3D for key object detection
? Work with various models such as GPT-4, LLaVA, Llama, and Qwen2
? Generate visualizations of predicted paths and compiled videos
? Support the nuScenes dataset for model training and testing
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