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SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving

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arxiv 2404.06892 v1 pith:D4EC3NXF submitted 2024-04-10 cs.CV

Diankun Zhang , Guoan Wang , Runwen Zhu , Jianbo Zhao , Xiwu Chen , Siyu Zhang , Jiahao Gong , Qibin Zhou
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Wenyuan Zhang Ningzi Wang Feiyang Tan Hangning Zhou Ziyao Xu Haotian Yao Chi Zhang Xiaojun Liu Xiaoguang Di Bin Li
This is my paper · ORCID
classification cs.CV
keywords end-to-enddrivingmethodsautonomoussparseperformancesparseadtasks
verification ladder T0 review T1 audit T2 compute T3 formal
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End-to-End paradigms use a unified framework to implement multi-tasks in an autonomous driving system. Despite simplicity and clarity, the performance of end-to-end autonomous driving methods on sub-tasks is still far behind the single-task methods. Meanwhile, the widely used dense BEV features in previous end-to-end methods make it costly to extend to more modalities or tasks. In this paper, we propose a Sparse query-centric paradigm for end-to-end Autonomous Driving (SparseAD), where the sparse queries completely represent the whole driving scenario across space, time and tasks without any dense BEV representation. Concretely, we design a unified sparse architecture for perception tasks including detection, tracking, and online mapping. Moreover, we revisit motion prediction and planning, and devise a more justifiable motion planner framework. On the challenging nuScenes dataset, SparseAD achieves SOTA full-task performance among end-to-end methods and significantly narrows the performance gap between end-to-end paradigms and single-task methods. Codes will be released soon.

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Cited by 7 Pith papers

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  2. Impromptu VLA: Open Weights and Open Data for Driving Vision-Language-Action Models

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A new 80K-clip dataset of unstructured driving scenarios with Q&A annotations improves VLA performance on NeuroNCAP and nuScenes benchmarks.

  3. Autoregressive Meta-Actions for Unified Controllable Trajectory Generation

    cs.RO 2025-05 conditional novelty 6.0 of 10

    Frame-level meta-actions, predicted and injected at every time step in an autoregressive trajectory model, improve alignment between high-level driving decisions and generated motion.

  4. MindDrive: A Vision-Language-Action Model for Autonomous Driving via Online Reinforcement Learning

    cs.CV 2025-12 conditional novelty 5.0 of 10

    An autonomous-driving vision-language model that uses online RL over discrete language actions, with a separate action expert mapping decisions to trajectories, reports DS 78.04 and SR 55.09% on Bench2Drive.

  5. CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving

    cs.RO 2025-05 conditional novelty 5.0 of 10

    CogAD reports state-of-the-art open-loop and closed-loop planning results by combining hierarchical scene-to-instance perception with intent-to-trajectory planning and dual-level uncertainty.

  6. Challenger: Affordable Adversarial Driving Video Generation

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    A framework for automatic generation of photorealistic adversarial driving videos, shown to sharply increase collision rates of end-to-end autonomous driving models.

  7. A Survey on Vision-Language-Action Models for Autonomous Driving

    cs.CV 2025-06 conditional novelty 4.0 of 10

    A survey organizes vision-language-action models for autonomous driving into four stages, compares over 20 systems, and catalogs datasets, benchmarks, and open challenges.

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