DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916773957206016 |
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| author | Jiang, Anqing Gao, Yu Sun, Zhigang Wang, Yiru Wang, Jijun Chai, Jinghao Cao, Qian Heng, Yuweng Jiang, Hao Dong, Yunda Zhang, Zongzheng Guo, Xianda Sun, Hao Zhao, Hao |
| author_facet | Jiang, Anqing Gao, Yu Sun, Zhigang Wang, Yiru Wang, Jijun Chai, Jinghao Cao, Qian Heng, Yuweng Jiang, Hao Dong, Yunda Zhang, Zongzheng Guo, Xianda Sun, Hao Zhao, Hao |
| contents | Research interest in end-to-end autonomous driving has surged owing to its fully differentiable design integrating modular tasks, i.e. perception, prediction and planing, which enables optimization in pursuit of the ultimate goal. Despite the great potential of the end-to-end paradigm, existing methods suffer from several aspects including expensive BEV (bird's eye view) computation, action diversity, and sub-optimal decision in complex real-world scenarios. To address these challenges, we propose a novel hybrid sparse-dense diffusion policy, empowered by a Vision-Language Model (VLM), called Diff-VLA. We explore the sparse diffusion representation for efficient multi-modal driving behavior. Moreover, we rethink the effectiveness of VLM driving decision and improve the trajectory generation guidance through deep interaction across agent, map instances and VLM output. Our method shows superior performance in Autonomous Grand Challenge 2025 which contains challenging real and reactive synthetic scenarios. Our methods achieves 45.0 PDMS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19381 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving Jiang, Anqing Gao, Yu Sun, Zhigang Wang, Yiru Wang, Jijun Chai, Jinghao Cao, Qian Heng, Yuweng Jiang, Hao Dong, Yunda Zhang, Zongzheng Guo, Xianda Sun, Hao Zhao, Hao Artificial Intelligence Computer Vision and Pattern Recognition Robotics Research interest in end-to-end autonomous driving has surged owing to its fully differentiable design integrating modular tasks, i.e. perception, prediction and planing, which enables optimization in pursuit of the ultimate goal. Despite the great potential of the end-to-end paradigm, existing methods suffer from several aspects including expensive BEV (bird's eye view) computation, action diversity, and sub-optimal decision in complex real-world scenarios. To address these challenges, we propose a novel hybrid sparse-dense diffusion policy, empowered by a Vision-Language Model (VLM), called Diff-VLA. We explore the sparse diffusion representation for efficient multi-modal driving behavior. Moreover, we rethink the effectiveness of VLM driving decision and improve the trajectory generation guidance through deep interaction across agent, map instances and VLM output. Our method shows superior performance in Autonomous Grand Challenge 2025 which contains challenging real and reactive synthetic scenarios. Our methods achieves 45.0 PDMS. |
| title | DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2505.19381 |