mlsanityData to 7 Oct 2026

GenAD: Generative End-to-End Autonomous Driving

arXiv 2402.11502 ↗First posted 18 Feb 2024cs.CVcited by 225

On the map: World models → World models for autonomous driving

also in 1 more
Abstract (arXiv)

Directly producing planning results from raw sensors has been a long-desired solution for autonomous driving and has attracted increasing attention recently. Most existing end-to-end autonomous driving methods factorize this problem into perception, motion prediction, and planning. However, we argue that the conventional progressive pipeline still cannot comprehensively model the entire traffic evolution process, e.g., the future interaction between the ego car and other traffic participants and the structural trajectory prior. In this paper, we explore a new paradigm for end-to-end autonomous driving, where the key is to predict how the ego car and the surroundings evolve given past scenes. We propose GenAD, a generative framework that casts autonomous driving into a generative modeling problem. We propose an instance-centric scene tokenizer that first transforms the surrounding scenes into map-aware instance tokens. We then employ a variational autoencoder to learn the future trajectory distribution in a structural latent space for trajectory prior modeling. We further adopt a temporal model to capture the agent and ego movements in the latent space to generate more effective future trajectories. GenAD finally simultaneously performs motion prediction and planning by sampling distributions in the learned structural latent space conditioned on the instance tokens and using the learned temporal model to generate futures. Extensive experiments on the widely used nuScenes benchmark show that the proposed GenAD achieves state-of-the-art performance on vision-centric end-to-end autonomous driving with high efficiency. Code: https://github.com/wzzheng/GenAD.

Key facts. Cited by 225 papers in the map (data through 7 Oct 2026). Placed in World models for autonomous driving, one of 11 directions in World models. Builds on 50 of its 56 references that are in the map. First posted on arXiv 18 Feb 2024.

Builds on

50 of 56 references.

Closest ancestors · 6

Same direction, newest first

  1. 2023OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving27 Nov
  2. 2023Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving27 Apr
  3. 2022Differentiable Raycasting for Self-supervised Occupancy Forecasting4 Oct
  4. 2022BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers31 Mar
  5. 2021Scene Transformer: A unified architecture for predicting multiple agent trajectories15 Jun
  6. 2019CoverNet: Multimodal Behavior Prediction using Trajectory Sets23 Nov

Other references · 44

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  4. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  5. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  6. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  7. 2013Auto-Encoding Variational Bayescited by 3,980
  8. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
  9. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  10. 2022Planning-oriented Autonomous Drivingcited by 1,068
  11. 2020Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3Dcited by 1,040
  12. 2022BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representationcited by 844
32 more
  1. 20173D Semantic Segmentation with Submanifold Sparse Convolutional Networkscited by 746
  2. 2021BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-Viewcited by 711
  3. 2023VAD: Vectorized Scene Representation for Efficient Autonomous Drivingcited by 613
  4. 2022BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detectioncited by 568
  5. 2022PETR: Position Embedding Transformation for Multi-View 3D Object Detectioncited by 550
  6. 2019MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Predictioncited by 417
  7. 2023Tri-Perspective View for Vision-Based 3D Semantic Occupancy Predictioncited by 404
  8. 2020Learning Lane Graph Representations for Motion Forecastingcited by 380
  9. 2022ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learningcited by 365
  10. 2022BEVFusion: A Simple and Robust LiDAR-Camera Fusion Frameworkcited by 334
  11. 2021Categorical Depth Distribution Network for Monocular 3D Object Detectioncited by 326
  12. 2022MapTR: Structured Modeling and Learning for Online Vectorized HD Map Constructioncited by 311
  13. 2023SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Drivingcited by 310
  14. 2021HDMapNet: An Online HD Map Construction and Evaluation Frameworkcited by 280
  15. 2021Voxel Transformer for 3D Object Detectioncited by 247
  16. 2022VectorMapNet: End-to-end Vectorized HD Map Learningcited by 243
  17. 2021End-to-end Interpretable Neural Motion Plannercited by 240
  18. 2021FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Camerascited by 239
  19. 2023Parting with Misconceptions about Learning-based Vehicle Motion Planningcited by 238
  20. 2023VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Trackingcited by 221
  21. 2023OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perceptioncited by 218
  22. 2023Scene as Occupancycited by 208
  23. 2021Multimodal Motion Prediction with Stacked Transformerscited by 207
  24. 2022BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Drivingcited by 191
  25. 2022PolarFormer: Multi-camera 3D Object Detection with Polar Transformercited by 147
  26. 2023SelfOcc: Self-Supervised Vision-Based 3D Occupancy Predictioncited by 133
  27. 2021Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradientscited by 122
  28. 2022ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queriescited by 116
  29. 2023PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Predictioncited by 51
  30. 2023PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Drivingcited by 33
  31. 2022Safe Real-World Autonomous Driving by Learning to Predict and Plan with a Mixture of Expertscited by 28
  32. 2022Perceive, Interact, Predict: Learning Dynamic and Static Clues for End-to-End Motion Predictioncited by 28

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 31; 2025: 96; 2026: 98.

225 papers.

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  1. 2024Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllabilitycited by 337
  2. 2024DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Drivingcited by 324
  3. 2025AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuningcited by 252
  4. 2024EMMA: End-to-End Multimodal Model for Autonomous Drivingcited by 226
  5. 2024How Far is Video Generation from World Model: A Physical Law Perspectivecited by 204
  6. 2025ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generationcited by 177
  7. 2024GaussianFormer: Scene as Gaussians for Vision-Based 3D Semantic Occupancy Predictioncited by 155
  8. 2025GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Drivingcited by 117
  9. 2025Epona: Autoregressive Diffusion World Model for Autonomous Drivingcited by 115
  10. 2025World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Modelcited by 91
  11. 2024VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervisioncited by 85
  12. 2025DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Drivingcited by 85
38 more
  1. 2025Don't Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Drivingcited by 84
  2. 2024OccSora: 4D Occupancy Generation Models as World Simulators for Autonomous Drivingcited by 76
  3. 2025RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement Learningcited by 76
  4. 2025A Survey on Vision-Language-Action Models for Autonomous Drivingcited by 72
  5. 2024DrivingWorld: Constructing World Model for Autonomous Driving via Video GPTcited by 66
  6. 2025A Comprehensive Survey on World Models for Embodied AIcited by 65
  7. 20253D and 4D World Modeling: A Surveycited by 64
  8. 2025A Survey of World Models for Autonomous Drivingcited by 60
  9. 2025Impromptu VLA: Open Weights and Open Data for Driving Vision-Language-Action Modelscited by 60
  10. 2024V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Modelscited by 47
  11. 2024Navigation-Guided Sparse Scene Representation for End-to-End Autonomous Drivingcited by 46
  12. 2025Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)cited by 45
  13. 2024DINO-Foresight: Looking into the Future with DINOcited by 44
  14. 2025iPad: Iterative Proposal-centric End-to-End Autonomous Drivingcited by 41
  15. 2024PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planningcited by 38
  16. 2025RoboScape: Physics-informed Embodied World Modelcited by 38
  17. 2025Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysiscited by 37
  18. 2026DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generationcited by 36
  19. 2025HiP-AD: Hierarchical and Multi-Granularity Planning with Deformable Attention for Autonomous Driving in a Single Decodercited by 35
  20. 2024DrivingSphere: Building a High-fidelity 4D World for Closed-loop Simulationcited by 34
  21. 2025OmniNWM: Omniscient Driving Navigation World Modelscited by 32
  22. 2025Vision-Language-Action Models for Autonomous Driving: Past, Present, and Futurecited by 32
  23. 2026DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Drivingcited by 31
  24. 2025SpaceDrive: Infusing Spatial Awareness into VLM-based Autonomous Drivingcited by 28
  25. 2025MindDrive: A Vision-Language-Action Model for Autonomous Driving via Online Reinforcement Learningcited by 25
  26. 20253D Scene Generation: A Surveycited by 24
  27. 2025A Survey of Interactive Generative Videocited by 23
  28. 2024DriveScape: Towards High-Resolution Controllable Multi-View Driving Video Generationcited by 22
  29. 2024HoloDrive: Holistic 2D-3D Multi-Modal Street Scene Generation for Autonomous Drivingcited by 21
  30. 2025GuideFlow: Constraint-Guided Flow Matching for Planning in End-to-End Autonomous Drivingcited by 21
  31. 2026SparseDriveV2: Scoring is All You Need for End-to-End Autonomous Drivingcited by 21
  32. 2025VLM-E2E: Enhancing End-to-End Autonomous Driving with Multimodal Driver Attention Fusioncited by 20
  33. 2025StyleDrive: Towards Driving-Style Aware Benchmarking of End-To-End Autonomous Drivingcited by 20
  34. 2026UniDrive-WM: Unified Understanding, Planning and Generation World Model for Autonomous Drivingcited by 20
  35. 2025Plan-R1: Safe and Feasible Trajectory Planning as Language Modelingcited by 19
  36. 2026DriveVA: Video Action Models are Zero-Shot Driverscited by 19
  37. 2024FASIONAD : FAst and Slow FusION Thinking Systems for Human-Like Autonomous Driving with Adaptive Feedbackcited by 18
  38. 2024GAGS: Granularity-Aware Feature Distillation for Language Gaussian Splattingcited by 18

50 most cited of 225 All 225 (JSON)

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  1. 2024GenAD: Generalized Predictive Model for Autonomous Driving
  2. 2023GAIA-1: A Generative World Model for Autonomous Driving
  3. 2025FutureX: Enhance End-to-End Autonomous Driving via Latent Chain-of-Thought World Model
  4. 2026DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving
  5. 2025SynAD: Enhancing Real-World End-to-End Autonomous Driving Models through Synthetic Data Integration
  6. 2026Bridging Scene Generation and Planning: Driving with World Model via Unifying Vision and Motion Representation
  7. 2025GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving
  8. 2025Future-Aware End-to-End Driving: Bidirectional Modeling of Trajectory Planning and Scene Evolution