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FM-AD-Survey

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[Survey Paper] This repository collects research papers of large Foundation Models for Scenario Generation and Analysis in Autonomous Driving. The repository will be continuously updated to track the latest update.

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# Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis :car:
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This repository will collect research, implementations, and resources related to **Foundation Models for Scenario Generation and Analysis** in autonomous driving. The repository will be maintained by [TUM-AVS](https://www.mos.ed.tum.de/avs/startseite/) (Professorship of Autonomous Vehicle Systems at Technical University of Munich) and will be continuously updated to track the latest work in the community.

## :fire: Updates

<img src="Assets/paper_timeline.png" width="100%" height="auto"/>

- **Jul. 2026** – Added **4 new papers** on scenario generation* and **14 new papers** on scenario analysis*.
- **Jun. 2026** – Added **33 new papers** on scenario analysis* (monthly backlog catch-up spanning Dec 2023 – Jun 2026).
- **May 2026** – Added **7 new papers** on scenario generation* and **19 new papers** on scenario analysis*.
- **Apr. 2026** – Added **5 new papers** on scenario generation* and **13 new papers** on scenario analysis*.
- **Mar. 2026** – Added **10 new papers** on scenario generation* and **11 new papers** on scenario analysis*.
- **Feb. 2026** – Added **4 new papers** on scenario generation* and **19 new papers** on scenario analysis*.
- **Jan. 2026** – 🎉 Our survey paper is accepted by **IEEE Open Journal of Intelligent Transportation Systems (OJ-ITS)** and uploaded a new version to arXiv.
- **Dec. 2025** – Added **3 new papers** on scenario generation* and **14 new papers** on scenario analysis*. Added new columns: **Hardware and Citation**.
- **Nov. 2025** – Added **2 new papers** on scenario analysis*. Added new section: **Useful Resources and Links**.
- Uploaded new version to arXiv. Repository now categorizes **348 papers**:
  - 93 on scenario generation
  - 56 on scenario analysis
  - 58 on datasets
  - 21 on simulators
  - 25 on benchmark challenges
  - 95 on other related topics (e.g., FMs' implementation)
- **Oct. 2025** – Added **17 new papers** on scenario generation* and **2 new papers** on scenario analysis*. 
- **Sep. 2025** – Added **3 new papers** on scenario generation* and **14 new papers** on scenario analysis*. 
- **Aug. 2025** – Added **4 new papers** on scenario generation* and **4 new papers** on scenario analysis*.  
- **Jul. 2025** – Added **9 new papers** on scenario generation* and **8 new papers** on scenario analysis*.  
- **Jun. 2025** – Released our paper on arXiv. Repository now categorizes **342 papers**:  
  - 93 on scenario generation  
  - 54 on scenario analysis  
  - 55 on datasets  
  - 21 on simulators  
  - 25 on benchmark challenges  
  - 94 on other related topics  
- **May 2025** – Repository initialized.

## 🤝 &nbsp; Citation
Please visit [Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis](https://arxiv.org/abs/2506.11526) for more details and comprehensive information. If you find our paper and repo helpful, please consider citing it as follows:

```BibTeX
@ARTICLE{11370877,
  author={Gao, Yuan and Piccinini, Mattia and Zhang, Yuchen and Wang, Dingrui and Moller, Korbinian and Brusnicki, Roberto and Zarrouki, Baha and Gambi, Alessio and Totz, Jan Frederik and Storms, Kai and Peters, Steven and Stocco, Andrea and Alrifaee, Bassam and Pavone, Marco and Betz, Johannes},
  journal={IEEE Open Journal of Intelligent Transportation Systems}, 
  title={Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis}, 
  year={2026},
  volume={},
  number={},
  pages={1-1},
  keywords={Autonomous vehicles;Surveys;Foundation models;Scenario generation;Frequency modulation;Visualization;Reviews;Diffusion models;Adaptation models;Cognition;Autonomous vehicles;foundation model;scenario generation;scenario analysis;scenario based testing},
  doi={10.1109/OJITS.2026.3660686}}
```

## :page_with_curl: Introduction
Foundation models are large-scale, pre-trained models that can be adapted to a wide range of downstream tasks. In the context of autonomous driving, foundation models offer a powerful approach to scenario generation and analysis, enabling more comprehensive and realistic testing, validation, and verification of autonomous driving systems. This repository aims to collect and organize research, tools, and resources in this important field.

## :chart_with_upwards_trend: Publication Timeline
The following figure shows the evolution of foundation model research in autonomous driving scenario generation and analysis over time:

## :mag: Search Methodology
The following list of keywords was used to search this survey's papers in the Google Scholar database. The keywords were entered either individually or in combination with other keywords in the list. The search was conducted until May 2025.

**Keywords:**
- **Foundation Model Types:** Foundation Models, Large Language Models (LLMs), Vision-Language Models (VLMs), Multimodal Large Language Models (MLLMs), Diffusion Models (DMs), World Models (WMs), Generative Models (GMs)
- **Scenario Generation & Analysis:** Scenario Generation, Scenario Simulation, Traffic Simulation, Scenario Testing, Scenario Understanding, Driving Scene Generation, Scene Reasoning, Risk Assessment, Safety-Critical Scenarios, Accident Prediction
- **Application Context:** Autonomous Driving, Self-Driving Vehicles, AV Simulation, Driving Video Generation, Traffic Datasets, Closed-Loop Simulation, Safety Assurance

## 🕒 Timeline of the Development of Foundation Models

The figure illustrates the evolution of foundation models. **LLMs** (e.g., BERT, GPT) appear at the bottom, followed by **VLMs** built on visual FMs (e.g., ViT, CLIP) and **instruction-tuned VLMs** for interactive vision–language reasoning. **MLLMs** are shown at the top. In parallel, the progression of visual FMs is traced through **Diffusion Models (DMs)** and **World Models (WMs)**. Highlighted entries mark key conceptual milestones.

