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:sunglasses: Awesome VLA for Autonomous Driving

Autonomous driving has long relied on modular "Perception-Decision-Action" pipelines, whose hand-crafted interfaces and rule-based components often struggle in complex, dynamic, or long-tailed scenarios. Their cascaded structure also amplifies upstream perception errors, undermining downstream planning and control.

This survey reviews vision-action (VA) models and vision-language-action (VLA) models for autonomous driving. We trace the evolution from early VA approaches to modern VLA frameworks, and organize existing methods into two principal paradigms:

• End-to-End VLA, which integrates perception, reasoning, and planning within a single model.
• Dual-System VLA, which separates slow deliberation (via VLMs) from fast, safety-critical execution (via planners).

For more details, kindly refer to our :books: Paper, :globe_with_meridians: Project Page, and :hugs: HuggingFace Leaderboard.

:books: Citation

If you find this work helpful for your research, please kindly consider citing our paper:

@article{survey_vla4ad,
    title   = {Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future},
    author  = {Tianshuai Hu and Xiaolu Liu and Song Wang and Yiyao Zhu and Ao Liang and Lingdong Kong and Guoyang Zhao and Zeying Gong and Jun Cen and Zhiyu Huang and Xiaoshuai Hao and Linfeng Li and Hang Song and Xiangtai Li and Jun Ma and Shaojie Shen and Jianke Zhu and Dacheng Tao and Ziwei Liu and Junwei Liang},
    journal = {arXiv preprint arXiv:2512.16760},
    year    = {2025},
}

@article{survey_3d_4d_world_models,
    title   = {{3D} and {4D} World Modeling: A Survey},
    author  = {Lingdong Kong and Wesley Yang and Jianbiao Mei and Youquan Liu and Ao Liang and Dekai Zhu and Dongyue Lu and Wei Yin and Xiaotao Hu and Mingkai Jia and Junyuan Deng and Kaiwen Zhang and Yang Wu and Tianyi Yan and Shenyuan Gao and Song Wang and Linfeng Li and Liang Pan and Yong Liu and Jianke Zhu and Wei Tsang Ooi and Steven C. H. Hoi and Ziwei Liu},
    journal = {arXiv preprint arXiv:2509.07996},
    year    = {2025}
}

Table of Contents

• 1. Vision-Action Models
  • Action-Only Models
  • Perception-Action Models
  • Image-Based World Models
  • Occupancy-Based World Models
  • Latent-Based World Models
• 2. Vision-Language-Action Models
  • Textual Action Generator
  • Numerical Action Generator
  • Explicit Action Guidance
  • Implicit Representations Transfer
• 3. Datasets & Benchmarks
  • Vision-Action Datasets
  • Vision-Language-Action Datasets
• 4. Applications
• 5. Other Resources

1. Vision-Action Models

:one: Action-Only Models

:timer_clock: In chronological order, from the earliest to the latest.

