{"total":79,"rows":[{"id":"2506.13757","title":"AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning","created":"2025-06-16","cited_by_count":260,"area":"lr"},{"id":"2506.08052","title":"ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving","created":"2025-06-09","cited_by_count":198,"area":"mllm"},{"id":"2504.01941","title":"End-to-End Driving with Online Trajectory Evaluation via BEV World Model","created":"2025-04-02","cited_by_count":151,"area":"wm"},{"id":"2512.15692","title":"mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs","created":"2025-12-17","cited_by_count":142,"area":"vla"},{"id":"2503.07608","title":"AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning","created":"2025-03-10","cited_by_count":108,"area":"lr"},{"id":"2512.07745","title":"DiffusionDriveV2: Reinforcement Learning-Constrained Truncated Diffusion Modeling in End-to-End Autonomous Driving","created":"2025-12-08","cited_by_count":56,"area":"diffusion"},{"id":"2512.23421","title":"DriveLaW:Unifying Planning and Video Generation in a Latent Driving World","created":"2025-12-29","cited_by_count":47,"area":"wm"},{"id":"2509.18905","title":"How Far are VLMs from Visual Spatial Intelligence? A Benchmark-Driven Perspective","created":"2025-09-23","cited_by_count":43,"area":"lr"},{"id":"2503.11650","title":"Centaur: Robust End-to-End Autonomous Driving with Test-Time Training","created":"2025-03-14","cited_by_count":42,"area":"driving"},{"id":"2510.04333","title":"RAP: 3D Rasterization Augmented End-to-End Planning","created":"2025-10-05","cited_by_count":41,"area":"driving"},{"id":"2511.23369","title":"SimScale: Learning to Drive via Real-World Simulation at Scale","created":"2025-11-28","cited_by_count":36,"area":null},{"id":"2510.18313","title":"OmniNWM: Omniscient Driving Navigation World Models","created":"2025-10-21","cited_by_count":33,"area":"wm"},{"id":"2512.16760","title":"Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future","created":"2025-12-18","cited_by_count":33,"area":"vla"},{"id":"2508.06571","title":"IRL-VLA: Training an Vision-Language-Action Policy via Reward World Model","created":"2025-08-07","cited_by_count":31,"area":"wm"},{"id":"2512.13636","title":"MindDrive: A Vision-Language-Action Model for Autonomous Driving via Online Reinforcement Learning","created":"2025-12-15","cited_by_count":25,"area":"vla"},{"id":"2503.12552","title":"MTGS: Multi-Traversal Gaussian Splatting","created":"2025-03-16","cited_by_count":19,"area":null},{"id":"2506.09800","title":"Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving","created":"2025-06-11","cited_by_count":19,"area":"driving"},{"id":"2507.04049","title":"DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving","created":"2025-07-05","cited_by_count":17,"area":"diffusion"},{"id":"2507.17596","title":"PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Driving","created":"2025-07-23","cited_by_count":16,"area":"driving"},{"id":"2510.24108","title":"Zero-Human Demonstration End-to-end Autonomous Driving with Trajectory Scorer","created":"2025-10-28","cited_by_count":16,"area":"driving"},{"id":"2603.24587","title":"DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving","created":"2026-03-25","cited_by_count":15,"area":"wm"},{"id":"2510.08562","title":"ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving","created":"2025-10-09","cited_by_count":14,"area":"driving"},{"id":"2511.20325","title":"AD-R1: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving with Impartial World Models","created":"2025-11-25","cited_by_count":14,"area":"wm"},{"id":"2605.10426","title":"CoWorld-VLA: Thinking in a Multi-Expert World Model for Autonomous Driving","created":"2026-05-11","cited_by_count":12,"area":"wm"},{"id":"2506.09518","title":"HAIF-GS: Hierarchical and Induced Flow-Guided Gaussian Splatting for Dynamic Scene","created":"2025-06-11","cited_by_count":11,"area":null},{"id":"2603.27287","title":"Uni-World VLA: Interleaved World Modeling and Planning for Autonomous Driving","created":"2026-03-28","cited_by_count":11,"area":"wm"},{"id":"2602.21952","title":"MindDriver: Introducing Progressive Multimodal Reasoning for Autonomous Driving","created":"2026-02-25","cited_by_count":10,"area":"lr"},{"id":"2503.14537","title":"Learning-based 3D Reconstruction in Autonomous Driving: A Comprehensive