## 🌟 Large Language Models for Autonomous Driving
<details open>
<summary>Scenario Generation (LLM)</summary>

| Paper | Date | Venue | Code | Hardware | Citation |
|:------|:-----|:------|:-----|:---------|:---------|
| [TARGET: Automated Scenario Generation from Traffic Rules for Testing Autonomous Vehicles](https://arxiv.org/abs/2305.06018) | 2023-05 | IEEE Transactions on Software Engineering | - | API&RTX4090 | 24 |
| [Language Conditioned Traffic Generation](https://arxiv.org/abs/2307.07947) | 2023-07 | CoRL 2023 | [GitHub](https://github.com/Ariostgx/lctgen/) | API | 98 |
| [A Generative AI-driven Application: Use of Large Language Models for Traffic Scenario Generation](https://ieeexplore.ieee.org/document/10415934) | 2023-11 | ELECO 2023 | - | API | 15 |
| [ChatGPT-Based Scenario Engineer: A New Framework on Scenario Generation for Trajectory Prediction](https://ieeexplore.ieee.org/document/10423819) | 2024-02 | IEEE Transactions on Intelligent Vehicles | - |- | 48 |
| [Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation](https://arxiv.org/abs/2404.08570) | 2024-04 | arXiv | [GitHub](https://github.com/zachtian/CRITICAL) | A6000 | 24 |
| [LLMScenario: Large Language Model Driven Scenario Generation](https://ieeexplore.ieee.org/document/10529537) | 2024-05 | IEEE Transactions on Systems, Man, and Cybernetics: Systems | - | API | 69 |
| [Automatic Generation Method for Autonomous Driving Simulation Scenarios Based on Large Language Model](https://link.springer.com/chapter/10.1007/978-981-96-3977-9_10) | 2024-05 | AIAT 2024 | - | API | 2 |
| [ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10655362) | 2024-05 | CVPR 2024 | [GitHub](https://github.com/javyduck/ChatScene) | API |113 |
| [Editable scene simulation for autonomous driving via collaborative llm-agents](https://ieeexplore.ieee.org/document/10656629) | 2024-06 | CVPR 2024 | [GitHub](https://github.com/yifanlu0227/ChatSim) | API |130 |
| [Chat2Scenario: Scenario Extraction From Dataset Through Utilization of Large Language Model](https://ieeexplore.ieee.org/document/10588843) | 2024-06 | IV 2024 | [GitHub](https://github.com/ftgTUGraz/Chat2Scenario) | - |15 |
| [SoVAR: Building Generalizable Scenarios from Accident Reports for Autonomous Driving Testing](https://arxiv.org/abs/2409.08081) | 2024-09 | ASE 2024 | - | RTX4070 | 29 |
| [LeGEND: A Top-Down Approach to Scenario Generation of Autonomous Driving Systems Assisted by Large Language Models](https://arxiv.org/abs/2409.10066) | 2024-09 | ASE 2024 | [GitHub](https://github.com/MayDGT/LeGEND) | RTX3090 | 32 |
| [Traffic Scene Generation from Natural Language Description for Autonomous Vehicles with Large Language Model](https://arxiv.org/abs/2409.09575) | 2024-09 | arXiv | [GitHub](https://github.com/basiclab/TTSG) | API | 18 |
| [Promptable Closed-loop Traffic Simulation](https://arxiv.org/abs/2409.05863) | 2024-09 | CoRL 2024 | [GitHub](https://ariostgx.github.io/ProSim/) | A100 | 16 |
| [Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles](https://arxiv.org/abs/2409.06450) | 2024-09 | Automotive Innovation | - | - | 26 |
| [LLM-Driven Testing for Autonomous Driving Scenarios](https://ieeexplore.ieee.org/document/10852505) | 2024-11 | FLLM 2024 | - | API& T4 | 15 |
| [ChatSUMO: Large Language Model for Automating Traffic Scenario Generation in Simulation of Urban MObility](https://ieeexplore.ieee.org/document/10770822) | 2024-11 | IEEE Transactions on Intelligent Vehicles | - | RTX4090 | 54 |
| [Generating Out-Of-Distribution Scenarios Using Language Models](https://arxiv.org/abs/2411.16554) | 2024-11 | arXiv | - | API | 13 |
| [Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner](https://arxiv.org/abs/2412.18086) | 2024-12 | AAAI 2025 Oral | [GitHub](https://ezharjan.github.io/AutoSceneGen/) | RTX 3090 | 3 |
| [LLM-attacker: Enhancing Closed-loop Adversarial Scenario Generation for Autonomous Driving with Large Language Models](https://arxiv.org/abs/2501.15850) | 2025-01 | TITS 2025 | - |RTX 4000 | 25 |
| [ML-SceGen: A Multi-level Scenario Generation Framework](https://arxiv.org/abs/2501.10782) | 2025-01 | arXiv | - | RTX 4090 | 0 |
| [From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarios](https://arxiv.org/abs/2502.02145) | 2025-02 | ITSC 2025 | [GitHub](https://github.com/TUM-AVS/From-Words-to-Collisions) | API | 3 |
| [CurricuVLM: Towards Safe Autonomous Driving via Personalized Safety-Critical Curriculum Learning with Vision-Language Models](https://arxiv.org/abs/2502.15119) | 2025-02 | arXiv | [GitHub](https://zihaosheng.github.io/CurricuVLM/) | - | 10 |
| [Text2Scenario: Text-Driven Scenario Generation for Autonomous Driving Test](https://arxiv.org/abs/2503.02911) | 2025-03 | arXiv | [GitHub](https://caixxuan.github.io/Text2Scenario.GitHub.io/) | - | 14 |
| [Enhancing Autonomous Driving Safety with Collision Scenario Integration](https://arxiv.org/abs/2503.03957) | 2025-03 | arXiv | - | 8xV100 | 6 |
| [Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Models](https://arxiv.org/abs/2505.00972) | 2025-05 | ITSC 2025 | - | API | 5 |
| [From Failures to Fixes: LLM-Driven Scenario Repair for Self-Evolving Autonomous Driving](https://arxiv.org/abs/2505.22067) | 2025-05 | arXiv | - | RTX4090 | 0 |
| [AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework](https://www.arxiv.org/abs/2507.13729v1) | 2025-07 | arXiv | - | API | 1 |
| [LLM-based Realistic Safety-Critical Driving Video Generation](https://www.arxiv.org/abs/2507.01264v1) | 2025-07 | arXiv | - | RTX4090 | 1 |
| [Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles](https://arxiv.org/abs/2508.14527) | 2025-08 | arXiv | [GitHub](https://scenge.github.io/) | 2xRTX4090 | 0 |
| [LLM-based Human-like Traffic Simulation for Self-driving Tests](https://arxiv.org/abs/2508.16962) | 2025-08 | arXiv | - | API& A800| 0 |
| [Conversational Code Generation: a Case Study of Designing a Dialogue System for Generating Driving Scenarios for Testing Autonomous Vehicles](https://arxiv.org/abs/2410.09829) | 2025-09 | GeCoIn 2025 | - | A6000 | 3 |
| [Txt2Sce: Scenario Generation for Autonomous Driving System Testing Based on Textual Reports](https://arxiv.org/abs/2509.02150) | 2025-09 | arXiv | - | RTX3070Ti | 1 |
| [LLM‑Based Semantic Modeling & Cooperative Evolutionary Fuzzing](https://conf.researchr.org/details/apsec-2025/apsec-2025-papers/53/LLM-Based-Semantic-Modeling-and-Cooperative-Evolutionary-Fuzzing-for-Traffic-Violatio) | 2025-09 | APSEC 2025 | - | - | 0 |
| [LinguaSim: Interactive Multi-Vehicle Testing Scenario Generation via Natural Language Instruction Based on Large Language Models](https://arxiv.org/abs/2510.08046) | 2025-10 | IEEE ITSC 2025 | - | - | 0 |
| [Learning from Risk: LLM-Guided Generation of Safety-Critical Scenarios with Prior Knowledge](https://arxiv.org/pdf/2511.20726) | 2025-11 | arXiv | [GitHub](https://github.com/echoleaeperw/LRF) | - | 0 |
| [AnchorDrive: LLM Scenario Rollout with Anchor-Guided Diffusion Regeneration for Safety-Critical Scenario Generation](https://arxiv.org/pdf/2603.02542) | 2026-03 | arXiv | - | - | 0 |
| [TRACE: Topology-aware Reconstruction of Accidents in CARLA for AV Evaluation](https://arxiv.org/abs/2604.22068) | 2026-04 | FSE 2026 | [GitHub](https://github.com/NahianSalsabil/carla-benchmark) | API | 0 |
| [Traffic Scenario Orchestration from Language via Constraint Satisfaction](https://arxiv.org/abs/2605.06966) | 2026-05 | arXiv | - | - | 0 |
| [PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment](https://arxiv.org/abs/2605.15654) | 2026-05 | arXiv | [Project](https://zhenhaooo.github.io/PCASim.github.io/) | - | 0 |
| [TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation](https://github.com/TrafficComposer/TrafficAlign) | 2026-06 | CVPR 2026 | [GitHub](https://github.com/TrafficComposer/TrafficAlign) | - | 0 |
</details>