Model	Paper	Venue	Website	GitHub
Selective Memory	Not All History Helps: Velocity-Aware Selective Memory for Long-Horizon End-to-End Autonomous Driving	arXiv 2026	-	-
MomADv2	MomADv2: Reliable Temporal Memory for End-to-End Autonomous Driving	arXiv 2026	-	-
RoG-DAgger	RoG-DAgger: Rollout-Guided Post-Training for End-to-End Driving	arXiv 2026	-	-
CL4AD	Scaling Curriculum Learning For Autonomous Driving	arXiv 2026	-	-
VG-SAF	Variance-Guided Spatial Attention Fusion for Robust End-to-End Driving under Asymmetric Sensor Degradation	arXiv 2026	-	-
LBC	Learning by Cheating	CoRL 2020	-
Latent-DRL	End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances	CVPR 2020	-	-
NEAT	NEAT: Neural Attention Fields for End-to-End Autonomous Driving	ICCV 2021	-
Roach	End-to-End Urban Driving by Imitating a Reinforcement Learning Coach	ICCV 2021
WoR	Learning to Drive from A World on Rails	ICCV 2021
TCP	Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline	NeurIPS 2022	-
Urban-Driver	Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients	CoRL 2022
LAV	Learning from All Vehicles	CVPR 2022
TransFuser	TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving	TPAMI 2023	-
GRI	GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving	Robotics 2023	-	-
BEVPlanner	Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving?	CVPR 2024	-
Raw2Drive	Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)	NeurIPS 2025	-	-
RAD	RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement Learning	NeurIPS 2025		-
TrajDiff	TrajDiff: End-to-End Autonomous Driving without Perception Annotation	arXiv 2025	-
SimScale	SimScale: Learning to Drive via Real-World Simulation at Scale	arXiv 2025
-	Addressing the Waypoint-Action Gap in End-to-End Autonomous Driving via Vehicle Motion Models	arXiv 2026	-	-
Velocity-Aware Selective Memory	Not All History Helps: Velocity-Aware Selective Memory for Long-Horizon End-to-End Autonomous Driving	arXiv 2026	-	-
Scaling Curriculum Learning	Scaling Curriculum Learning For Autonomous Driving	arXiv 2026	-	-
MomADv2	MomADv2: Reliable Temporal Memory for End-to-End Autonomous Driving	arXiv 2026	-	-
RoG-DAgger	RoG-DAgger: Rollout-Guided Post-Training for End-to-End Driving	arXiv 2026	-	-
DriveZero	DriveZero: End-to-End Driving Beyond Human Demonstrations	arXiv 2026	-	-
EMPlan	Efficient Multi-Modal Planning with Reward-Guided Preference Optimization for Autonomous Driving	arXiv 2026	-	-
RADP	Learning to Explain While Planning: Rule-Aligned Diffusion Planning for Autonomous Driving	arXiv 2026	-	-
TerraTransfer++	TerraTransfer++: Demonstration-Free End-to-End Planner	arXiv 2026	-	-

:two: Perception-Action Models

:timer_clock: In chronological order, from the earliest to the latest.

Model	Paper	Venue	Website	GitHub
ST-P3	ST-P3: End-to-End Vision-Based Autonomous Driving via Spatial-Temporal Feature Learning	ECCV 2022	-
UniAD	Planning-Oriented Autonomous Driving	CVPR 2023	-
VAD	VAD: Vectorized Scene Representation for Efficient Autonomous Driving	ICCV 2023	-
OccNet	Scene as Occupancy	ICCV 2023	-
GenAD	GenAD: Generative End-to-End Autonomous Driving	ECCV 2024	-
PARA-Drive	PARA-Drive: Parallelized Architecture for Real-Time Autonomous Driving	CVPR 2024		-
Hydra-MDP	Hydra-MDP: End-to-End Multimodal Planning with Multi-Target Hydra-Distillation	CVPRW 2024
SparseAD	SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving	arXiv 2024	-	-
GaussianAD	GaussianAD: Gaussian-Centric End-to-End Autonomous Driving	arXiv 2024	-	-
DriveTransformer	DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving	ICLR 2025	-
SparseDrive	SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation	ICRA 2025	-
DiffusionDrive	DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving	CVPR 2025	-
GoalFlow	GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving	CVPR 2025
GuideFlow	GuideFlow: Constraint-Guided Flow Matching for Planning in End-to-End Autonomous Driving	arXiv 2025	-
ETA	ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models	arXiv 2025	-
Geo	Spatial Retrieval Augmented Autonomous Driving	arXiv 2025	-	-
DiffusionDriveV2	DiffusionDriveV2: Reinforcement Learning-Constrained Truncated Diffusion Modeling in End-to-End Autonomous Driving	arXiv 2025	-
NaviHydra	NaviHydra: Controllable Navigation-Guided End-to-End Autonomous Driving with Hydra Distillation	arXiv 2025	-	-
Mimir	Mimir: Hierarchical Goal-Driven Diffusion with Uncertainty Propagation for End-to-End Autonomous Driving	arXiv 2025	-
FROST-Drive	FROST-Drive: Scalable and Efficient End-to-End Driving with a Frozen Vision Encoder	arXiv 2026	-	-
DrivoR	Driving on Registers	arXiv 2026
SPS	See Less, Drive Better: Generalizable End-to-End Autonomous Driving via Foundation Models Stochastic Patch Selection	arXiv 2026	-	-
BevAD	What Matters for Scalable and Robust Learning in End-to-End Driving Planners?	CVPR 2026
EgoFSD	EgoFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving	ICRA 2026	-
BeyondSight	BeyondSight: Object Permanence for End-to-End Autonomous Driving	arXiv 2026	-	-
What's	What's Hidden Matters: Identifying Planning-Critical Occluded Agents using Vision-Language Models	arXiv 2026	-	-
Plug	Plug-and-Play Traffic Element Awareness for End-to-End Autonomous Driving	arXiv 2026	-	-
Off	Off the Rails: Hijacking the Scoring Head in Generative End-to-End Driving Planners with Safety-Violating Adversarial Perturbations	arXiv 2026	-	-
Herding	Herding End-to-End Autonomous Driving via Neuro-Symbolic Safety Guards	arXiv 2026	-	-
VG-SAF	Variance-Guided Spatial Attention Fusion for Robust End-to-End Driving under Asymmetric Sensor Degradation	arXiv 2026	-	-
ViRA	Do Better Visual Representations Always Lead to Better End-to-End Autonomous Driving?	arXiv 2026
DriveMamba	DriveMamba: Task-Centric Scalable State Space Model for Efficient End-to-End Autonomous Driving	ICLR 2026	-	-
FPD-Drive	FPD-Drive: Future Policy Distillation for End-to-End Autonomous Driving	arXiv 2026
ConDenseDrive	ConDenseDrive: Knowledge-Dense, Token-Sparse Visual Encoder for End-to-End Autonomous Driving	arXiv 2026	-	-
GRID-Planner	GRID-Planner: Graph-Structured Relational Reward Shaping for Diffusion Planning	arXiv 2026	-
Expand-and-Prune	Expand-and-Prune: Test-Time Candidate-Space Adaptation for End-to-End Autonomous Driving	WACV 2027	-
QualiDrive	From Likelihood to Utility: Quality-Axis Diffusion for End-to-End Autonomous Driving	arXiv 2026	-	-

