mlsanityData to 14 Oct 2026

Towards learning-based planning:The nuPlan benchmark for real-world autonomous driving

arXiv 2403.04133 ↗First posted 7 Mar 2024cs.CV cs.ROcited by 104

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

Abstract (arXiv)

Machine Learning (ML) has replaced traditional handcrafted methods for perception and prediction in autonomous vehicles. Yet for the equally important planning task, the adoption of ML-based techniques is slow. We present nuPlan, the world's first real-world autonomous driving dataset, and benchmark. The benchmark is designed to test the ability of ML-based planners to handle diverse driving situations and to make safe and efficient decisions. To that end, we introduce a new large-scale dataset that consists of 1282 hours of diverse driving scenarios from 4 cities (Las Vegas, Boston, Pittsburgh, and Singapore) and includes high-quality auto-labeled object tracks and traffic light data. We exhaustively mine and taxonomize common and rare driving scenarios which are used during evaluation to get fine-grained insights into the performance and characteristics of a planner. Beyond the dataset, we provide a simulation and evaluation framework that enables a planner's actions to be simulated in closed-loop to account for interactions with other traffic participants. We present a detailed analysis of numerous baselines and investigate gaps between ML-based and traditional methods. Find the nuPlan dataset and code at nuplan.org.

Key facts. Cited by 104 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 61 of its 92 references that are in the map. First posted on arXiv 7 Mar 2024.

Builds on

61 of 92 references.

Closest ancestors · 5

Same direction, newest first

  1. 2022ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning15 Jul
  2. 2022Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline16 Jun
  3. 2022Control-Aware Prediction Objectives for Autonomous Driving28 Apr
  4. 2021SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies28 Sep
  5. 2021End-to-end Interpretable Neural Motion Planner17 Jan

Other references · 56

  1. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  2. 2018YOLOv3: An Incremental Improvementcited by 2,659
  3. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  4. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  5. 2016Generative Adversarial Imitation Learningcited by 1,597
  6. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  7. 2020Center-based 3D Object Detection and Trackingcited by 1,144
  8. 2022Planning-oriented Autonomous Drivingcited by 1,087
  9. 2016End to End Learning for Self-Driving Carscited by 1,009
  10. 2020Designing Network Design Spacescited by 960
  11. 2019Argoverse: 3D Tracking and Forecasting with Rich Mapscited by 732
  12. 2023Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecastingcited by 707
38 more
  1. 2021Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Datasetcited by 537
  2. 2017AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehiclescited by 523
  3. 2019PointPainting: Sequential Fusion for 3D Object Detectioncited by 449
  4. 2018ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worstcited by 397
  5. 2019Learning by Cheatingcited by 383
  6. 2020Learning Lane Graph Representations for Motion Forecastingcited by 382
  7. 2019Exploring the Limitations of Behavior Cloning for Autonomous Drivingcited by 346
  8. 2016Safe, Multi-Agent, Reinforcement Learning for Autonomous Drivingcited by 311
  9. 2021DenseTNT: End-to-end Trajectory Prediction from Dense Goal Setscited by 295
  10. 2021MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learningcited by 290
  11. 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 277
  12. 2018Learning to Drive in a Daycited by 273
  13. 2019CoverNet: Multimodal Behavior Prediction using Trajectory Setscited by 259
  14. 2016A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehiclescited by 254
  15. 2023Parting with Misconceptions about Learning-based Vehicle Motion Planningcited by 242
  16. 2020One Thousand and One Hours: Self-driving Motion Prediction Datasetcited by 240
  17. 2019INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Mapscited by 231
  18. 2021MP3: A Unified Model to Map, Perceive, Predict and Plancited by 212
  19. 2015Maximum Entropy Deep Inverse Reinforcement Learningcited by 176
  20. 2021NEAT: Neural Attention Fields for End-to-End Autonomous Drivingcited by 174
  21. 2019End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Cloudscited by 171
  22. 2020Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representationscited by 153
  23. 2021Offboard 3D Object Detection from Point Cloud Sequencescited by 145
  24. 2023GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Drivingcited by 141
  25. 2019The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersectionscited by 137
  26. 2020Pillar-based Object Detection for Autonomous Drivingcited by 137
  27. 2022Learning from All Vehiclescited by 135
  28. 2021Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradientscited by 122
  29. 2021Learning to drive from a world on railscited by 114
  30. 2021Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Taskscited by 101
  31. 2018Baidu Apollo EM Motion Plannercited by 100
  32. 2018Driving Policy Transfer via Modularity and Abstractioncited by 92
  33. 2019Urban Driving with Conditional Imitation Learningcited by 86
  34. 2018Motion Planning Networkscited by 80
  35. 2022Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulationcited by 61
  36. 2023From Prediction to Planning With Goal Conditioned Lane Graph Traversalscited by 45
  37. 2021Auto4D: Learning to Label 4D Objects from Sequential Point Cloudscited by 42
  38. 2023CAPE: Camera View Position Embedding for Multi-View 3D Object Detectioncited by 40

50 most cited of 56 All 56 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 15; 2025: 44; 2026: 45.

104 papers.