<details open>
<summary>Scenario Analysis (LLM)</summary>

| Paper | Date | Venue | Code | Hardware | Citation |
|:------|:-----|:------|:-----|:---------|:---------|
| [Semantic Anomaly Detection with Large Language Models](https://arxiv.org/abs/2305.11307) | 2023-09 | Autonomous Robots | - | - | 131 |
| [LaMPilot: An Open Benchmark Dataset for Autonomous Driving with Language Model Programs](https://arxiv.org/abs/2312.04372) | 2023-12 | CVPR 2024 | [GitHub](https://github.com/PurdueDigitalTwin/LaMPilot) | - | 0 |
| [Hybrid Reasoning Based on Large Language Models for Autonomous Car Driving](https://arxiv.org/abs/2402.13602) | 2024-02 | arXiv | [GitHub](https://github.com/Mehdiazarafza/Hybrid-reasoning) | - | 0 |
| [Reality Bites: Assessing the Realism of Driving Scenarios with Large Language Models](https://arxiv.org/abs/2403.09906) | 2024-03 | IEEE/ACM First International Conference on AI Foundation Models and Software Engineering (Forge) | [GitHub](https://github.com/Simula-COMPLEX/RealityBites) | API | 22 |
| [Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving](https://ieeexplore.ieee.org/abstract/document/10611018) | 2024-05 | ICRA 2024 | [GitHub](https://github.com/wayveai/Driving-with-LLMs) | API | 340 |
| [Bench4Merge: A Comprehensive Benchmark for Merging in Realistic Dense Traffic with Micro-Interactive Vehicles](https://arxiv.org/abs/2410.15912) | 2024-10 | arXiv | [GitHub](https://github.com/WZM5853/Bench4Merge) | - | 0 |
| [SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving](https://arxiv.org/abs/2501.03535) | 2025-01 | 2025 WACVW | - | API | 9 |
| [From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarios](https://arxiv.org/abs/2502.02145) | 2025-02 | ITSC 2025 | [GitHub](https://github.com/TUM-AVS/From-Words-to-Collisions) | API | 3 |
| [A Comprehensive LLM-powered Framework for Driving Intelligence Evaluation](https://arxiv.org/abs/2503.05164) | 2025-03 | ICRA 2025 | [GitHub](https://github.com/AIR-DISCOVER/Driving-Intellenge-Evaluation-Framework) | API | 7 |
| [Understanding Driving Risks using Large Language Models: Toward Elderly Driver Assessment](https://arxiv.org/abs/2507.08367) | 2025-07 | arXiv| - | - | 0 |
| [Collision risk prediction and takeover requirements assessment based on radar-video integrated sensors data: A system framework based on LLM](https://www.sciencedirect.com/science/article/pii/S0001457525001277) | 2025-08 | Accident Analysis & Prevention | - | API&RTX4090 | 7 |
| [Evaluation of Large Language Models for Anomaly Detection in Autonomous Vehicles](https://arxiv.org/abs/2509.05315) | 2025-09 | arXiv | - | - | 0 |
| [AgentDrive: An open benchmark suite for agentic AI reasoning in autonomous systems](https://arxiv.org/abs/2601.16964) | 2026-01 | arXiv | [GitHub](https://github.com/maferrag/AgentDrive) | - | 0 |
| [LLM-MLFFN: Multi-Level Autonomous Driving Behavior Feature Fusion via Large Language Model](https://arxiv.org/abs/2603.02528) | 2025-03 | arXiv | - | - | 0 |
| [Towards Lawful Autonomous Driving: Deriving Scenario-Aware Driving Requirements from Traffic Laws and Regulations](https://arxiv.org/abs/2604.24562) | 2026-04 | arXiv | - | - | 0 |
| [SwarmDrive: Semantic V2V Coordination for Latency-Constrained Cooperative Autonomous Driving](https://arxiv.org/abs/2604.22852) | 2026-04 | arXiv | - | - | 0 |
| [Pedestrian-Aware LLM-Driven Behavioral Planning for Autonomous Vehicles](https://arxiv.org/abs/2605.16858) | 2026-05 | IEEE ITSC 2026 | - | - | 0 |
| [AutoMine Solution for AV2 2026 Scenario Mining Challenge](https://arxiv.org/abs/2606.11874) | 2026-06 | arXiv | - | - | 0 |
| [A knowledge-augmented dataset of high-risk driving scenarios with LLM annotations for autonomous driving](https://arxiv.org/abs/2607.07103) | 2026-07 | arXiv | [GitHub](https://github.com/benmagnifico/K-Risk) | - | 0 |
</details>

