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arxiv: 2412.15208 · v2 · pith:LWOWB2TD · submitted 2024-12-19 · cs.CV · cs.LG· cs.RO

OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Driving

Shuo Xing , Chengyuan Qian , Yuping Wang , Hongyuan Hua , Kexin Tian , Yang Zhou , Zhengzhong Tu This is my paper

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classification cs.CV cs.LGcs.RO
keywords drivingopenemmaend-to-endautonomousmllmssignificantacrossmodels
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Since the advent of Multimodal Large Language Models (MLLMs), they have made a significant impact across a wide range of real-world applications, particularly in Autonomous Driving (AD). Their ability to process complex visual data and reason about intricate driving scenarios has paved the way for a new paradigm in end-to-end AD systems. However, the progress of developing end-to-end models for AD has been slow, as existing fine-tuning methods demand substantial resources, including extensive computational power, large-scale datasets, and significant funding. Drawing inspiration from recent advancements in inference computing, we propose OpenEMMA, an open-source end-to-end framework based on MLLMs. By incorporating the Chain-of-Thought reasoning process, OpenEMMA achieves significant improvements compared to the baseline when leveraging a diverse range of MLLMs. Furthermore, OpenEMMA demonstrates effectiveness, generalizability, and robustness across a variety of challenging driving scenarios, offering a more efficient and effective approach to autonomous driving. We release all the codes in https://github.com/taco-group/OpenEMMA.

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Cited by 4 Pith papers

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    ReCogDrive unifies VLM scene understanding with a diffusion planner reinforced by DiffGRPO to reach state-of-the-art results on NAVSIM and Bench2Drive benchmarks.

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  3. OneDrive: Unified Multi-Paradigm Driving with Vision-Language-Action Models

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  4. VERDI: VLM-Embedded Reasoning for Autonomous Driving

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    VERDI aligns perception, prediction, and planning outputs of end-to-end AD models with VLM-generated text features at training time to embed structured reasoning, yielding up to 11% better l2 distance and 10% higher n...