mlsanityData to 14 Oct 2026

DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning

arXiv 2506.06659 ↗First posted 7 Jun 2025cs.RO cs.AI cs.CVcited by 94

Not on the map. Why?

Cited by 82 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Autonomous vehicles must navigate safely in complex driving environments. Imitating a single expert trajectory, as in regression-based approaches, usually does not explicitly assess the safety of the predicted trajectory. Selection-based methods address this by generating and scoring multiple trajectory candidates and predicting the safety score for each. However, they face optimization challenges in precisely selecting the best option from thousands of candidates and distinguishing subtle but safety-critical differences, especially in rare and challenging scenarios. We propose DriveSuprim to overcome these challenges and advance the selection-based paradigm through a coarse-to-fine paradigm for progressive candidate filtering, a rotation-based augmentation method to improve robustness in out-of-distribution scenarios, and a self-distillation framework to stabilize training. DriveSuprim achieves state-of-the-art performance, reaching 93.5% PDMS in NAVSIM v1 and 87.1% EPDMS in NAVSIM v2 without extra data, with 83.02 Driving Score and 60.00 Success Rate on the Bench2Drive benchmark, demonstrating superior planning capabilities in various driving scenarios.

Key facts. Cited by 94 papers in the map (data through 14 Oct 2026). Builds on 62 of its 73 references that are in the map. First posted on arXiv 7 Jun 2025.

Builds on

62 of 73 references.

References · 62

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38 more
  1. 2020RobustBench: a standardized adversarial robustness benchmarkcited by 661
  2. 2023GAIA-1: A Generative World Model for Autonomous Drivingcited by 637
  3. 2023VAD: Vectorized Scene Representation for Efficient Autonomous Drivingcited by 622
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  7. 2021Multi-Modal Fusion Transformer for End-to-End Autonomous Drivingcited by 377
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  14. 2023DriveDreamer: Towards Real-world-driven World Models for Autonomous Drivingcited by 261
  15. 2024Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillationcited by 249
  16. 2022Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baselinecited by 248
  17. 2023Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving?cited by 242
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50 most cited of 62 All 62 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 18; 2026: 76.

94 papers.