:three: Image-Based World Models

:timer_clock: In chronological order, from the earliest to the latest.

Model	Paper	Venue	Website	GitHub
DriveDreamer	DriveDreamer: Towards Real-World-Driven World Models for Autonomous Driving	ECCV 2024
GenAD	GenAD: Generalized Predictive Model for Autonomous Driving	CVPR 2024	-
Drive-WM	Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving	CVPR 2024
DrivingWorld	DrivingWorld: Constructing World Model for Autonomous Driving via Video GPT	arXiv 2024
Imagine-2-Drive	Imagine-2-Drive: Leveraging High-Fidelity World Models via Multi-Modal Diffusion Policies	IROS 2025		-
DrivingGPT	DrivingGPT: Unifying Driving World Modeling and Planning with Multi-Modal Autoregressive Transformers	ICCV 2025		-
Epona	Epona: Autoregressive Diffusion World Model for Autonomous Driving	ICCV 2025
VaViM	VaViM and VaVAM: Autonomous Driving through Video Generative Modeling	arXiv 2025
UniDrive-WM	UniDrive-WM: Unified Understanding, Planning and Generation World Model For Autonomous Driving	arXiv 2026		-
DwD	Driving with DINO: Vision Foundation Features as a Unified Bridge for Sim-to-Real Generation in Autonomous Driving	arXiv 2026	-	-
WorldDrive	Bridging Scene Generation and Planning: Driving with World Model via Unifying Vision and Motion Representation	NeurIPS 2026	-
OmniDreams	NVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation	arXiv 2026	-	-
DriveDreamer-Policy	DriveDreamer-Policy: A Geometry-Grounded World-Action Model for Unified Generation and Planning	NeurIPS 2026
DriveVA	DriveVA: Video Action Models are Zero-Shot Drivers	ECCV 2026	-	Models
Xiaomi EV World Model	Xiaomi Auto World Model: A Joint World Model Integrating Reconstruction and Generation for Autonomous Driving	arXiv 2026	-	-
HERMES++	HERMES++: Toward a Unified Driving World Model for 3D Scene Understanding and Generation	arXiv 2026	-	-
LMGenDrive	LMGenDrive: Bridging Multimodal Understanding and Generative World Modeling for End-to-End Driving	arXiv 2026	-	-
X-World	X-World: Controllable Ego-Centric Multi-Camera World Models for Scalable End-to-End Driving	arXiv 2026	-	-
How	How Can Driving World Models Do Counterfactual Prediction?	arXiv 2026	-	-
Orbis	Orbis 2: A Hierarchical World Model for Driving	arXiv 2026	-	-
M$^\text{4}$World:	M$^\text{4}$World: A Multi-view Multimodal Driving World Model for Interactive Object Manipulation and Minute-long Streaming	arXiv 2026	-	-
Is	Is Energy Guidance All You Need? Training-Free Norm Injection for Driving World Models	arXiv 2026	-	-
GeoRoute	GeoRoute: Geometry-Aware Hybrid Inference for Traffic Future-Frame Prediction	arXiv 2026	-	-
Cam2Sim	Cam2Sim: Neural Scenario Reconstruction for Closed-Loop Autonomous Driving Simulation	arXiv 2026	-	-
RealWeather	RealWeather: Realistic and Scene-Faithful Weather Translation with Driving World Models	arXiv 2026	-	-
SUV	SUV: Future Scene Understanding as Video Generation for End-to-End Driving	arXiv 2026	-	Models
ZYT-World	ZYT-World: A Real-Time Controllable World Model for Closed-Loop Autonomous-Driving Simulation	arXiv 2026	-	-
RoXDrive	RoXDrive: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving via Action-Faithful Rollouts	arXiv 2026	-	-
PhysWAM	PhysWAM: Physically Consistent World Action Model for Autonomous Driving	arXiv 2026	-	-
SV-WAM	SV-WAM: An Efficient Surround-View World-Action Model for End-to-End Autonomous Driving	arXiv 2026	-	-
DriveUniTok	DriveUniTok: A Unified Multimodal Tokenizer for Driving World-Action Models	arXiv 2026	-	-
OSWAM	OSWAM: An Effective World Action Model with One-Step Action Generation	arXiv 2026

:four: Occupancy-Based World Models

:timer_clock: In chronological order, from the earliest to the latest.

Model	Paper	Venue	Website	GitHub
OccWorld	OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving	ECCV 2024
NeMo	Neural Volumetric World Models for Autonomous Driving	ECCV 2024	-	-
OccVAR	OCCVAR: Scalable 4D Occupancy Prediction via Next-Scale Prediction	OpenReview 2024	-	-
RenderWorld	RenderWorld: World Model with Self-Supervised 3D Label	arXiv 2024	-	-
DFIT-OccWorld	An Efficient Occupancy World Model via Decoupled Dynamic Flow and Image-assisted Training	arXiv 2024	-	-
Drive-OccWorld	Driving in the Occupancy World: Vision-Centric 4D Occupancy Forecasting and Planning via World Models for Autonomous Driving	AAAI 2025
T³Former	Temporal Triplane Transformers as Occupancy World Models	arXiv 2025	-	-
OmniNWM	OmniNWM: Omniscient Driving Navigation World Models	arXiv 2025	-
AD-R1	AD-R1: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving with Impartial World Models	arXiv 2025	-	-
SparseOccVLA	SparseOccVLA: Bridging Occupancy and Vision-Language Models via Sparse Queries for Unified 4D Scene Understanding and Planning	arXiv 2026	-
GEM	GEM: Gaussian Evolution Model for Occupancy Forecasting and Motion Planning	arXiv 2026	-	-
OWMDrive	OWMDrive: Causality-Aware End-to-End Autonomous Driving via 4D Occupancy World Model	arXiv 2026	-	-
GaussianDWM++	GaussianDWM++: Language-Grounded 3D Gaussian Driving World Model for Unified Scene Understanding, Editing, and Multi-Modal Generation	arXiv 2026	-	-
V2X-WAM	V2X-WAM: A Cooperative World Action Model for End-to-End Autonomous Driving	arXiv 2026	-	-
InfraWorld	InfraWorld: Motion-Aware World Modeling for Roadside 4D Occupancy Forecasting	arXiv 2026	-	Release pending
OccWAM	OccWAM: Occupancy World-Action Models with Joint Scene-to-Ego Flow Matching	arXiv 2026	-	-

:five: Latent-Based World Models

:timer_clock: In chronological order, from the earliest to the latest.

Model	Paper	Venue	Website	GitHub
WA-JEPA	WA-JEPA: Rethinking the Video JEPA Paradigm for World-Action Modeling in Autonomous Driving	arXiv 2026
GeoWAM	GeoWAM: Visual Geometry World Action Models for Autonomous Driving	arXiv 2026	-	-
RISE	RISE: Adaptive Imagination for World Action Models	arXiv 2026	-	-
Covariate-Shift	Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models	arXiv 2024	-	-
World4Drive	World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Model	ICCV 2025	-	-
WoTE	End-to-End Driving with Online Trajectory Evaluation via BEV World Model	ICCV 2025	-
LAW	Enhancing End-to-End Autonomous Driving with Latent World Model	ICLR 2025	-
SSR	Navigation-Guided Sparse Scene Representation for End-to-End Autonomous Driving	ICLR 2025	-
Echo-Planning	Echo Planning for Autonomous Driving: From Current Observations to Future Trajectories and Back	arXiv 2025	-	-
SeerDrive	Future-Aware End-to-End Driving: Bidirectional Modeling of Trajectory Planning and Scene Evolution	NeurIPS 2025	-
Drive-JEPA	Drive-JEPA: Video JEPA Meets Multimodal Trajectory Distillation for End-to-End Driving	arXiv 2026	-
GraphWorld	GraphWorld: Long-Horizon Planning with World Models for End-to-End Autonomous Driving	arXiv 2026	-	-
Unified Driving Tokens	Unified Driving Tokens: Representation- and Geometry-Guided Discrete Tokenizer for Driving World Models and Planning	arXiv 2026	-	-
EponaV2	EponaV2: Driving World Model with Comprehensive Future Reasoning	arXiv 2026	-	-
IDOL	IDOL: Inverse-Dynamics-Guided Future Prediction for End-to-End Autonomous Driving	arXiv 2026	-	-
ExploreVLA	ExploreVLA: Dense World Modeling and Exploration for End-to-End Autonomous Driving	arXiv 2026	-	-
Kinematics-Aware LWM	Kinematics-Aware Latent World Models for Data-Efficient Autonomous Driving	arXiv 2026	-	-
SparseWorld	SparseWorld: Enhancing End-to-End Autonomous Driving via World Models with Sparse Scene Representation	arXiv 2026	-	-
LWM Survey	Latent World Models for Automated Driving: A Unified Taxonomy, Evaluation Framework, and Open Challenges	arXiv 2026	-	-
World Models	World Models: A Comprehensive Survey of Architectures, Methodologies, Reasoning Paradigms, and Applications	arXiv 2026	-	-
DynVLA	DynVLA: Learning World Dynamics for Action Reasoning in Autonomous Driving	arXiv 2026	-	-
DriveWorld-VLA	DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Driving	arXiv 2026	-	-
CRISP	CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining	arXiv 2026	-	-
Geographic	Geographic Diversity Beats Data Volume for Cross-Domain Generalization in Zero-Label JEPA Driving World Models	arXiv 2026	-	-
Auto	Auto-JEPA: A Latent World Model of Continuous Intent for End-to-End Autonomous Driving	arXiv 2026	-	-
BrainWAM	BrainWAM: Action-Space Coordination of Semantic Priors and Predictive Dynamics for Autonomous Driving	arXiv 2026	-	-
X-Mind	X-Mind: Efficient Visual Chain-of-Thought via Predictive World Model for End-to-End Driving	arXiv 2026	-	-
WCog	WCog-VLA: A Dual-Level World-Cognitive Vision-Language-Action Model for End-to-End Autonomous Driving	arXiv 2026	-	-
HyWorldVLA	HyWorldVLA: A Vision-Language-Action Model with Hybrid World Modeling for Autonomous Driving	arXiv 2026	-	-
LWDrive	LWDrive: Layer-Wise World-Model-Guided Vision-Language Model Planning for Autonomous Driving	arXiv 2026	-	-
DriveCache	DriveCache: Action-Aware Caching for Driving World Model Inference	arXiv 2026	-	-
DA-WAM	DA-WAM: Decision-Aligned Future Latents for Driving World Models	arXiv 2026	-
RISE	RISE: Adaptive Imagination for World Action Models	arXiv 2026	-	-
WA-JEPA	WA-JEPA: Rethinking the Video JEPA Paradigm for World-Action Modeling in Autonomous Driving	arXiv 2026
GeoWAM	GeoWAM: Visual Geometry World Action Models for Autonomous Driving	arXiv 2026	-	-
Drive-HWM	Drive-HWM: Hierarchical World Models for Dynamic-Latent Guided Autonomous Driving	arXiv 2026	-	-
MM-Future	MM-Future: Multi-Mode Joint World-Action Modeling for Autonomous Driving	arXiv 2026	-	-
D-JEPA	D-JEPA: A Decision-Aligned Latent World Model	arXiv 2026	-	-
ForeDrive	ForeDrive: Foresight-Guided End-to-End Autonomous Driving with a Planning-Relevant Latent World Model	arXiv 2026	-	-
WALT	WALT: Learning World-Model-Aligned Latent Trajectories for Autonomous Driving	arXiv 2026	-	-
MomWorld	MomWorld: Momentum-Aware Latent World Model for Long-Horizon Autonomous Driving	arXiv 2026	-	-
AD-E2E-JEPA	AD-E2E-JEPA: A Joint-Embedding Predictive Architecture For End-to-End Autonomous Driving	arXiv 2026	-
World4Scorer	World4Scorer: Outcome-Grounded World Modeling for Autonomous Driving	arXiv 2026
ReWAM	ReWAM: Reciprocal World Action Models for Interactive Autonomous Driving	arXiv 2026	-
VGGTWorld-VLA	VGGTWorld-VLA: Intent-Conditioned 3D World Evolution for Autonomous Driving	arXiv 2026	-	-
PlanWAM	PlanWAM: Planning-Shaped Future Representations for End-to-End Autonomous Driving	arXiv 2026
ManeuverFlow	ManeuverFlow: Maneuver Hypothesis Generation with Future Evidence for Trajectory Evaluation and Refinement in End-to-End Autonomous Driving	arXiv 2026	-
MORE	MORE: Learning Motion-Oriented Registers for Driving World Models and Planning	arXiv 2026	-	Release pending
VITAL	VITAL: From Visual Motion to Precise Action	arXiv 2026
WAC	World Action Critic (WAC) Model: Joint Foresight and Criticism for Policy Learning	arXiv 2026	-	-
EvoAD	EvoAD: Self-Evolving Autonomous Driving via Latent World-Model Guided Post-hoc Reflection	arXiv 2026	-	-

2. Vision-Language-Action Models

:one: Textual Action Generator

:timer_clock: In chronological order, from the earliest to the latest.

Model	Paper	Venue	Website	GitHub
GAPL	GAPL: Grounded Action-effect Policy Learning for LLM-Based Trajectory Planning	arXiv 2026	-	-
MAO-LLM	Multi-Agent Orchestration with the Common-Sense Reasoning Capabilities of LLMs for Autonomous Driving	arXiv 2026	-	-
DriveMLM	DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving	arXiv 2023	-
RAG-Driver	RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model	RSS 2024
RDA-Driver	Making Large Language Models Better Planners with Reasoning-Decision Alignment	ECCV 2024	-	-
DriveLM	DriveLM: Driving with Graph Visual Question Answering	ECCV 2024
DriveGPT4	DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model	RA-L 2024		-
DriVLMe	DriVLMe: Enhancing LLM-based Autonomous Driving Agents with Embodied and Social Experience	IROS 2024