端到端驾驶论文趋势 2023–2026
E2E 驾驶策略
统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 161 篇被归入该主题的论文。
主要结论
- 端到端驾驶论文从 2023 年的 33 篇增长到 2026 年的 58 篇，三年间扩大到约 1.8 倍。
- CVPR 是发端到端驾驶论文最多的会议，2023–2026 年共 71 篇，占四个会议总量的 44%。
- 交叉方向里涨得最快的是多模态大模型：同时属于两个主题的论文从 2023 年的 0 篇增至 2026 年的 11 篇。
- 在「自动驾驶」的 5 个主题里，它的 2026 年论文数排第 5；同组增长最快的运动规划是 2.4 倍，端到端驾驶是 1.8 倍。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 9 | 3 | 8 | 13 | 33 |
| 2024 | 8 | 1 | 3 | 19 | 31 |
| 2025 | 18 | 5 | 4 | 12 | 39 |
| 2026 | 15 | 12 | 4 | 27 | 58 |
代表论文
按年份和会议均匀挑选，方便快速看到每一届的样貌
- NeurIPS 2026AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving
- ICML 2026Hide&Seek: Learning to Explain in an End-to-End Differentiable Network
- ICLR 2026RAP: 3D Rasterization Augmented End-to-End Planning
- CVPR 2026Bias at the End of the Score
- NeurIPS 2025Embodied Cognition Augmented End2End Autonomous Driving
- ICML 2025GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance
- ICLR 2025MGMapNet: Multi-Granularity Representation Learning for End-to-End Vectorized HD Map Construction
- CVPR 2025Bridging Past and Future: End-to-End Autonomous Driving with Historical Prediction and Planning
- NeurIPS 2024Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving
- ICML 2024Learning Exceptional Subgroups by End-to-End Maximizing KL-Divergence
- ICLR 2024DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view Representation
- CVPR 2024HIMap: HybrId Representation Learning for End-to-end Vectorized HD Map Construction
- NeurIPS 2023[Re] End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking
- ICML 2023End-to-end Differentiable Clustering with Associative Memories
- ICLR 2023E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking
- CVPR 2023End-to-End Vectorized HD-Map Construction With Piecewise Bezier Curve
- NeurIPS 2026On the Existence of Uniformly Optimal Policies in MDPs under Epistemic Uncertainty
- ICML 2026RoCA: Robust Cross-Domain End-to-End Autonomous Driving
- ICLR 2026THE END OF MANUAL DECODING: TOWARDS TRULY END-TO-END LANGUAGE MODELS
- CVPR 2026LEAD Minimizing Learner-Expert Asymmetry in End-to-End Driving
全部端到端驾驶论文
共 21 篇，第 1 / 1 页
- ICLR 2026AutoDV: An End-to-End Deep Learning Model for High-Dimensional Data Visualization
- ICLR 2026BridgeDrive: Diffusion Bridge Policy for Closed-Loop Trajectory Planning in Autonomous Driving
- ICLR 2026ComPhy: Composing Physical Models with end-to-end Alignment
- ICLR 2026DriveMamba: Task-Centric Scalable State Space Model for Efficient End-to-End Autonomous Driving
- ICLR 2026Muon Outperforms Adam in Tail-End Associative Memory Learning
- ICLR 2026Optimal transport unlocks end-to-end learning for single-molecule localization
- ICLR 2026RAP: 3D Rasterization Augmented End-to-End Planning
- ICLR 2026RAVEN: End-to-end Equivariant Robot Learning with RGB Cameras
- ICLR 2026ResWorld: Temporal Residual World Model for End-to-End Autonomous Driving
- ICLR 2026THE END OF MANUAL DECODING: TOWARDS TRULY END-TO-END LANGUAGE MODELS
- ICLR 2026Uni-X: Mitigating Modality Conflict with a Two-End-Separated Architecture for Unified Multimodal Models
- ICLR 2026VADv2: End-to-End Autonomous Driving via Probabilistic Planning
- ICLR 2025DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving
- ICLR 2025Enhancing End-to-End Autonomous Driving with Latent World Model
- ICLR 2025MGMapNet: Multi-Granularity Representation Learning for End-to-End Vectorized HD Map Construction
- ICLR 2025Navigation-Guided Sparse Scene Representation for End-to-End Autonomous Driving
- ICLR 2025PETRA: Parallel End-to-end Training with Reversible Architectures
- ICLR 2024DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view Representation
- ICLR 2023DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
- ICLR 2023E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking
- ICLR 2023Systematic Rectification of Language Models via Dead-end Analysis
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查端到端驾驶在 CVPR 2023–2026 的论文趋势，哪一年涨得最快？
AI 会调用的工具：get_topic_stats {"topic": "end-to-end-driving"}
常见问题
- 2023–2026 年四大会议一共有多少篇端到端驾驶论文？
- 共 161 篇，按年份分别是 2023 年 33 篇、2024 年 31 篇、2025 年 39 篇、2026 年 58 篇。
- NeurIPS 2026 有多少篇端到端驾驶论文？
- NeurIPS 2026 有 15 篇端到端驾驶论文，上一届是 18 篇。
- ICLR 2026 有多少篇端到端驾驶论文？
- ICLR 2026 有 12 篇端到端驾驶论文，上一届是 5 篇。
- ICML 2026 有多少篇端到端驾驶论文？
- ICML 2026 有 4 篇端到端驾驶论文，上一届是 4 篇。
- CVPR 2026 有多少篇端到端驾驶论文？
- CVPR 2026 有 27 篇端到端驾驶论文，上一届是 12 篇。
- 哪个会议的端到端驾驶论文最多？
- CVPR，2023–2026 年共 71 篇。