## 🌟 Vision-Language Models for Autonomous Driving

<details open>
<summary>Scenario Generation (VLM)</summary>

| Paper | Date | Venue | Code | Hardware | Citation |
|:------|:-----|:------|:-----|:---------|:---------|
| [WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models](https://arxiv.org/pdf/2305.07528) | 2023-05 | CVPR workshop 2023 | [GitHub](https://github.com/Infernolia/WEDGE) | API | 63 |
| [DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving](https://ieeexplore.ieee.org/abstract/document/10786438) | 2024-08 | IAVVC 2024 | - | L4 |13|
| [From Dashcam Videos to Driving Simulations: Stress Testing Automated Vehicles against Rare Events](https://arxiv.org/abs/2411.16027) | 2024-11 | arXiv | - | API |7 |
| [From Accidents to Insights: Leveraging Multimodal Data for Scenario-Driven ADS Testing](https://arxiv.org/abs/2502.02025) | 2025-02 | arXiv | - | API |4 |
| [CurricuVLM: Towards Safe Autonomous Driving via Personalized Safety-Critical Curriculum Learning with Vision-Language Models](https://arxiv.org/abs/2502.15119) | 2025-02 | arXiv | [GitHub](https://zihaosheng.github.io/CurricuVLM/) | - |8 |
| [CrashAgent: Crash Scenario Generation via Multi-modal Reasoning](https://www.arxiv.org/abs/2505.18341) | 2025-05 | arXiv | - | API |2 |
| [SEAL: Vision-Language Model-Based Safe End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling](https://arxiv.org/pdf/2506.21041) | 2025-07 | arXiv | - | - |0 |
| [BENCH2ADVLM: A Closed-Loop Benchmark for Vision-language Models in Autonomous Driving](https://www.arxiv.org/abs/2508.02028v2) | 2025-08 | arXiv | - | 8×A800 |2 |
| [Vision Language Model-based Testing of Industrial Autonomous Mobile Robots](https://www.arxiv.org/abs/2508.02338v1) | 2025-08 | arXiv | - | - |1 |
| [VLMasStrategist: Adaptive Generation of Safety-critical Testing Scenarios via Guided Diffusion](https://arxiv.org/pdf/2512.02844) | 2025-12 | arXiv | - | - |0 |
| [Any2Critical: Safety-Critical Scenario Generation from Arbitrary Real-World Driving Contexts](https://ojs.aaai.org/index.php/AAAI/article/view/40861) | 2026-03 | AAAI 2026 | [GitHub](https://github.com/Steven-iai/Any2Critical) | - |0 |
| [CT-1: Vision-Language-Camera Models Transfer Spatial Reasoning Knowledge to Camera-Controllable Video Generation](https://arxiv.org/abs/2604.09201) | 2026-04 | arXiv | [GitHub](https://github.com/gulucaptain/Camera-Transformer-1) | - | 0 |
| [Agent-driven Long-tail Simulation for Autonomous Driving](https://arxiv.org/abs/2607.04331) | 2026-07 | arXiv | [Project](https://tsinghua-mars-lab.github.io/SemanticPlan) | - | 0 |
</details>

