mlsanityData to 14 Oct 2026

ARTEMIS: Autoregressive End-to-End Trajectory Planning with Mixture of Experts for Autonomous Driving

arXiv 2504.19580 ↗First posted 28 Apr 2025cs.ROcited by 67

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

Abstract (arXiv)

This paper presents ARTEMIS, an end-to-end autonomous driving framework that combines autoregressive trajectory planning with Mixture-of-Experts (MoE). Traditional modular methods suffer from error propagation, while existing end-to-end models typically employ static one-shot inference paradigms that inadequately capture the dynamic changes of the environment. ARTEMIS takes a different method by generating trajectory waypoints sequentially, preserves critical temporal dependencies while dynamically routing scene-specific queries to specialized expert networks. It effectively relieves trajectory quality degradation issues encountered when guidance information is ambiguous, and overcomes the inherent representational limitations of singular network architectures when processing diverse driving scenarios. Additionally, we use a lightweight batch reallocation strategy that significantly improves the training speed of the Mixture-of-Experts model. Through experiments on the NAVSIM dataset, ARTEMIS exhibits superior competitive performance, achieving 87.0 PDMS and 83.1 EPDMS with ResNet-34 backbone, demonstrates state-of-the-art performance on multiple metrics.

Key facts. Cited by 67 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 26 of its 36 references that are in the map. First posted on arXiv 28 Apr 2025.

Builds on

26 of 36 references.

Closest ancestors · 10

Same direction, newest first

  1. 2025Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation17 Mar
  2. 2024Generalizing Motion Planners with Mixture of Experts for Autonomous Driving21 Oct
  3. 2024DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba7 Aug
  4. 2024NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking21 Jun
  5. 2024SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation30 May
  6. 2023Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving?5 Dec
  7. 2023Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes17 May
  8. 2022Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline16 Jun
  9. 2022TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving31 May
  10. 2021NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles22 Jun

Other references · 16

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,073
  3. 2022Planning-oriented Autonomous Drivingcited by 1,087
  4. 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,061
  5. 2024DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Modelscited by 867
  6. 2023VAD: Vectorized Scene Representation for Efficient Autonomous Drivingcited by 622
  7. 2024DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Drivingcited by 334
  8. 2021AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecastingcited by 331
  9. 2024VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planningcited by 289
  10. 2024Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillationcited by 249
  11. 2021Multimodal Motion Prediction with Stacked Transformerscited by 208
  12. 2025GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Drivingcited by 120
4 more
  1. 2024DrivingGPT: Unifying Driving World Modeling and Planning with Multi-modal Autoregressive Transformerscited by 96
  2. 2024PLUTO: Pushing the Limit of Imitation Learning-based Planning for Autonomous Drivingcited by 84
  3. 2024DrivingWorld: Constructing World Model for Autonomous Driving via Video GPTcited by 66
  4. 2024Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Networkcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 18; 2026: 49.

67 papers.

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  1. 2025ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Drivingcited by 198
  2. 2025DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Drivingcited by 134
  3. 2025DiffusionDriveV2: Reinforcement Learning-Constrained Truncated Diffusion Modeling in End-to-End Autonomous Drivingcited by 56
  4. 2025DriveDPO: Policy Learning via Safety DPO For End-to-End Autonomous Drivingcited by 39
  5. 2026SGDrive: Scene-to-Goal Hierarchical World Cognition for Autonomous Drivingcited by 38
  6. 2026DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Drivingcited by 37
  7. 2026SparseDriveV2: Scoring is All You Need for End-to-End Autonomous Drivingcited by 26
  8. 2026DriveFine: Refining-Augmented Masked Diffusion VLA for Precise and Robust Drivingcited by 19
  9. 2025DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Drivingcited by 17
  10. 2026DriveDreamer-Policy: A Geometry-Grounded World-Action Model for Unified Generation and Planningcited by 17
  11. 2026SpanVLA: Efficient Action Bridging and Learning from Negative-Recovery Samples for Vision-Language-Action Modelcited by 17
  12. 2026DriveFuture: Future-Aware Latent World Models for Autonomous Drivingcited by 17
38 more
  1. 2025GaussianFusion: Gaussian-Based Multi-Sensor Fusion for End-to-End Autonomous Drivingcited by 16
  2. 2025PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Drivingcited by 16
  3. 2025WAM-Flow: Parallel Coarse-to-Fine Motion Planning via Discrete Flow Matching for Autonomous Drivingcited by 16
  4. 2026Devil is in Narrow Policy: Unleashing Exploration in Driving VLA Modelscited by 15
  5. 2025ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Drivingcited by 14
  6. 2026DVGT-2: Vision-Geometry-Action Model for Autonomous Driving at Scalecited by 13
  7. 2025MindDrive: An All-in-One Framework Bridging World Models and Vision-Language Model for End-to-End Autonomous Drivingcited by 12
  8. 2026ExploreVLA: Dense World Modeling and Exploration for End-to-End Autonomous Drivingcited by 12
  9. 2026CLOVER: Closed-Loop Value Estimation and Ranking for End-to-End Autonomous Driving Planningcited by 11
  10. 2026Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigationcited by 11
  11. 2025WAM-Diff: A Masked Diffusion VLA Framework with MoE and Online Reinforcement Learning for Autonomous Drivingcited by 10
  12. 2026FLARE: Learning Future-Aware Latent Representations from Vision-Language Models for Autonomous Drivingcited by 10
  13. 2026SimWAM: A Simple World Action Model for End-to-End Autonomous Drivingcited by 9
  14. 2026TRACE: Trajectory-Aware Comprehensive Evaluation for Deep Research Agentscited by 8
  15. 2026Beyond Imitation: Learning Safe End-to-End Autonomous Driving from Hard Negativescited by 8
  16. 2026Vega: Learning to Drive with Natural Language Instructionscited by 6
  17. 2026WA-JEPA: Rethinking the Video JEPA Paradigm for World-Action Modeling in Autonomous Drivingcited by 6
  18. 2025A Knowledge-Driven Diffusion Policy for End-to-End Autonomous Driving Based on Expert Routingcited by 5
  19. 2026Senna-2: Aligning VLM and End-to-End Driving Policy for Consistent Decision Making and Planningcited by 5
  20. 2026DA-WAM: Decision-Aligned Future Latents for Driving World Modelscited by 5
  21. 2025FlowDrive: Energy Flow Field for End-to-End Autonomous Drivingcited by 4
  22. 2025DAP: A Discrete-token Autoregressive Planner for Autonomous Drivingcited by 4
  23. 2026Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Drivingcited by 4
  24. 2025ExpertAD: Enhancing Autonomous Driving Systems with Mixture of Expertscited by 3
  25. 2026Distill to Think, Foresee to Act: Cognitive-Physical Reinforcement Learning for Autonomous Drivingcited by 3
  26. 2026GeoWAM: Visual Geometry World Action Models for Autonomous Drivingcited by 3
  27. 2025CBDES MoE: Hierarchically Decoupled Mixture-of-Experts for Functional Modules in Autonomous Drivingcited by 2
  28. 2025Autoregressive End-to-End Planning with Time-Invariant Spatial Alignment and Multi-Objective Policy Refinementcited by 2
  29. 2026The Rise of Sparse Mixture-of-Experts: A Survey from Algorithmic Foundations to Decentralized Architectures and Vertical Domain Applicationscited by 2
  30. 2026SafeAlign-VLA: A Negative-Enhanced Safe Alignment Framework for Risk-Aware Autonomous Drivingcited by 2
  31. 2026NTR: Neural Token Reconstruction for Scene Token Bottleneck in End-to-End Drivingcited by 2
  32. 2026CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Drivingcited by 2
  33. 2025PIE: Perception and Interaction Enhanced End-to-End Motion Planning for Autonomous Drivingcited by 1
  34. 2026A Unified Candidate Set with Scene-Adaptive Refinement via Diffusion for End-to-End Autonomous Drivingcited by 1
  35. 2026From Representational Complementarity to Dual Systems: Synergizing VLM and Vision-Only Backbones for End-to-End Drivingcited by 1
  36. 2026Do You See What I Am Pointing At? Gesture-Based Egocentric Video Question Answeringcited by 1
  37. 2026Grounding Driving VLA via Inverse Kinematicscited by 1
  38. 2026FlowR2A: Learning Reward-to-Action Distribution for Multimodal Driving Planningcited by 1

50 most cited of 67 All 67 (JSON)

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  1. 2025TrajMoE: Scene-Adaptive Trajectory Planning with Mixture of Experts and Reinforcement Learning
  2. 2026ManeuverFlow: Maneuver Hypothesis Generation with Future Evidence for Trajectory Evaluation and Refinement in End-to-End Autonomous Driving
  3. 2026DriveVer: Lightweight Trajectory Evaluator as Test-Time Verifier for Autonomous Driving
  4. 2026HEAT: Heterogeneous End-to-End Autonomous Driving via Trajectory-Guided World Models
  5. 2026Beyond Waypoint Regression: Query-Based Cost Learning over Reachable Ego Futures for End-to-End Driving
  6. 2025Fully Unified Motion Planning for End-to-End Autonomous Driving
  7. 2025Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems
  8. 2025GEMINUS: Dual-aware Global and Scene-Adaptive Mixture-of-Experts for End-to-End Autonomous Driving