mlsanityData to 7 Oct 2026

Rethinking Imitation-based Planner for Autonomous Driving

arXiv 2309.10443 ↗First posted 19 Sep 2023cs.RO cs.AIcited by 25

On the map: End-to-end autonomous driving → Imitation-learning end-to-end planners

Abstract (arXiv)

In recent years, imitation-based driving planners have reported considerable success. However, due to the absence of a standardized benchmark, the effectiveness of various designs remains unclear. The newly released nuPlan addresses this issue by offering a large-scale real-world dataset and a standardized closed-loop benchmark for equitable comparisons. Utilizing this platform, we conduct a comprehensive study on two fundamental yet underexplored aspects of imitation-based planners: the essential features for ego planning and the effective data augmentation techniques to reduce compounding errors. Furthermore, we highlight an imitation gap that has been overlooked by current learning systems. Finally, integrating our findings, we propose a strong baseline model-PlanTF. Our results demonstrate that a well-designed, purely imitation-based planner can achieve highly competitive performance compared to state-of-the-art methods involving hand-crafted rules and exhibit superior generalization capabilities in long-tail cases. Our models and benchmarks are publicly available. Project website https://jchengai.github.io/planTF.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving. Builds on 44 of its 55 references that are in the map. First posted on arXiv 19 Sep 2023.

Builds on

44 of 55 references.

Closest ancestors · 9

Same direction, newest first

  1. 2023CCIL: Context-conditioned imitation learning for urban driving4 May
  2. 2023GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving10 Mar
  3. 2022Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios21 Dec
  4. 2022Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving18 Oct
  5. 2022Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Driving21 Jul
  6. 2022Real-Time Trajectory Planning for Autonomous Driving with Gaussian Process and Incremental Refinement24 May
  7. 2022Learning from All Vehicles22 Mar
  8. 2021Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients27 Sep
  9. 2021Exploring Imitation Learning for Autonomous Driving with Feedback Synthesizer and Differentiable Rasterization2 Mar

Other references · 35

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  4. 2020Shortcut Learning in Deep Neural Networkscited by 1,928
  5. 2022Planning-oriented Autonomous Drivingcited by 1,068
  6. 2023VAD: Vectorized Scene Representation for Efficient Autonomous Drivingcited by 613
  7. 2021NuPlan: A closed-loop ML-based planning benchmark for autonomous vehiclescited by 485
  8. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  9. 2022TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Drivingcited by 477
  10. 2018ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worstcited by 391
  11. 2022ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learningcited by 365
  12. 2019Learning by Cheatingcited by 361
23 more
  1. 2019Exploring the Limitations of Behavior Cloning for Autonomous Drivingcited by 343
  2. 2021End-to-end Interpretable Neural Motion Plannercited by 240
  3. 2023Parting with Misconceptions about Learning-based Vehicle Motion Planningcited by 238
  4. 2022Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformercited by 211
  5. 2021NEAT: Neural Attention Fields for End-to-End Autonomous Drivingcited by 174
  6. 2023MARS: An Instance-aware, Modular and Realistic Simulator for Autonomous Drivingcited by 170
  7. 2023Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Drivingcited by 168
  8. 2022PlanT: Explainable Planning Transformers via Object-Level Representationscited by 105
  9. 2021VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehiclescited by 81
  10. 2023Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoderscited by 72
  11. 2020Fighting Copycat Agents in Behavioral Cloning from Observation Historiescited by 70
  12. 2021SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policiescited by 68
  13. 2022Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulationcited by 61
  14. 2023ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modelingcited by 60
  15. 2021Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Drivingcited by 46
  16. 2023From Prediction to Planning With Goal Conditioned Lane Graph Traversalscited by 45
  17. 2019Monocular Plan View Networks for Autonomous Drivingcited by 37
  18. 2023The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Reviewcited by 29
  19. 2022Safe Real-World Autonomous Driving by Learning to Predict and Plan with a Mixture of Expertscited by 28
  20. 2021Improved Deep Reinforcement Learning with Expert Demonstrations for Urban Autonomous Drivingcited by 26
  21. 2023Imitation with Spatial-Temporal Heatmap: 2nd Place Solution for NuPlan Challengecited by 19
  22. 2022MMFN: Multi-Modal-Fusion-Net for End-to-End Drivingcited by 17
  23. 2023Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban Environmentscited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 12; 2025: 4; 2026: 9.

