End-to-End Driving papers, 2023–2026

E2E driving policies

Scope: NeurIPS, ICLR, ICML and CVPR, 2023–2026. 161 papers are tagged with this topic.

Key findings

Papers per year

Papers per venue

Counts by year and venue

End-to-End Driving papers by year and venue
YearNeurIPSICLRICMLCVPRTotal
20239381333
20248131931
202518541239
2026151242758

Representative papers

Spread across years and venues so every edition shows up

All End-to-End Driving papers

31 papers, page 1 of 1

  1. CVPR 2024DualAD: Disentangling the Dynamic and Static World for End-to-End Driving
  2. CVPR 2024End-to-End Spatio-Temporal Action Localisation with Video Transformers
  3. CVPR 2024End-to-End Temporal Action Detection with 1B Parameters Across 1000 Frames
  4. CVPR 2024Feedback-Guided Autonomous Driving
  5. CVPR 2024GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians
  6. CVPR 2024Generalized Predictive Model for Autonomous Driving
  7. CVPR 2024Generating Handwritten Mathematical Expressions From Symbol Graphs: An End-to-End Pipeline
  8. CVPR 2024HIMap: HybrId Representation Learning for End-to-end Vectorized HD Map Construction
  9. CVPR 2024Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving?
  10. CVPR 2024LMDrive: Closed-Loop End-to-End Driving with Large Language Models
  11. CVPR 2024NeuRAD: Neural Rendering for Autonomous Driving
  12. CVPR 2024On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous Driving
  13. CVPR 2024PACER+: On-Demand Pedestrian Animation Controller in Driving Scenarios
  14. CVPR 2024PARA-Drive: Parallelized Architecture for Real-time Autonomous Driving
  15. CVPR 2024PatchFusion: An End-to-End Tile-Based Framework for High-Resolution Monocular Metric Depth Estimation
  16. CVPR 2024PixelRNN: In-pixel Recurrent Neural Networks for End-to-end-optimized Perception with Neural Sensors
  17. CVPR 2024SynFog: A Photo-realistic Synthetic Fog Dataset based on End-to-end Imaging Simulation for Advancing Real-World Defogging in Autonomous Driving
  18. CVPR 2024Uncertainty-Guided Never-Ending Learning to Drive
  19. CVPR 2024UniPAD: A Universal Pre-training Paradigm for Autonomous Driving
  20. ICLR 2024DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view Representation
  21. ICML 2024Correcting Diffusion-Based Perceptual Image Compression with Privileged End-to-End Decoder
  22. ICML 2024End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations
  23. ICML 2024Learning Exceptional Subgroups by End-to-End Maximizing KL-Divergence
  24. NeurIPS 2024Autonomous Driving with Spiking Neural Networks
  25. NeurIPS 2024Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving
  26. NeurIPS 2024E2E-MFD: Towards End-to-End Synchronous Multimodal Fusion Detection
  27. NeurIPS 2024End-to-End Ontology Learning with Large Language Models
  28. NeurIPS 2024Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning
  29. NeurIPS 2024Learning to be Smooth: An End-to-End Differentiable Particle Smoother
  30. NeurIPS 2024Noisy Label Learning with Instance-Dependent Outliers: Identifiability via Crowd Wisdom
  31. NeurIPS 2024OpenCDA-Loop: A Closed-loop Benchmarking Platform for End-to-end Evaluation of Cooperative Perception

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FAQ

How many End-to-End Driving papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?
161 in total: 33 in 2023, 31 in 2024, 39 in 2025, 58 in 2026.
How many End-to-End Driving papers are at NeurIPS 2026?
NeurIPS 2026 has 15 End-to-End Driving papers, versus 18 the year before.
How many End-to-End Driving papers are at ICLR 2026?
ICLR 2026 has 12 End-to-End Driving papers, versus 5 the year before.
How many End-to-End Driving papers are at ICML 2026?
ICML 2026 has 4 End-to-End Driving papers, versus 4 the year before.
How many End-to-End Driving papers are at CVPR 2026?
CVPR 2026 has 27 End-to-End Driving papers, versus 12 the year before.
Which conference publishes the most End-to-End Driving papers?
CVPR, with 71 papers from 2023 to 2026.

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