# Imitation-learning end-to-end planners

Area: [End-to-end autonomous driving](https://mlsanity.com/driving/index.md). HTML: https://mlsanity.com/driving/imitation-learning-end-to-end-planners/

## Key facts

- Imitation-learning end-to-end planners: 287 papers on arXiv since 2021 (data through 14 Oct 2026).
- 47 of its 287 mapped papers (16%) cite “DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving” (2024, arXiv 2411.15139).
- 45 of its 287 mapped papers (16%) cite “Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients” (2021, arXiv 2109.13333).
- 22 new papers 17 Sep–14 Oct.

## Most cited here

- [DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving](https://mlsanity.com/paper/2411.15139/) (2024), cited by 47 papers here
- [Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients](https://mlsanity.com/paper/2109.13333/) (2021), cited by 45 papers here
- [VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning](https://mlsanity.com/paper/2402.13243/) (2024), cited by 39 papers here
- [Diffusion-Based Planning for Autonomous Driving with Flexible Guidance](https://mlsanity.com/paper/2501.15564/) (2025), cited by 35 papers here
- [Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios](https://mlsanity.com/paper/2212.11419/) (2022), cited by 32 papers here
- [GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving](https://mlsanity.com/paper/2303.05760/) (2023), cited by 32 papers here
- [PLUTO: Pushing the Limit of Imitation Learning-based Planning for Autonomous Driving](https://mlsanity.com/paper/2404.14327/) (2024), cited by 29 papers here
- [End-to-End Urban Driving by Imitating a Reinforcement Learning Coach](https://mlsanity.com/paper/2108.08265/) (2021), cited by 27 papers here
- [Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research](https://mlsanity.com/paper/2310.08710/) (2023), cited by 27 papers here
- [GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving](https://mlsanity.com/paper/2503.05689/) (2025), cited by 27 papers here

## New (17 Sep–14 Oct)

- [Uncertainty-Driven Self-Play for Robust Multi-Agent Driving Policies](https://mlsanity.com/paper/2610.19714/) (2026-10-14)
- [ManeuverFlow: Maneuver Hypothesis Generation with Future Evidence for Trajectory Evaluation and Refinement in End-to-End Autonomous Driving](https://mlsanity.com/paper/2610.13409/) (2026-10-09)
- [BridgeGuard: Explicit Safety Drift for Diffusion-based Autonomous Driving](https://mlsanity.com/paper/2610.11483/) (2026-10-08)
- [Beyond Policy Support: Interaction Constrained Offline Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2610.09763/) (2026-10-07)
- [Beyond Waypoint Regression: Query-Based Cost Learning over Reachable Ego Futures for End-to-End Driving](https://mlsanity.com/paper/2610.08123/) (2026-10-06)
- [Closed-Loop Refinement and Execution for Learned Driving Planners](https://mlsanity.com/paper/2610.00992/) (2026-10-01)
- [Learning to Explain While Planning: Rule-Aligned Diffusion Planning for Autonomous Driving](https://mlsanity.com/paper/2609.39995/) (2026-10-01)
- [ReWAM: Reciprocal World Action Models for Interactive Autonomous Driving](https://mlsanity.com/paper/2609.39245/) (2026-09-30)
- [Efficient Multi-Modal Planning with Reward-Guided Preference Optimization for Autonomous Driving](https://mlsanity.com/paper/2609.38862/) (2026-09-30)
- [RoXDrive: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving via Action-Faithful Rollouts](https://mlsanity.com/paper/2609.36851/) (2026-09-30)
- [Diffusion-2BC: Hybrid Diffusion and Regression Training for Offline Behavior Cloning in Autonomous Driving](https://mlsanity.com/paper/2609.38472/) (2026-09-29)
- [Guiding End-to-End Driving Models with Endpoint-Constrained Trajectory Optimization](https://mlsanity.com/paper/2609.31383/) (2026-09-25)
- [INTERACT: Interactive Planning for Autonomous Driving via Anchor-Conditioned Prediction and Trust-Region Refinement](https://mlsanity.com/paper/2609.31137/) (2026-09-25)
- [Evaluation Is All You Need for Multi-Modal Autonomous Driving](https://mlsanity.com/paper/2609.30818/) (2026-09-25)
- [Sim-to-Real Aware End-to-End Learning Environment for Micromobility](https://mlsanity.com/paper/2609.28969/) (2026-09-24)
- [Taming an End-to-End Autonomous Driving Policy for Urban Navigation of Quadruped Robots](https://mlsanity.com/paper/2610.08812/) (2026-09-23)
- [BranchDrive: A Branch-Structured Dataset for Action-Conditioned Driving Prediction](https://mlsanity.com/paper/2609.27275/) (2026-09-23)
- [Stability-Aware Imitation Learning from Model Predictive Control for Autonomous Vehicle Lateral Control: Exact Q-Loss and a Novel Training Procedure](https://mlsanity.com/paper/2609.23506/) (2026-09-20)
- [CounterPlay: Counterfactual Post-Training for Self-Play Driving Policies](https://mlsanity.com/paper/2609.21617/) (2026-09-18)
- [Stabilizing Trajectory Outputs in End-to-End Autonomous Driving via SC-IMM Based Teacher Signals](https://mlsanity.com/paper/2609.21404/) (2026-09-18)
- [OPTED: On-Policy Fine-Tuning for End-to-End Driving using a Render-Free Teacher](https://mlsanity.com/paper/2609.20756/) (2026-09-17)
- [MILER: Semantic Mid-Level Representation for Sim-to-Real Reinforcement Learning in Unstructured Autonomous Driving](https://mlsanity.com/paper/2609.20747/) (2026-09-17)