25 papers.

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By area:

  1. 2024DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mambacited by 87
  2. 2024PLUTO: Pushing the Limit of Imitation Learning-based Planning for Autonomous Drivingcited by 84
  3. 2024Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?cited by 38
  4. 2024PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planningcited by 38
  5. 2024GaussianAD: Gaussian-Centric End-to-End Autonomous Drivingcited by 31
  6. 2024SMART: Scalable Multi-agent Real-time Motion Generation via Next-token Predictioncited by 25
  7. 2024Bench2Drive-R: Turning Real World Data into Reactive Closed-Loop Autonomous Driving Benchmark by Generative Modelcited by 14
  8. 2024Instruct Large Language Models to Drive like Humanscited by 11
  9. 2024Exploring the Causality of End-to-End Autonomous Drivingcited by 7
  10. 2025LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarioscited by 7
  11. 2024LoRD: Adapting Differentiable Driving Policies to Distribution Shiftscited by 4
  12. 2025FlowDrive: moderated flow matching with data balancing for trajectory planningcited by 3
13 more
  1. 2025HYPE: Hybrid Planning with Ego Proposal-Conditioned Predictionscited by 3
  2. 2026Safe and Stylized Trajectory Planning for Autonomous Driving via Diffusion Modelcited by 2
  3. 2024Utilizing Navigation Paths to Generate Target Points for Enhanced End-to-End Autonomous Driving Planningcited by 1
  4. 2024LHPF: Look back the History and Plan for the Future in Autonomous Drivingcited by 1
  5. 2026Physics-informed Diffusion Mamba Transformer for Real-world Drivingcited by 1
  6. 2026Learning from Mistakes: Rollout-Retrieval Lifelong Policy Learning for Autonomous Drivingcited by 1
  7. 2025Align2Act: Instruction-Tuned Models for Human-Aligned Autonomous Drivingcited by 0
  8. 2026A Neuro-Symbolic Framework Combining Inductive and Deductive Reasoning for Autonomous Driving Planningcited by 0
  9. 2026Dash2Sim: Closed-Loop Driving Simulation from in-the-wild Dashcam Videoscited by 0
  10. 2026Shift & Drift: A Zero-Shot Benchmark for Generalizable and Robust Autonomous Driving Motion Planningcited by 0
  11. 2026Real-Time Rulebook-Aware Nonlinear MPC for Autonomous Driving with Priority-Biased Tiered Slackscited by 0
  12. 2026nuTruck: Benchmarking Autonomous Driving Planning for Distributed Electric-drive Truckscited by 0
  13. 2026DriveZero: End-to-End Driving Beyond Human Demonstrationscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Exposing the Copycat Problem of Imitation-based Planner: A Novel Closed-Loop Simulator, Causal Benchmark and Joint IL-RL Baseline
  2. 2024Hybrid Imitation-Learning Motion Planner for Urban Driving
  3. 2025Sequence of Expert: Boosting Imitation Planners for Autonomous Driving through Temporal Alternation
  4. 2024PP-TIL: Personalized Planning for Autonomous Driving with Instance-based Transfer Imitation Learning
  5. 2024EasyChauffeur: A Baseline Advancing Simplicity and Efficiency on Waymax
  6. 2025Predictive Planner for Autonomous Driving with Consistency Models
  7. 2025Diffusion-Based Planning for Autonomous Driving with Flexible Guidance
  8. 2025Benchmarking Autonomous Driving Planners Across Leaderboards: A Unified CARLA-Based Evaluation