Survey","created":"2025-03-17","cited_by_count":9,"area":"driving"},{"id":"2604.10856","title":"BridgeSim: Unveiling the OL-CL Gap in End-to-End Autonomous Driving","created":"2026-04-12","cited_by_count":9,"area":"driving"},{"id":"2512.17370","title":"TakeAD: Preference-based Post-optimization for End-to-end Autonomous Driving with Expert Takeover Data","created":"2025-12-19","cited_by_count":8,"area":"driving"},{"id":"2604.15308","title":"RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework","created":"2026-04-16","cited_by_count":8,"area":"deeprl"},{"id":"2606.01164","title":"Towards Interactive Video World Modeling: Frontiers, Challenges, Benchmarks, and Future Trends","created":"2026-05-31","cited_by_count":8,"area":"wm"},{"id":"2606.19836","title":"World Engine: Towards the Era of Post-Training for Autonomous Driving","created":"2026-06-18","cited_by_count":8,"area":"driving"},{"id":"2505.08854","title":"Generative AI for Autonomous Driving: Frontiers and Opportunities","created":"2025-05-13","cited_by_count":7,"area":"driving"},{"id":"2511.21584","title":"Model-Based Policy Adaptation for Closed-Loop End-to-End Autonomous Driving","created":"2025-11-26","cited_by_count":7,"area":"driving"},{"id":"2601.01386","title":"ParkGaussian: Surround-view 3D Gaussian Splatting for Autonomous Parking","created":"2026-01-04","cited_by_count":7,"area":null},{"id":"2506.23042","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","created":"2025-06-29","cited_by_count":6,"area":null},{"id":"2601.11475","title":"Generative Scenario Rollouts for End-to-End Autonomous Driving","created":"2026-01-16","cited_by_count":6,"area":"vla"},{"id":"2506.22099","title":"B\\'ezierGS: Dynamic Urban Scene Reconstruction with B\\'ezier Curve Gaussian Splatting","created":"2025-06-27","cited_by_count":5,"area":null},{"id":"2510.15446","title":"VDRive: Leveraging Reinforced VLA and Diffusion Policy for End-to-end Autonomous Driving","created":"2025-10-17","cited_by_count":5,"area":"diffusion"},{"id":"2511.08214","title":"Prioritizing Perception-Guided Self-Supervision: A New Paradigm for Causal Modeling in End-to-End Autonomous Driving","created":"2025-11-11","cited_by_count":5,"area":"driving"},{"id":"2603.11219","title":"Senna-2: Aligning VLM and End-to-End Driving Policy for Consistent Decision Making and Planning","created":"2026-03-11","cited_by_count":5,"area":"mllm"},{"id":"2605.10904","title":"MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems","created":"2026-05-11","cited_by_count":5,"area":"driving"},{"id":"2605.18137","title":"Xiaomi Auto World Model: A Joint World Model Integrating Reconstruction and Generation for Autonomous Driving","created":"2026-05-18","cited_by_count":5,"area":"wm"},{"id":"2511.20095","title":"WPT: World-to-Policy Transfer via Online World Model Distillation","created":"2025-11-25","cited_by_count":4,"area":"wm"},{"id":"2603.13842","title":"Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving","created":"2026-03-14","cited_by_count":4,"area":"driving"},{"id":"2604.03581","title":"HAD: Combining Hierarchical Diffusion with Metric-Decoupled RL for End-to-End Driving","created":"2026-04-04","cited_by_count":4,"area":"driving"},{"id":"2606.19641","title":"Scaling Self-Play for End-to-End Driving","created":"2026-06-17","cited_by_count":4,"area":"driving"},{"id":"2602.20943","title":"UFO: Unifying Feed-Forward and Optimization-based Methods for Large Driving Scene Modeling","created":"2026-02-24","cited_by_count":3,"area":"driving"},{"id":"2603.09465","title":"EvoDriveVLA: Evolving Driving VLA Models via Collaborative Perception-Planning Distillation","created":"2026-03-10","cited_by_count":3,"area":"vla"},{"id":"2606.17386","title":"TerraTransfer: Learning End-to-End Driving Policies Without Expert Demonstrations","created":"2026-06-16","cited_by_count":3,"area":"driving"},{"id":"2609.06055","title":"DriveZero: End-to-End Driving Beyond Human Demonstrations","created":"2026-09-05","cited_by_count":3,"area":"driving"},{"id":"2503.23650","title":"A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective","created":"2025-03-31","cited_by_count":2,"area":"driving"},{"id":"2509.20938","title":"Autoregressive End-to-End Planning with Time-Invariant Spatial Alignment and Multi-Objective Policy Refinement","created":"2025-09-25","cited_by_count":2,"area":"alignment"},{"id":"2601.22930","title":"MTDrive: Multi-turn Interactive Reinforcement Learning for Autonomous Driving","created":"2026-01-30","cited_by_count":2,"area":"driving"},{"id":"2603.19675","title":"DynFlowDrive: Flow-Based Dynamic World Modeling for Autonomous Driving","created":"2026-03-20","cited_by_count":2,"area":"wm"},{"id":"2604.11734","title":"SCORP: Scene-Consistent Multi-agent Diffusion Planning with Stable Online Reinforcement Post-Training for Cooperative Driving","created":"2026-04-13","cited_by_count":2,"area":"driving"},{"id":"2605.24354","title":"SparseWorld: Enhancing End-to-End Autonomous Driving via World Models with Sparse Scene Representation","created":"2026-05-23","cited_by_count":2,"area":"wm"},{"id":"2605.25373","title":"Physics-Aware 3D Gaussian Editing for Driving Scene Generation","created":"2026-05-25","cited_by_count":2,"area":null},{"id":"2606.27504","title":"ReWorld: Representation Learning for World Action Models","created":"2026-06-25","cited_by_count":2,"area":"wm"},{"id":"2607.02841","title":"CLEAR: Closed-Loop Reinforcement Learning at Scale for End-to-End Autonomous Driving","created":"2026-07-03","cited_by_count":2,"area":"vla"},{"id":"2512.07135","title":"TrajMoE: Scene-Adaptive Trajectory Planning with Mixture of Experts and Reinforcement Learning","created":"2025-12-08","cited_by_count":1,"area":"moe"},{"id":"2601.01762","title":"AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving","created":"2026-01-05","cited_by_count":1,"area":"driving"},{"id":"2603.14908","title":"PerlAD: Towards Enhanced Closed-loop End-to-end Autonomous Driving with Pseudo-simulation-based Reinforcement Learning","created":"2026-03-16","cited_by_count":1,"area":"driving"},{"id":"2609.20756","title":"OPTED: On-Policy Fine-Tuning for End-to-End Driving using a Render-Free Teacher","created":"2026-09-17","cited_by_count":1,"area":"driving"},{"id":"2510.12560","title":"CoIRL-AD: Collaborative-Competitive Imitation-Reinforcement Learning in Latent World Models for Autonomous Driving","created":"2025-10-14","cited_by_count":0,"area":"wm"},{"id":"2512.18662","title":"Pseudo-Expert Regularized Offline RL for End-to-End Autonomous Driving in Photorealistic Closed-Loop Environments","created":"2025-12-21","cited_by_count":0,"area":"driving"},{"id":"2602.07101","title":"Zero-Shot UAV Navigation in Forests via Relightable 3D Gaussian Splatting","created":"2026-02-06","cited_by_count":0,"area":null},{"id":"2604.28111","title":"GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment","created":"2026-04-30","cited_by_count":0,"area":"driving"},{"id":"2605.25029","title":"ParkingWorld: End-to-End Autonomous Parking Reinforcement Learning from Corrective Experience in 3DGS Simulation","created":"2026-05-24","cited_by_count":0,"area":"deeprl"},{"id":"2606.31918","title":"DriveWeaver: Point-Conditioned Video Inpainting for Controllable Vehicle Insertion in Autonomous Driving Simulation","created":"2026-06-30","cited_by_count":0,"area":"wm"},{"id":"2608.12203","title":"GeoFlow: Efficient Driving Video Generation via Geometry-Aligned Priors","created":"2026-08-12","cited_by_count":0,"area":"diffusion"},{"id":"2608.17420","title":"SPVC: Structured and Panoptic Video Fixing for Cross-Dataset Driving Scene Rendering","created":"2026-08-18","cited_by_count":0,"area":null},{"id":"2609.02462","title":"VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation","created":"2026-09-02","cited_by_count":0,"area":"driving"},{"id":"2609.26792","title":"DreamStream: Towards Policy-Oriented Generative Simulation for End-to-End Driving","created":"2026-09-22","cited_by_count":0,"area":"driving"},{"id":"2609.36851","title":"RoXDrive: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving via Action-Faithful Rollouts","created":"2026-09-29","cited_by_count":0,"area":"wm"},{"id":"2609.38862","title":"Efficient Multi-Modal Planning with Reward-Guided Preference Optimization for Autonomous Driving","created":"2026-09-30","cited_by_count":0,"area":"driving"},{"id":"2610.16908","title":"ConDenseDrive: Knowledge-Dense, Token-Sparse Visual Encoder for End-to-End Autonomous Driving","created":"2026-10-12","cited_by_count":0,"area":"driving"},{"id":"2610.17903","title":"DriveWLA: Unifying Future Dynamics and Scene Semantics through World-Language-Action Modeling for Autonomous Driving","created":"2026-10-13","cited_by_count":0,"area":"wm"}],"next_cursor":null}