FocalAD: Local Motion Planning for End-to-End Autonomous Driving
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915999377260544 |
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| author | Sun, Bin Zhang, Boao Lu, Jiayi Feng, Xinjie Shang, Jiachen Cao, Rui Zheng, Mengchao Wang, Chuanye Yang, Shichun Cao, Yaoguang Song, Ziying |
| author_facet | Sun, Bin Zhang, Boao Lu, Jiayi Feng, Xinjie Shang, Jiachen Cao, Rui Zheng, Mengchao Wang, Chuanye Yang, Shichun Cao, Yaoguang Song, Ziying |
| contents | In end-to-end autonomous driving,the motion prediction plays a pivotal role in ego-vehicle planning. However, existing methods often rely on globally aggregated motion features, ignoring the fact that planning decisions are primarily influenced by a small number of locally interacting agents. Failing to attend to these critical local interactions can obscure potential risks and undermine planning reliability. In this work, we propose FocalAD, a novel end-to-end autonomous driving framework that focuses on critical local neighbors and refines planning by enhancing local motion representations. Specifically, FocalAD comprises two core modules: the Ego-Local-Agents Interactor (ELAI) and the Focal-Local-Agents Loss (FLA Loss). ELAI conducts a graph-based ego-centric interaction representation that captures motion dynamics with local neighbors to enhance both ego planning and agent motion queries. FLA Loss increases the weights of decision-critical neighboring agents, guiding the model to prioritize those more relevant to planning. Extensive experiments show that FocalAD outperforms existing state-of-the-art methods on the open-loop nuScenes datasets and closed-loop Bench2Drive benchmark. Notably, on the robustness-focused Adv-nuScenes dataset, FocalAD achieves even greater improvements, reducing the average colilision rate by 41.9% compared to DiffusionDrive and by 15.6% compared to SparseDrive. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11419 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FocalAD: Local Motion Planning for End-to-End Autonomous Driving Sun, Bin Zhang, Boao Lu, Jiayi Feng, Xinjie Shang, Jiachen Cao, Rui Zheng, Mengchao Wang, Chuanye Yang, Shichun Cao, Yaoguang Song, Ziying Artificial Intelligence Robotics In end-to-end autonomous driving,the motion prediction plays a pivotal role in ego-vehicle planning. However, existing methods often rely on globally aggregated motion features, ignoring the fact that planning decisions are primarily influenced by a small number of locally interacting agents. Failing to attend to these critical local interactions can obscure potential risks and undermine planning reliability. In this work, we propose FocalAD, a novel end-to-end autonomous driving framework that focuses on critical local neighbors and refines planning by enhancing local motion representations. Specifically, FocalAD comprises two core modules: the Ego-Local-Agents Interactor (ELAI) and the Focal-Local-Agents Loss (FLA Loss). ELAI conducts a graph-based ego-centric interaction representation that captures motion dynamics with local neighbors to enhance both ego planning and agent motion queries. FLA Loss increases the weights of decision-critical neighboring agents, guiding the model to prioritize those more relevant to planning. Extensive experiments show that FocalAD outperforms existing state-of-the-art methods on the open-loop nuScenes datasets and closed-loop Bench2Drive benchmark. Notably, on the robustness-focused Adv-nuScenes dataset, FocalAD achieves even greater improvements, reducing the average colilision rate by 41.9% compared to DiffusionDrive and by 15.6% compared to SparseDrive. |
| title | FocalAD: Local Motion Planning for End-to-End Autonomous Driving |
| topic | Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2506.11419 |