DiffSemanticFusion: Semantic Raster BEV Fusion for Autonomous Driving via Online HD Map Diffusion
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916878356578304 |
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| author | Sun, Zhigang Wang, Yiru Jiang, Anqing Wang, Shuo Gao, Yu Heng, Yuwen Zhang, Shouyi He, An Jiang, Hao Chai, Jinhao Gu, Zichong Jijun, Wang Tang, Shichen Halilaj, Lavdim Luettin, Juergen Sun, Hao |
| author_facet | Sun, Zhigang Wang, Yiru Jiang, Anqing Wang, Shuo Gao, Yu Heng, Yuwen Zhang, Shouyi He, An Jiang, Hao Chai, Jinhao Gu, Zichong Jijun, Wang Tang, Shichen Halilaj, Lavdim Luettin, Juergen Sun, Hao |
| contents | Autonomous driving requires accurate scene understanding, including road geometry, traffic agents, and their semantic relationships. In online HD map generation scenarios, raster-based representations are well-suited to vision models but lack geometric precision, while graph-based representations retain structural detail but become unstable without precise maps. To harness the complementary strengths of both, we propose DiffSemanticFusion -- a fusion framework for multimodal trajectory prediction and planning. Our approach reasons over a semantic raster-fused BEV space, enhanced by a map diffusion module that improves both the stability and expressiveness of online HD map representations. We validate our framework on two downstream tasks: trajectory prediction and planning-oriented end-to-end autonomous driving. Experiments on real-world autonomous driving benchmarks, nuScenes and NAVSIM, demonstrate improved performance over several state-of-the-art methods. For the prediction task on nuScenes, we integrate DiffSemanticFusion with the online HD map informed QCNet, achieving a 5.1\% performance improvement. For end-to-end autonomous driving in NAVSIM, DiffSemanticFusion achieves state-of-the-art results, with a 15\% performance gain in NavHard scenarios. In addition, extensive ablation and sensitivity studies show that our map diffusion module can be seamlessly integrated into other vector-based approaches to enhance performance. All artifacts are available at https://github.com/SunZhigang7/DiffSemanticFusion. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01778 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DiffSemanticFusion: Semantic Raster BEV Fusion for Autonomous Driving via Online HD Map Diffusion Sun, Zhigang Wang, Yiru Jiang, Anqing Wang, Shuo Gao, Yu Heng, Yuwen Zhang, Shouyi He, An Jiang, Hao Chai, Jinhao Gu, Zichong Jijun, Wang Tang, Shichen Halilaj, Lavdim Luettin, Juergen Sun, Hao Computer Vision and Pattern Recognition Robotics Autonomous driving requires accurate scene understanding, including road geometry, traffic agents, and their semantic relationships. In online HD map generation scenarios, raster-based representations are well-suited to vision models but lack geometric precision, while graph-based representations retain structural detail but become unstable without precise maps. To harness the complementary strengths of both, we propose DiffSemanticFusion -- a fusion framework for multimodal trajectory prediction and planning. Our approach reasons over a semantic raster-fused BEV space, enhanced by a map diffusion module that improves both the stability and expressiveness of online HD map representations. We validate our framework on two downstream tasks: trajectory prediction and planning-oriented end-to-end autonomous driving. Experiments on real-world autonomous driving benchmarks, nuScenes and NAVSIM, demonstrate improved performance over several state-of-the-art methods. For the prediction task on nuScenes, we integrate DiffSemanticFusion with the online HD map informed QCNet, achieving a 5.1\% performance improvement. For end-to-end autonomous driving in NAVSIM, DiffSemanticFusion achieves state-of-the-art results, with a 15\% performance gain in NavHard scenarios. In addition, extensive ablation and sensitivity studies show that our map diffusion module can be seamlessly integrated into other vector-based approaches to enhance performance. All artifacts are available at https://github.com/SunZhigang7/DiffSemanticFusion. |
| title | DiffSemanticFusion: Semantic Raster BEV Fusion for Autonomous Driving via Online HD Map Diffusion |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2508.01778 |