<details open>
<summary>Scenario Analysis (VLM)</summary>

| Paper | Date | Venue | Code | Hardware | Citation |
|:------|:-----|:------|:-----|:---------|:---------|
| [Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Driving](https://arxiv.org/abs/2309.14491) | 2023-09 | ICCV 2023 | - | - | 49 |
| [OpenAnnotate3D: Open-Vocabulary Auto-Labeling System for Multi-modal 3D Data](https://arxiv.org/abs/2310.13398) | 2023-10 | ICRA 2024 | [GitHub](https://github.com/Fudan-ProjectTitan/OpenAnnotate3D) | API&RTX4090| 19 |
| [On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving](https://arxiv.org/abs/2311.05332) | 2023-11 | ICIL 2024 Workshop on Large Language Models for Agents | [GitHub](https://github.com/PJLab-ADG/GPT4V-AD-Exploration) | - | 105 |
| [Talk2BEV: Language-enhanced Bird's-eye View Maps for Autonomous Driving](https://arxiv.org/abs/2310.02251) | 2023-11 | ICRA 2024 | [GitHub](https://github.com/llmbev/talk2bev) | A100 | 114 |
| [LLM Multimodal Traffic Accident Forecasting](https://www.mdpi.com/1424-8220/23/22/9225) | 2023-11 | Sensors 2023 MDPI | - | - | 98 |
| [NuScenes-MQA: Integrated Evaluation of Captions and QA for Autonomous Driving Datasets using Markup Annotations](https://ieeexplore.ieee.org/abstract/document/10495633) | 2024-01 | WACVW LLVM-AD 2024 | [GitHub](https://github.com/turingmotors/NuScenes-MQA) | 8xA100 |35 |
| [Is it safe to cross? Interpretable Risk Assessment with GPT-4V for Safety-Aware Street Crossing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10597464) | 2024-02 | UR 2024 | - | - |22 |
| [Multi-Frame, Lightweight & Efficient Vision-Language Models for Question Answering in Autonomous Driving](https://arxiv.org/abs/2403.19838) | 2024-03 | VLADR 2024 | [GitHub](https://github.com/akshaygopalkr/EM-VLM4AD) | RTX 3090Ti&V100 | 55 |
| [Embodied Understanding of Driving Scenarios (ELM)](https://arxiv.org/abs/2403.04593) | 2024-03 | ECCV 2024 | [GitHub](https://github.com/OpenDriveLab/ELM) | - | 0 |
| [LATTE: A Real-time Lightweight Attention-based Traffic Accident Anticipation Engine](https://arxiv.org/abs/2504.04103) | 2024-04 | Information Fusion (Elsevier) | - | RTX4080 | 3 |
| [OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning](https://arxiv.org/abs/2405.01533) | 2024-05 | CVPR 2025 | [GitHub](https://github.com/NVlabs/OmniDrive) | - |47 |
| [Hard Cases Detection in Motion Prediction by Vision-Language Foundation Models](https://arxiv.org/abs/2405.20991) | 2024-05 | arXiv | [GitHub](https://github.com/KTH-RPL/Detect_VLM) | - | 0 |
| [Reason2Drive: Towards Interpretable and Chain-based Reasoning for Autonomous Driving](https://arxiv.org/abs/2312.03661) | 2024-06 | ECCV 2024 | [GitHub](https://github.com/fudan-zvg/reason2drive) | 8xV100 |118 |
| [ConnectGPT: Connect Large Language Models with Connected and Automated Vehicles](https://ieeexplore.ieee.org/document/10588835) | 2024-06 | IV 2024 | - | - |21 |
| [SimpleLLM4AD: An End-to-End Vision-Language Model with Graph Visual Question Answering for Autonomous Driving](https://arxiv.org/abs/2407.21293) | 2024-07 | arXiv | - | - |14 |
| [Large Language Models Powered Context-aware Motion Prediction in Autonomous Driving](https://arxiv.org/abs/2403.11057) | 2024-07 | IROS 2024 | [GitHub](https://github.com/AIR-DISCOVER/LLM-Augmented-MTR) | API | 13 |
| [DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving](https://ieeexplore.ieee.org/abstract/document/10786438) | 2024-08 | IAVVC 2024 | - | L4 |13 |
| [V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models](https://arxiv.org/abs/2408.09251) | 2024-08 | arXiv | - | RTX4090 | 46 |
| [Multi-Frame Vision-Language Model for Long-form Reasoning in Driver Behavior Analysis](https://arxiv.org/abs/2408.01682) | 2024-08 | arXiv | - | - | 0 |
| [Think-Driver: From Driving-Scene Understanding to Decision-Making with Vision Language Models](https://mllmav.github.io/papers/Think-Driver:%20From%20Driving-Scene%20Understanding%20to%20Decision-Making%20with%20Vision%20Language%20Models.pdf) | 2024-09 | ECCV 2024 Workshop | - | 4xRTX4090 | 4 |
| [Can LVLMs Obtain a Driver's License? A Benchmark Towards Reliable AGI for Autonomous Driving](https://arxiv.org/abs/2409.02914) | 2024-09 | AAAI 2025 | [Project](https://4dvlab.github.io/project_page/idkb.html) | - | 0 |
| [ContextVLM: Zero-Shot and Few-Shot Context Understanding for Autonomous Driving using Vision Language Models](https://arxiv.org/abs/2409.00301) | 2024-09 | arXiv | [GitHub](https://github.com/ssuralcmu/ContextVLM) | - | 0 |
| [Efficient Driving Behavior Narration and Reasoning on Edge Device Using Large Language Models](https://arxiv.org/abs/2409.20364) | 2024-09 | arXiv | - | - | 0 |
| [VLM-Auto: VLM-based Autonomous Driving Assistant with Human-like Behavior and Understanding for Complex Road Scenes](https://arxiv.org/abs/2405.05885) | 2024-10 | FLLM 2024 | [GitHub](https://github.com/ZionGo6/VLM-Auto) | RTX4090 |41 |