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  1. 2025DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Drivingcited by 134
  2. 2025A Survey on Vision-Language-Action Models for Autonomous Drivingcited by 72
  3. 2025DiffusionDriveV2: Reinforcement Learning-Constrained Truncated Diffusion Modeling in End-to-End Autonomous Drivingcited by 56
  4. 2026Driving on Registerscited by 44
  5. 2026DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Drivingcited by 37
  6. 2026Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Drivingcited by 33
  7. 2026Drive-JEPA: Video JEPA Meets Multimodal Trajectory Distillation for End-to-End Drivingcited by 30
  8. 2026SparseDriveV2: Scoring is All You Need for End-to-End Autonomous Drivingcited by 26
  9. 2025GuideFlow: Constraint-Guided Flow Matching for Planning in End-to-End Autonomous Drivingcited by 22
  10. 2025Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Drivingcited by 19
  11. 2026DriveFine: Refining-Augmented Masked Diffusion VLA for Precise and Robust Drivingcited by 19
  12. 2026Unleashing VLA Potentials in Autonomous Driving via Explicit Learning from Failurescited by 19
38 more
  1. 2025DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Drivingcited by 17
  2. 2026DriveDreamer-Policy: A Geometry-Grounded World-Action Model for Unified Generation and Planningcited by 17
  3. 2026SpanVLA: Efficient Action Bridging and Learning from Negative-Recovery Samples for Vision-Language-Action Modelcited by 17
  4. 2026DriveFuture: Future-Aware Latent World Models for Autonomous Drivingcited by 17
  5. 2025PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Drivingcited by 16
  6. 2025Zero-Human Demonstration End-to-end Autonomous Driving with Trajectory Scorercited by 16
  7. 2026Devil is in Narrow Policy: Unleashing Exploration in Driving VLA Modelscited by 15
  8. 2025ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Drivingcited by 14
  9. 2026MeanFuser: Fast One-Step Multi-Modal Trajectory Generation and Adaptive Reconstruction via MeanFlow for End-to-End Autonomous Drivingcited by 13
  10. 2026DVGT-2: Vision-Geometry-Action Model for Autonomous Driving at Scalecited by 13
  11. 2025EvaDrive: Evolutionary Adversarial Policy Optimization for End-to-End Autonomous Drivingcited by 12
  12. 2026ExploreVLA: Dense World Modeling and Exploration for End-to-End Autonomous Drivingcited by 12
  13. 2026CLOVER: Closed-Loop Value Estimation and Ranking for End-to-End Autonomous Driving Planningcited by 11
  14. 2026Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigationcited by 11
  15. 2025DiffRefiner: Coarse to Fine Trajectory Planning via Diffusion Refinement with Semantic Interaction for End to End Autonomous Drivingcited by 10
  16. 2025WAM-Diff: A Masked Diffusion VLA Framework with MoE and Online Reinforcement Learning for Autonomous Drivingcited by 10
  17. 2026BridgeSim: Unveiling the OL-CL Gap in End-to-End Autonomous Drivingcited by 9
  18. 2026SimWAM: A Simple World Action Model for End-to-End Autonomous Drivingcited by 9
  19. 2026SafeDrive: Fine-Grained Safety Reasoning for End-to-End Driving in a Sparse Worldcited by 8
  20. 2026RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Frameworkcited by 8
  21. 2026Beyond Imitation: Learning Safe End-to-End Autonomous Driving from Hard Negativescited by 8
  22. 2026Test-Time Trajectory Optimization for Autonomous Drivingcited by 7
  23. 2026Vega: Learning to Drive with Natural Language Instructionscited by 6
  24. 2026Do Open-Loop Metrics Predict Closed-Loop Driving? A Cross-Benchmark Correlation Study of NAVSIM and Bench2Drivecited by 6
  25. 2026GraphWorld: Long-Horizon Planning with World Models for End-to-End Autonomous Drivingcited by 6
  26. 2026WA-JEPA: Rethinking the Video JEPA Paradigm for World-Action Modeling in Autonomous Drivingcited by 6
  27. 2026HiST-VLA: A Hierarchical Spatio-Temporal Vision-Language-Action Model for End-to-End Autonomous Drivingcited by 5
  28. 2026Senna-2: Aligning VLM and End-to-End Driving Policy for Consistent Decision Making and Planningcited by 5
  29. 2026Auto-JEPA: A Latent World Model of Continuous Intent for End-to-End Autonomous Drivingcited by 5
  30. 2026DA-WAM: Decision-Aligned Future Latents for Driving World Modelscited by 5
  31. 2025FlowDrive: Energy Flow Field for End-to-End Autonomous Drivingcited by 4
  32. 2026HAD: Combining Hierarchical Diffusion with Metric-Decoupled RL for End-to-End Drivingcited by 4
  33. 2026ChainFlow-VLA: Causal Flow Planning with Vision-Language Modelscited by 4
  34. 2025Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Surveycited by 3
  35. 2026Bench2Drive-Robust: Benchmarking Closed-Loop Autonomous Driving under Deployment Perturbationscited by 3
  36. 2026Distill to Think, Foresee to Act: Cognitive-Physical Reinforcement Learning for Autonomous Drivingcited by 3
  37. 2026DriveJudge: Rethinking Autonomous Driving Evaluation with Vision-Language Modelscited by 3
  38. 2026GeoWAM: Visual Geometry World Action Models for Autonomous Drivingcited by 3

50 most cited of 94 All 94 (JSON)

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  1. 2026DriveVer: Lightweight Trajectory Evaluator as Test-Time Verifier for Autonomous Driving
  2. 2025Drive in Corridors: Enhancing the Safety of End-to-end Autonomous Driving via Corridor Learning and Planning
  3. 2024Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation
  4. 2026FPD-Drive: Future Policy Distillation for End-to-End Autonomous Driving
  5. 2025DriveDPO: Policy Learning via Safety DPO For End-to-End Autonomous Driving
  6. 2025Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling
  7. 2025HMAD: Advancing E2E Driving with Anchored Offset Proposals and Simulation-Supervised Multi-target Scoring
  8. 2026From Proxy Learning to Driving Decisions: A Transfer-Based Framework for Evaluating Future-Aware Autonomous Driving Planners