: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{survey3d4dworldmodels,     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

• 2. Vision-Language-Action Models

• 3. Datasets & Benchmarks

• 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 | |:-:|:-|:-:|:-:|:-:| || | 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 | - | - | ||

: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 | - | - | | DiFSD |
DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-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 | | | ||

: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 | arXiv 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 | arXiv 2026 | - | - | | DriveVA |
DriveVA: Video Action Models are Zero-Shot Drivers | arXiv 2026 | - | - | | 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 | - | - | ||

: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 | - | - | ||

:five: Latent-Based World Models

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

| Model | Paper | Venue | Website | GitHub | |:-:|:-|:-:|:-:|:-:| || | 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 | - | - | ||

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 | |:-:|:-|:-:|:-:|:-:| || | 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 | | | | LLaDA |
Driving Everywhere with Large Language Model Policy Adaptation | CVPR 2024 | | | | VLAAD |
VLAAD: Vision and Language Assistant for Autonomous Driving | WACVW 2024 | - | | | OccLLaMA |
OccLLaMA: A Unified Occupancy-Language-Action World Model for Understanding and Generation Tasks in Autonomous Driving | arXiv 2024 | | - | | Doe-1 |
Doe-1: Closed-Loop Autonomous Driving with Large World Model | arXiv 2024 | | | | LINGO-2 |
LINGO-2: Driving with Natural Language | - | | - | | SafeAuto |
SafeAuto: Knowledge-Enhanced Safe Autonomous Driving with Multimodal Foundation Models | ICML 2025 | - | | | OpenEMMA |
OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Driving | WACV 2025 | - | | | ReasonPlan |
ReasonPlan: Unified Scene Prediction and Decision Reasoning for Closed-loop Autonomous Driving | CoRL 2025 | - | | | WKER |
World Knowledge-Enhanced Reasoning Using Instruction-Guided Interactor in Autonomous Driving | AAAI 2025 | - | - | | OmniDrive |
OmniDrive: A Holistic LLM-Agent Framework for Autonomous Driving with 3D Perception, Reasoning and Planning | CVPR 2025 | - | | | S4-Driver |
S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Model with Spatio-Temporal Visual Representation | CVPR 2025 | | - | | Occ-LLM |
Occ-LLM: Enhancing Autonomous Driving with Occupancy-BasedLarge Language Models | ICRA 2025 | - | - | | DriveBench |
Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives | ICCV 2025 | | | | FutureSightDrive |
FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving | NeurIPS 2025 | | | | ImpromptuVLA |
Impromptu VLA: Open Weights and Open Data for Driving Vision-Language-Action Models | NeurIPS 2025 | | | | Sce2DriveX |
Sce2DriveX: A Generalized MLLM Framework for Scene-to-Drive Learning | RA-L 2025 | - | - | | EMMA |
EMMA: End-to-End Multimodal Model for Autonomous Driving | TMLR 2025 | | - | | DriveAgent-R1 |
DriveAgent-R1: Advancing VLM-Based Autonomous Driving with Hybrid Thinking and Active Perception | arXiv 2025 | - | - | | Drive-R1 |
Drive-R1: Bridging Reasoning and Planning in VLMs for Autonomous Driving with Reinforcement Learning | arXiv 2025 | - | - | | FastDriveVLA |
FastDriveVLA: Efficient End-to-End Driving via Plug-and-Play Reconstruction-Based Token Pruning | arXiv 2025 | - | - | | WiseAD |
WiseAD: Knowledge Augmented End-to-End Autonomous Driving with Vision-Language Model | arXiv 2025 | | | | AutoDrive-R² |
AutoDrive-R²: Incentivizing Reasoning and Self-Reflection Capacity for VLA Model in Autonomous Driving | arXiv 2025 | - | - | | OmniReason |
OmniReason: A Temporal-Guided Vision-Language-Action Framework for Autonomous Driving | arXiv 2025 | - | - | | OpenREAD |
OpenREAD: Reinforced Open-Ended Reasoning for End-to-End Autonomous Driving with LLM-as-Critic | arXiv 2025 | - | | | dVLM-AD |
dVLM-AD: Enhance Diffusion Vision-Language-Model for Driving via Controllable Reasoning | arXiv 2025 | - | - | | PLA |
A Unified Perception-Language-Action Framework for Adaptive Autonomous Driving | arXiv 2025 | - | - | | AlphaDrive |
AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning | arXiv 2025 | - | | | CoReVLA |
CoReVLA: A Dual-Stage End-to-End Autonomous Driving Framework for Long-Tail Scenarios via Collect-and-Refine | arXiv 2025 | | | | WAM-Diff |
WAM-Diff: A Masked Diffusion VLA Framework with MoE and Online Reinforcement Learning for Autonomous Driving | arXiv 2025 | - | | | VLADriveBench |
VLADriveBench: Evaluating CoT-Action Relationship in VLA for Autonomous Driving | arXiv 2026 | - | - | | BLUE |
BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving | arXiv 2026 | - | - | | DriveMA |
DriveMA: Rethinking Language Interfaces in Driving VLAs with One-Step Meta-Actions | arXiv 2026 | - | - | | C-CoT |
C-CoT: Counterfactual Chain-of-Thought with Vision-Language Models for Safe Autonomous Driving | arXiv 2026 | - | - | | MAGNIFIED |
MAGNIFIED: RL Fine-tuning of Multimodal Large Language Models for Motion Planning | arXiv 2026 | - | - | | DriveReward |
DriveReward: A Comprehensive Dataset and Generative Vision-Language Reward Model for Autonomous Driving | arXiv 2026 | - | - | | nuReasoning |
nuReasoning: A Reasoning-Centric Dataset and Benchmark for Long-Tail Autonomous Driving | arXiv 2026 | - | - | | Decision-Making |
Decision-Making with Lightweight Confidence-Aware Language Model for Autonomous Driving | arXiv 2026 | - | - | | Is |
Is VLA Reasoning Faithful? Probing Safety of Chain-of-Causation in Autonomous Driving Models | arXiv 2026 | - | - | | ReasonBreak |
ReasonBreak: Probing Vulnerabilities in Reasoning-Enabled Vision-Language-Action Models for Autonomous Driving | arXiv 2026 | - | - | | Intend, |
Intend, Reflect, Refine: An Adaptive Multimodal Reflection Framework for Autonomous Driving | arXiv 2026 | - | - | | Judge, Then Drive |
Judge, Then Drive: A Critic-Centric Vision Language Action Framework for Autonomous Driving | arXiv 2026 | - | - | | EvoDrive |
EvoDrive: Pareto Evolution for Safety-Critical Autonomous Driving via Self-Improving LLM Agents | arXiv 2026 | - | - | | Unifying |
Unifying Language-Action Understanding and Generation for Autonomous Driving | arXiv 2026 | - | - | | MindDriver |
MindDriver: Introducing Progressive Multimodal Reasoning for Autonomous Driving | arXiv 2026 | - | - | | HERMES |
HERMES: A Holistic End-to-End Risk-Aware Multimodal Embodied System with Vision-Language Models for Long-Tail Autonomous Driving | arXiv 2026 | - | - | | Counterfactual VLA |
Counterfactual VLA: Self-Reflective Vision-Language-Action Model with Adaptive Reasoning | arXiv 2025 | - | - | | OmniDrive-R1 |
OmniDrive-R1: Reinforcement-driven Interleaved Multi-modal Chain-of-Thought for Trustworthy Vision-Language Autonomous Driving | arXiv 2025 | - | - | | BeLLA |
BeLLA: End-to-End Birds Eye View Large Language Assistant for Autonomous Driving | arXiv 2025 | - | - | ||