| [Visual Adversarial Attack on Vision-Language Models for Autonomous Driving](https://arxiv.org/abs/2411.18275) | 2024-11 | arXiv | - | A800 | 19 |
| [Automated Evaluation of Large Vision-Language Models on Self-driving Corner Cases](https://arxiv.org/abs/2404.10595) | 2024-12 | WACV 2025 | [GitHub](https://coda-dataset.github.io/coda-lm/) | 8xA800 | 33 |
| [SFF Rendering-Based Uncertainty Prediction using VisionLLM](https://openreview.net/forum?id=q8ptjh1pDl) | 2024-12 | AAAI 2025 Workshop LM4Plan | - | A100 |3 |
| [AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving](https://arxiv.org/abs/2412.15206) | 2024-12 | arXiv | [GitHub](https://github.com/taco-group/AutoTrust) | - | 0 |
| [Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives](https://arxiv.org/abs/2501.04003) | 2025-01 | ICCV 2025 | [GitHub](https://drive-bench.github.io/) | 8xA800 |28 |
| [Enhancing Large Vision Model in Street Scene Semantic Understanding through Leveraging Posterior Optimization Trajectory](https://arxiv.org/abs/2501.01710) | 2025-01 | arXiv | - | 2xRTX4090 |7 |
| [Enhancing Vision-Language Models with Scene Graphs for Traffic Accident Understanding](https://arxiv.org/abs/2407.05910) | 2025-01 | IAVVC 2024 | - | - |15 |
| [DriveLM: Driving with Graph Visual Question Answering](https://arxiv.org/abs/2312.14150) | 2025-01 | ECCV 2024 | [GitHub](https://github.com/OpenDriveLab/DriveLM) | 8xV100 | 473 |
| [Scenario Understanding of Traffic Scenes Through Large Visual Language Models](https://arxiv.org/pdf/2501.17131) | 2025-01 | WACV 2025 | - | A100 | 7 |
| [Application of Vision-Language Model to Pedestrian Behavior and Scene Understanding in Autonomous Driving](https://arxiv.org/abs/2501.06680) | 2025-01 | arXiv | - | - | 0 |
| [INSIGHT: Enhancing Autonomous Driving Safety through Vision-Language Models on Context-Aware Hazard Detection and Edge Case Evaluation](https://www.arxiv.org/abs/2502.00262) | 2025-02 | arXiv | - | A6000 |8 |
| [Evaluating Multimodal Vision-Language Model Prompting Strategies for Visual Question Answering in Road Scene Understanding](https://openaccess.thecvf.com/content/WACV2025W/LLVMAD/html/Keskar_Evaluating_Multimodal_Vision-Language_Model_Prompting_Strategies_for_Visual_Question_Answering_WACVW_2025_paper.html) | 2025-02 | WACV workshop 2025 | - | RTX4090 | 14 |
| [Vision-Integrated LLMs for Autonomous Driving Assistance: Human Performance Comparison and Trust Evaluation](https://arxiv.org/abs/2502.06843) | 2025-02 | arXiv | - | - | 0 |
| [NuGrounding: A Multi-View 3D Visual Grounding Framework in Autonomous Driving](https://arxiv.org/abs/2503.22436) | 2025-03 | arXiv | - | - | 4 |
| [Fine-Grained Evaluation of Large Vision-Language Models in Autonomous Driving](https://arxiv.org/abs/2503.21505) | 2025-03 | ICCV 2025 | [GitHub](https://github.com/Depth2World/VLADBench) | 8xV100 | 10 |
| [AutoDrive-QA- Automated Generation of Multiple-Choice Questions for Autonomous Driving Datasets Using Large Vision-Language Models](https://arxiv.org/abs/2503.15778) | 2025-03 | arXiv | [GitHub](https://github.com/Boshrakh/AutoDrive-QA) | 3xA6000 |4 |
| [DriveLMM-o1: A Step-by-Step Reasoning Dataset and Large Multimodal Model for Driving Scenario Understanding](https://arxiv.org/abs/2503.10621) | 2025-03 | arXiv | [GitHub](https://github.com/ayesha-ishaq/DriveLMM-o1) | 4xA6000 |20 |
| [ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation](https://www.arxiv.org/abs/2503.19755) | 2025-03 | arXiv | [GitHub](https://xiaomi-mlab.github.io/Orion/) | 32xA800 | 50 |
| [ChatBEV: A Visual Language Model that Understands BEV Maps](https://www.arxiv.org/abs/2503.13938) | 2025-03 | arXiv | - | - | 2 |
| [Robusto-1 Dataset: Comparing Humans and VLMs on real out-of-distribution Autonomous Driving VQA from Peru](https://www.arxiv.org/abs/2503.07587) | 2025-03 | arXiv | [GitHub](https://huggingface.co/datasets/Artificio/robusto-1/tree/main) | A100&API | 2 |
| [Evaluation of Safety Cognition Capability in Vision-Language Models for Autonomous Driving](https://arxiv.org/abs/2503.06497) | 2025-03 | arXiv | - | - | 0 |
| [ST-VLM: Kinematic Instruction Tuning for Spatio-Temporal Reasoning in Vision-Language Models](https://arxiv.org/abs/2503.19355) | 2025-03 | arXiv | [Project](https://ikodoh.github.io/ST-VLM) | - | 0 |
| [Retrieval-Based Interleaved Visual Chain-of-Thought in Real-World Driving Scenarios](https://www.arxiv.org/abs/2501.04671) | 2025-04 | arXiv | [GitHub](https://vita-epfl.github.io/DrivingVQA/) | 4xA100 | 6 |
| [ReasonDrive: Efficient Visual Question Answering for Autonomous Vehicles with Reasoning-Enhanced Small Vision-Language Models](https://arxiv.org/abs/2504.10757) | 2025-04 | arXiv | [GitHub](https://github.com/Zhourobotics/ReasonDrive) | - | 0 |
