Data to 14 Oct 2026

iPad: Iterative Proposal-centric End-to-End Autonomous Driving

arXiv 2505.15111 ↗First posted 21 May 2025cs.CV cs.AIcited by 42

On the map: End-to-end autonomous driving → End-to-end system architecture

Abstract (arXiv)

End-to-end (E2E) autonomous driving systems offer a promising alternative to traditional modular pipelines by reducing information loss and error accumulation, with significant potential to enhance both mobility and safety. However, most existing E2E approaches directly generate plans based on dense bird's-eye view (BEV) grid features, leading to inefficiency and limited planning awareness. To address these limitations, we propose iterative Proposal-centric autonomous driving (iPad), a novel framework that places proposals - a set of candidate future plans - at the center of feature extraction and auxiliary tasks. Central to iPad is ProFormer, a BEV encoder that iteratively refines proposals and their associated features through proposal-anchored attention, effectively fusing multi-view image data. Additionally, we introduce two lightweight, proposal-centric auxiliary tasks - mapping and prediction - that improve planning quality with minimal computational overhead. Extensive experiments on the NAVSIM and CARLA Bench2Drive benchmarks demonstrate that iPad achieves state-of-the-art performance while being significantly more efficient than prior leading methods.

Key facts. Cited by 42 papers in the map (data through 14 Oct 2026). Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving. Builds on 43 of its 49 references that are in the map. First posted on arXiv 21 May 2025.

Builds on

43 of 49 references.

Closest ancestors · 14

Same direction, newest first

2 more

Other references · 29

2015Deep Residual Learning for Image Recognitioncited by 52,096
2020Denoising Diffusion Probabilistic Modelscited by 21,076
2023GPT-4 Technical Reportcited by 18,396
2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,422
2017CARLA: An Open Urban Driving Simulatorcited by 2,449
2017Deformable Convolutional Networkscited by 1,488
2022BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformerscited by 1,218
2022Planning-oriented Autonomous Drivingcited by 1,087
2020Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3Dcited by 1,044
2016End to End Learning for Self-Driving Carscited by 1,009
2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 690

17 more

2023VAD: Vectorized Scene Representation for Efficient Autonomous Drivingcited by 622
2019A Survey of Autonomous Driving: Common Practices and Emerging Technologiescited by 379
2019Exploring the Limitations of Behavior Cloning for Autonomous Drivingcited by 346
2024DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Drivingcited by 334
2023Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Drivingcited by 319
2024VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planningcited by 289
2019Causal Confusion in Imitation Learningcited by 288
2024Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillationcited by 249
2023Parting with Misconceptions about Learning-based Vehicle Motion Planningcited by 242
2024GenAD: Generative End-to-End Autonomous Drivingcited by 225
2018Conditional Affordance Learning for Driving in Urban Environmentscited by 70
2024Think2Drive: Efficient Reinforcement Learning by Thinking in Latent World Model for Quasi-Realistic Autonomous Driving (in CARLA-v2)cited by 52
2023PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Drivingcited by 33
2024GraphAD: Interaction Scene Graph for End-to-end Autonomous Drivingcited by 25
2024Hybrid-Prediction Integrated Planning for Autonomous Drivingcited by 18
2023CCIL: Context-conditioned imitation learning for urban drivingcited by 12
2024Curb Your Attention: Causal Attention Gating for Robust Trajectory Prediction in Autonomous Drivingcited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 9; 2026: 33.

42 papers.

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2025A Survey on Vision-Language-Action Models for Autonomous Drivingcited by 72
2026Driving on Registerscited by 44
2025RAP: 3D Rasterization Augmented End-to-End Planningcited by 41
2025SimScale: Learning to Drive via Real-World Simulation at Scalecited by 36
2026Drive-JEPA: Video JEPA Meets Multimodal Trajectory Distillation for End-to-End Drivingcited by 30
2026SparseDriveV2: Scoring is All You Need for End-to-End Autonomous Drivingcited by 26
2025Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Drivingcited by 19
2026SpanVLA: Efficient Action Bridging and Learning from Negative-Recovery Samples for Vision-Language-Action Modelcited by 17
2025PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Drivingcited by 16
2025ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Drivingcited by 14
2026CLOVER: Closed-Loop Value Estimation and Ranking for End-to-End Autonomous Driving Planningcited by 11
2026RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Frameworkcited by 8

30 more

2026The DAWN of World-Action Interactive Modelscited by 8
2026World Engine: Towards the Era of Post-Training for Autonomous Drivingcited by 8
2026Test-Time Trajectory Optimization for Autonomous Drivingcited by 7
2025KnowVal: A Knowledge-Augmented and Value-Guided Autonomous Driving Systemcited by 5
2026HiST-VLA: A Hierarchical Spatio-Temporal Vision-Language-Action Model for End-to-End Autonomous Drivingcited by 5
2026DA-WAM: Decision-Aligned Future Latents for Driving World Modelscited by 5
2025WPT: World-to-Policy Transfer via Online World Model Distillationcited by 4
2026ProDrive: Proactive Planning for Autonomous Driving via Ego-Environment Co-Evolutioncited by 4
2026ChainFlow-VLA: Causal Flow Planning with Vision-Language Modelscited by 4
2026Discrete-WAM: Unified Discrete Vision-Action Token Editing for World-Policy Learningcited by 4
2026Zero-Shot Cross-City Generalization in End-to-End Autonomous Driving: Self-Supervised versus Supervised Representationscited by 3
2026DriveZero: End-to-End Driving Beyond Human Demonstrationscited by 3
2026NTR: Neural Token Reconstruction for Scene Token Bottleneck in End-to-End Drivingcited by 2
2026IDOL: Inverse-Dynamics-Guided Future Prediction for End-to-End Autonomous Drivingcited by 2
2026CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Drivingcited by 2
2026GeoWorldAD: Geometry World Action Model for Autonomous Drivingcited by 2
2026AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Drivingcited by 1
2026A Unified Candidate Set with Scene-Adaptive Refinement via Diffusion for End-to-End Autonomous Drivingcited by 1
2026From Representational Complementarity to Dual Systems: Synergizing VLM and Vision-Only Backbones for End-to-End Drivingcited by 1
2026FlowR2A: Learning Reward-to-Action Distribution for Multimodal Driving Planningcited by 1
2026WCog-VLA: A Dual-Level World-Cognitive Vision-Language-Action Model for End-to-End Autonomous Drivingcited by 1
2025LADY: Linear Attention for Autonomous Driving Efficiency without Transformerscited by 0
2026D3-MoE:Dual Disentangled Diffusion Mixture-of-Experts for Style-Controllable End-to-End Autonomous Drivingcited by 0
2026Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Modelscited by 0
2026A Degradation-Tolerance Benchmark for Camera-Only End-to-End Drivingcited by 0
2026READ: Learning Risk-Informed Fields for End-to-End Autonomous Drivingcited by 0
2026S2Planner: Multi-Scale Semantic Planner for End-to-End Autonomous Drivingcited by 0
2026Evaluation Is All You Need for Multi-Modal Autonomous Drivingcited by 0
2026Progressive Risk Estimation for Accident Anticipationcited by 0
2026MORE: Learning Motion-Oriented Registers for Driving World Models and Planningcited by 0

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