:two: Numerical Action Generator

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

| Model | Paper | Venue | Website | GitHub | |:-:|:-|:-:|:-:|:-:| || | LMDrive |
LMDrive: Closed-Loop End-to-End Driving with Large Language Models | CVPR 2024 | | | | BEVDriver |
BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving | IROS 2025 | - | - | | CoVLA-Agent |
CoVLA: Comprehensive Vision-Language-Action Dataset for Autonomous Driving | WACV 2025 | | - | | ORION |
ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation | ICCV 2025 | | | | SimLingo |
SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment | CVPR 2025 | | | | DriveGPT4-V2 |
DriveGPT4-V2: Harnessing Large Language Model Capabilities for Enhanced Closed-Loop Autonomous Driving | CVPR 2025 | - | - | | AutoVLA |
AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning | NeurIPS 2025 | | | | DriveMoE |
DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving | arXiv 2025 | | | | DSDrive |
DSDrive: Distilling Large Language Model for Lightweight End-to-End Autonomous Driving with Unified Reasoning and Planning | arXiv 2025 | - | - | | OccVLA |
OccVLA: Vision-Language-Action Model with Implicit 3D Occupancy Supervision. | arXiv 2025 | - | - | | VDRive |
VDRive: Leveraging Reinforced VLA and Diffusion Policy for End-to-End Autonomous Driving | arXiv 2025 | - | - | | ReflectDrive |
Discrete Diffusion for Reflective Vision-Language-Action Models in Autonomous Driving | arXiv 2025 | - | | | E3AD |
E3AD: An Emotion-Aware Vision-Language-Action Model for Human-Centric End-to-End Autonomous Driving | arXiv 2025 | - | - | | LCDrive |
Latent Chain-of-Thought World Modeling for End-to-End Driving | arXiv 2025 | - | - | | Alpamayo-R1 |
Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail | arXiv 2025 | - | - | | UniUGP |
UniUGP: Unifying understanding, generation, and planing for end-to-end autonomous driving. | arXiv 2025 | - | - | | MindDrive |
MindDrive: An All-in-One Framework Bridging World Models and Vision-Language Model for End-to-End Autonomous Driving | arXiv 2025 | - | - | | AdaThinkDrive |
AdaThinkDrive: Adaptive Thinking via Reinforcement Learning for Autonomous Driving | arXiv 2025 | - | - | | Percept-WAM |
Percept-WAM: Perception-Enhanced World-Awareness-Action Model for Robust End-to-End Autonomous Driving | arXiv 2025 | - | - | | Reasoning-VLA |
Reasoning-VLA: A Fast and General Vision-Language-Action Reasoning Model for Autonomous Driving | arXiv 2025 | - | - | | SpaceDrive |
SpaceDrive: Infusing Spatial Awareness into VLM-Based Autonomous Driving | arXiv 2025 | - | - | | OpenDriveVLA |