| [Vision Foundation Model Embedding-Based Semantic Anomaly Detection](https://arxiv.org/abs/2505.07998) | 2025-05 | ICRA 2025 Workshop | - | - | 3 |
| [OpenLKA: An Open Dataset of Lane Keeping Assist from Recent Car Models under Real-world Driving Conditions](https://arxiv.org/abs/2505.09092) | 2025-05 | arXiv | [GitHub](https://github.com/OpenLKA/OpenLKA) | - | 3 |
| [SURDS: Benchmarking Spatial Understanding and Reasoning in Driving Scenarios with Vision Language Models](https://arxiv.org/abs/2411.13112) | 2025-05 | NeurIPS 2025 | [GitHub](https://github.com/XiandaGuo/Drive-MLLM) | 8xA800 | 6 |
| [Extending Large Vision-Language Model for Diverse Interactive Tasks in Autonomous Driving](https://www.arxiv.org/abs/2505.08725) | 2025-05 | arXiv | [GitHub](https://github.com/zc-zhao/DriveMonkey) | 8xA800 | 5 |
| [Bridging Human Oversight and Black-box Driver Assistance: Vision-Language Models for Predictive Alerting in Lane Keeping Assist systems](https://arxiv.org/abs/2505.11535) | 2025-05 | arXiv | - | - | 3 |
| [AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving](https://arxiv.org/abs/2505.15298) | 2025-05 | EMNLP Findings 2025 | [GitHub](https://github.com/curryqka/AgentThink) | - | 0 |
| [FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving](https://arxiv.org/abs/2505.17685) | 2025-06 | NeurIPS 2025 | - | 8xA6000 | 65 |
| [Case-based Reasoning Augmented Large Language Model Framework for Decision Making in Realistic Safety-Critical Driving Scenarios](https://arxiv.org/abs/2506.20531) | 2025-06 | arXiv | - | API | 1 |
| [Structured Labeling Enables Faster Vision-Language Models for End-to-End Autonomous Driving](https://arxiv.org/abs/2506.05442) | 2025-06 | arXiv | - | 8xRTX4090 | 2 |
| [DriveAction: A Benchmark for Exploring Human-like Driving Decisions in VLA Models](https://arxiv.org/abs/2506.05667) | 2025-06 | arXiv | [Dataset](https://huggingface.co/datasets/LiAuto-DriveAction/drive-action) | - | 0 |
| [Box-QAymo: Box-Referring VQA Dataset for Autonomous Driving](https://www.arxiv.org/abs/2507.00525) | 2025-07 | arXiv | [GitHub](https://djamahl99.github.io/qaymo-pages/) | - | 0 |
| [DriveMRP: Enhancing Vision-Language Models with Synthetic Motion Data for Motion Risk Prediction](https://www.arxiv.org/abs/2507.02948) | 2025-07 | arXiv | [GitHub](https://github.com/hzy138/DriveMRP) | 8xH100 | 0 |
| [SafeDriveRAG: Towards Safe Autonomous Driving with Knowledge Graph-based Retrieval-Augmented Generation](https://arxiv.org/abs/2507.21585) | 2025-07 | ACMMM 2025 | [GitHub](https://github.com/Lumos0507/SafeDriveRAG) | - | 3 |
| [DRAMA-X: A Fine-grained Intent Prediction and Risk Reasoning Benchmark For Driving](https://www.arxiv.org/abs/2506.17590) | 2025-08 | arXiv | [GitHub](https://github.com/taco-group/DRAMA-X) | - | 2 |
| [NuRisk: A Visual Question Answering Dataset for Agent-Level Risk Assessment in Autonomous Driving](https://www.arxiv.org/abs/2509.25944) | 2025-09 | ICRA | - | API&RTX5090 |1 |
| [DriveAgent-R1: Advancing VLM-based Autonomous Driving with Active Perception and Hybrid Thinking](https://arxiv.org/abs/2507.20879) | 2025-09 | arXiv | - | API&8xH20 |1 |
| [Enhancing Vision-Language Models for Autonomous Driving through Task-Specific Prompting and Spatial Reasoning](https://arxiv.org/abs/2510.24152) | 2025-09 | IROS 2025 RoboSense| [GitHub](https://github.com/wuaodi/UCAS-CSU-phase2) | API |0 |
| [Are VLMs Ready for Lane Topology Awareness in Autonomous Driving?](https://arxiv.org/abs/2509.16654) | 2025-09 | arXiv | - | - | 0 |
| [More Than Meets the Eye? Uncovering the Reasoning-Planning Disconnect in Training Vision-Language Driving Models](https://arxiv.org/html/2510.04532v1?utm_source=chatgpt.com) | 2025-10 | arXiv | - | - |0 |
| [STRIDE-QA: Visual Question Answering Dataset for Spatiotemporal Reasoning in Urban Driving Scenes](https://www.arxiv.org/abs/2508.10427) | 2025-10 | AAAI 2026 | [GitHub](https://turingmotors.github.io/stride-qa/) | 16xH100 | 3 |
| [VLR-Driver: Large Vision-Language-Reasoning Models for Embodied Autonomous Driving](https://openaccess.thecvf.com/content/ICCV2025/html/Kong_VLR-Driver_Large_Vision-Language-Reasoning_Models_for_Embodied_Autonomous_Driving_ICCV_2025_paper.html) | 2025-10 | ICCV 2025 | - | - | 0 |
| [Hierarchical Reasoning with Vision-Language Models for Incident Reports from Dashcam Videos](https://arxiv.org/abs/2510.12190) | 2025-10 | arXiv | [GitHub](https://github.com/riron1206/kaggle-2COOOL-2nd-Place-Solution) | - | 0 |
| [WaymoQA: A Multi-View Visual Question Answering Dataset for Safety-Critical Reasoning in Autonomous Driving](https://www.arxiv.org/abs/2511.20022v1) | 2025-11 | arXiv | - | 8xA6000 | 0 |
| [A TOOL FOR BENCHMARKING LARGE LANGUAGE MODELS’ ROBUSTNESS IN ASSESSING THE REALISM OF DRIVING SCENARIOS](https://www.arxiv.org/abs/2511.04267v1) | 2025-11 | arXiv | - | API | 0 |