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  1. 2024NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarkingcited by 354
  2. 2024DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Drivingcited by 334
  3. 2025AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuningcited by 260
  4. 2024Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Drivingcited by 252
  5. 2025Pseudo-Simulation for Autonomous Drivingcited by 150
  6. 2025Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillationcited by 96
  7. 2024HUGSIM: A Real-Time, Photo-Realistic and Closed-Loop Simulator for Autonomous Drivingcited by 90
  8. 2024NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Drivingcited by 62
  9. 2025A Survey of World Models for Autonomous Drivingcited by 60
  10. 2024Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuningcited by 42
  11. 2025DriveDPO: Policy Learning via Safety DPO For End-to-End Autonomous Drivingcited by 39
  12. 2025Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysiscited by 38
38 more
  1. 2026SGDrive: Scene-to-Goal Hierarchical World Cognition for Autonomous Drivingcited by 38
  2. 2025SimScale: Learning to Drive via Real-World Simulation at Scalecited by 36
  3. 2025CaRL: Learning Scalable Planning Policies with Simple Rewardscited by 35
  4. 2025Vision-Language-Action Models for Autonomous Driving: Past, Present, and Futurecited by 33
  5. 2024Hidden Biases of End-to-End Driving Datasetscited by 30
  6. 2025Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environmentscited by 28
  7. 2024SLEDGE: Synthesizing Driving Environments with Generative Models and Rule-Based Trafficcited by 27
  8. 2025LEAD: Minimizing Learner-Expert Asymmetry in End-to-End Drivingcited by 23
  9. 2026DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Drivingcited by 21
  10. 2025SceneDiffuser++: City-Scale Traffic Simulation via a Generative World Modelcited by 20
  11. 2025MTGS: Multi-Traversal Gaussian Splattingcited by 19
  12. 2026SpanVLA: Efficient Action Bridging and Learning from Negative-Recovery Samples for Vision-Language-Action Modelcited by 17
  13. 2024Bench2Drive-R: Turning Real World Data into Reactive Closed-Loop Autonomous Driving Benchmark by Generative Modelcited by 14
  14. 2025ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Drivingcited by 14
  15. 2026DriveWAM: Video Generative Priors Enable Scalable World-Action Modeling for Autonomous Drivingcited by 14
  16. 2025RefAV: Towards Planning-Centric Scenario Miningcited by 13
  17. 2025LANGTRAJ: Diffusion Model and Dataset for Language-Conditioned Trajectory Simulationcited by 11
  18. 2025GenieDrive: Towards Physics-Aware Driving World Model with 4D Occupancy Guided Video Generationcited by 11
  19. 2026nuReasoning: A Reasoning-Centric Dataset and Benchmark for Long-Tail Autonomous Drivingcited by 9
  20. 2026Qwen-Drive-1.0: An Initial Step towards a Vision-Language Foundation Model for Autonomous Drivingcited by 9
  21. 2024Memorize What Matters: Emergent Scene Decomposition from Multitraversecited by 8
  22. 2025LeAP: Consistent multi-domain 3D labeling using Foundation Modelscited by 8
  23. 2026SafeDrive: Fine-Grained Safety Reasoning for End-to-End Driving in a Sparse Worldcited by 8
  24. 2026World Engine: Towards the Era of Post-Training for Autonomous Drivingcited by 8
  25. 2025When Planners Meet Reality: How Learned, Reactive Traffic Agents Shift nuPlan Benchmarkscited by 7
  26. 2025Model-Based Policy Adaptation for Closed-Loop End-to-End Autonomous Drivingcited by 7
  27. 2026Factored Latent Action World Modelscited by 7
  28. 2026Do Open-Loop Metrics Predict Closed-Loop Driving? A Cross-Benchmark Correlation Study of NAVSIM and Bench2Drivecited by 6
  29. 2025B'ezierGS: Dynamic Urban Scene Reconstruction with B'ezier Curve Gaussian Splattingcited by 5
  30. 2026What Matters for Scalable and Robust Learning in End-to-End Driving Planners?cited by 5
  31. 2026Beyond Self-Play and Scale: A Behavior Benchmark for Generalization in Autonomous Drivingcited by 5
  32. 2024LoRD: Adapting Differentiable Driving Policies to Distribution Shiftscited by 4
  33. 2025CoPlanner: An Interactive Motion Planner with Contingency-Aware Diffusion for Autonomous Drivingcited by 4
  34. 2025FlowDrive: moderated flow matching with data balancing for trajectory planningcited by 4
  35. 2025Perfect Prediction or Plenty of Proposals? What Matters Most in Planning for Autonomous Drivingcited by 4
  36. 2025Forging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systemscited by 4
  37. 2024Adversarial and Reactive Traffic Entities for Behavior-Realistic Driving Simulation: A Reviewcited by 3
  38. 2025V-Max: A Reinforcement Learning Framework for Autonomous Drivingcited by 3

50 most cited of 104 All 104 (JSON)

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  1. 2021NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles
  2. 2025nuPlan-R: A Closed-Loop Planning Benchmark for Autonomous Driving via Reactive Multi-Agent Simulation
  3. 2026Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment
  4. 2025Benchmarking Autonomous Driving Planners Across Leaderboards: A Unified CARLA-Based Evaluation
  5. 2024Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions
  6. 2023Scaling Planning for Automated Driving using Simplistic Synthetic Data
  7. 2024Planning with Adaptive World Models for Autonomous Driving
  8. 2023Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions