| HiP-AD: Hierarchical and Multi-Granularity Planning with Deformable Attention for Autonomous Driving in a Single Decoder | ✓ Link | 86.77 | HiP-AD | 2025-03-11 |
| []() | | 86.28 | R2SE | |
| CarLLaVA: Vision language models for camera-only closed-loop driving | ✓ Link | 85.94 | SimLingo-Base (CarLLaVa) | 2024-06-14 |
| Hidden Biases of End-to-End Driving Models | ✓ Link | 84.21 | TransFuser++ | 2023-06-13 |
| GaussianFusion: Gaussian-Based Multi-Sensor Fusion for End-to-End Autonomous Driving | | 79.4 | GaussianFusion | 2025-05-27 |
| ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation | | 77.7 | ORION | 2025-03-25 |
| Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2) | | 74.36 | Raw2Drive | 2025-05-22 |
| []() | | 74.33 | ETA | |
| DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving | | 74.22 | DriveMoE | 2025-05-22 |
| Hydra-NeXt: Robust Closed-Loop Driving with Open-Loop Training | ✓ Link | 73.86 | Hydra-NeXt | 2025-03-15 |
| Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning | | 73.29 | VL (on failure) | 2024-06-03 |
| []() | | 68.90 | DRIVER | |
| DiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving | | 67.92 | DiffAD | 2025-03-15 |
| []() | | 67.17 | NavigationDrive | |
| iPad: Iterative Proposal-centric End-to-End Autonomous Driving | ✓ Link | 65.02 | iPad | 2025-05-21 |
| DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving | ✓ Link | 64.22 | DriveAdapter | 2023-08-01 |
| ReasonPlan: Unified Scene Prediction and Decision Reasoning for Closed-loop Autonomous Driving | ✓ Link | 64.01 | ReasonPlan | 2025-05-26 |
| DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving | ✓ Link | 63.46 | Drivetransformer-Large | 2025-03-07 |
| Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving | ✓ Link | 62.44 | ThinkTwice | 2023-05-10 |
| Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline | ✓ Link | 59.90 | TCP-traj | 2022-06-16 |
| DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving | ✓ Link | 52.02 | DiFSD | 2024-09-15 |
| X-Driver: Explainable Autonomous Driving with Vision-Language Models | | 51.70 | X-Driver | 2025-05-08 |
| Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline | ✓ Link | 49.30 | TCP-traj w/o distillation | 2022-06-16 |
| CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving | | 48.30 | CogAD | 2025-05-27 |
| []() | | 47.91 | MomAD | |
| Planning-oriented Autonomous Driving | ✓ Link | 45.81 | UniAD-Base | 2022-12-20 |
| Two Tasks, One Goal: Uniting Motion and Planning for Excellent End To End Autonomous Driving Performance | | 45.23 | TTOG | 2025-04-17 |
| GenAD: Generative End-to-End Autonomous Driving | ✓ Link | 44.81 | GenAD | 2024-02-18 |
| SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation | ✓ Link | 44.54 | SparseDrive | 2024-05-30 |
| VAD: Vectorized Scene Representation for Efficient Autonomous Driving | ✓ Link | 42.35 | VAD | 2023-03-21 |
| Planning-oriented Autonomous Driving | ✓ Link | 40.73 | UniAD-Tiny | 2022-12-20 |
| Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline | ✓ Link | 40.70 | TCP | 2022-06-16 |
| From Failures to Fixes: LLM-Driven Scenario Repair for Self-Evolving Autonomous Driving | | 35.64 | VAD + SERA | 2025-05-28 |
| Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline | ✓ Link | 30.47 | TCP-ctrl | 2022-06-16 |
| Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving | ✓ Link | 18.05 | AD-MLP | 2024-06-06 |