| [V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models](https://arxiv.org/abs/2408.09251) | 2025-11 | arXiv | - | RTX 4090 | 49 |
| [RoadBench: Benchmarking MLLMs on Fine-Grained Spatial Understanding and Reasoning under Urban Road Scenarios](https://arxiv.org/abs/2511.18011) | 2025-11 | arXiv | [GitHub](https://github.com/tsinghua-fib-lab/RoadBench) | - | 0 |
| [Is Your VLM for Autonomous Driving Safety-Ready? A Comprehensive Benchmark for Evaluating External and In-Cabin Risks](https://arxiv.org/abs/2511.14592) | 2025-11 | arXiv | - | - | 0 |
| [Toward Automatic Safe Driving Instruction: A Large-Scale Vision Language Model Approach](https://arxiv.org/abs/2511.23311) | 2025-11 | arXiv | - | - | 0 |
| [RoadSceneVQA: Benchmarking Visual Question Answering in Roadside Perception Systems for Intelligent Transportation System](https://www.arxiv.org/abs/2511.18286v2) | 2025-12 | arXiv | [GitHub](https://github.com/GuanRunwei/RS-VQA) | 4xA100 | 0 |
| [OmniDrive-R1: Reinforcement-driven Interleaved Multi-modal Chain-of-Thought for Trustworthy Vision-Language Autonomous Driving](https://arxiv.org/abs/2512.14044) | 2025-12 | arXiv | - | 16xA800 | 0 |
| [Counterfactual VLA: Self-Reflective Vision-Language-Action Model with Adaptive Reasoning](https://arxiv.org/abs/2512.24426) | 2025-12 | CVPR 2026 | - | - | 0 |
| [Semantic-Drive: Democratizing Long-Tail Data Curation via Open-Vocabulary Grounding and Neuro-Symbolic VLM Consensus](https://arxiv.org/abs/2512.12012) | 2025-12 | arXiv | [GitHub](https://github.com/AntonioAlgaida/Semantic-Drive) | - | 0 |
| [Vision-Based Natural Language Scene Understanding for Autonomous Driving: An Extended Dataset and a New Model for Traffic Scene Description Generation](https://arxiv.org/pdf/2601.14438) | 2026-01 | arXiv | - | A100 | 0 |
| [AutoDriDM: An Explainable Benchmark for Decision-Making of Vision-Language Models in Autonomous Driving](https://arxiv.org/pdf/2601.14702) | 2026-01 | arXiv | - | A100 | 0 |
| [ScenePilot-Bench: A Large-Scale Dataset and Benchmark for Evaluation of Vision-Language Models in Autonomous Driving ](https://arxiv.org/pdf/2601.19582) | 2026-01 | arXiv | [GitHub](https://github.com/yjwangtj/ScenePilot-Bench) | 4xA800 | 0 |
| [Vision and Language: Novel Representations and Artificial intelligence for Driving Scene Safety Assessment and Autonomous Vehicle Planning](https://arxiv.org/abs/2602.07680) | 2026-02 | arXiv | - | - | 0 |
| [DRIV-EX: Counterfactual Explanations for Driving LLMs](https://arxiv.org/pdf/2603.00696) | 2026-02 | arXiv | - | 20xA100 | 0 |
| [DriveCombo: Benchmarking Compositional Traffic Rule Reasoning in Autonomous Driving](https://arxiv.org/abs/2603.01637) | 2026-03 | arXiv | - | - | 0 |
| [DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving](https://arxiv.org/abs/2603.00919) | 2026-03 | arXiv | - | 8xH100 | 0 |
| [Probing Visual Concepts in Lightweight Vision-Language Models for Automated Driving](https://arxiv.org/abs/2603.06054) | 2026-03 | arXiv | [GitHub](https://github.com/D2ICE-Automotive-Research/probing_visual_concepts_in_traffic_scenes) | - | 0 |
| [BEVLM: Distilling Semantic Knowledge from LLMs into Bird’s-Eye View Representations](https://arxiv.org/abs/2603.06576) | 2026-03 | arXiv | - | 8xH100 | 0 |
| [Perception-Aware Multimodal Spatial Reasoning from Monocular Images](https://arxiv.org/abs/2603.06985) | 2026-03 | arXiv | - | 8xH100 | 0 |
| [Comparative Analysis of Patch Attack on VLM-Based Autonomous Driving Architectures](https://arxiv.org/abs/2603.08897) | 2026-03 | arXiv | IV 2025 | - | 0 |
| [More than the Sum: Panorama-Language Models for Adverse Omni-Scenes](https://arxiv.org/html/2603.09573v1) | 2026-03 | CVPR2026 | - | [https://github.com/InSAI-Lab/PanoVQA](https://github.com/InSAI-Lab/PanoVQA) | 0 |
| [Probing the Reliability of Driving VLMs: From Inconsistent Responses to Grounded Temporal Reasoning](https://arxiv.org/abs/2603.09512) | 2026-03 | arXiv | - | 4xA100 | 0 |
| [Are Video Reasoning Models Ready to Go Outside?](https://arxiv.org/abs/2603.10652) | 2026-03 | arXiv | [GitHub](https://robust-video-reason.github.io/) | 4xA100 | 0 |
| [WorldVLM: Combining World Model Forecasting and Vision-Language Reasoning](https://arxiv.org/abs/2603.14497) | 2026-03 | arXiv | - | 4xA40 | 0 |
| [VLM-AutoDrive: Post-Training Vision-Language Models for Safety-Critical Autonomous Driving Events](https://arxiv.org/abs/2603.18178) | 2026-03 | arXiv | - | 32xH100 | 0 |
| [KLDrive: Fine-Grained 3D Scene Reasoning for Autonomous Driving based on Knowledge Graph](https://arxiv.org/abs/2603.21029) | 2026-03 | arXiv | - | A6000 | 0 |
| [DRIVINGVQA: A Dataset for Interleaved Visual Chain-of-Thought in Real-World Driving Scenarios](https://aclanthology.org/2026.findings-eacl.173.pdf) | 2026-03 | ECAL 2026 | [GitHub](https://vita-epfl.github.io/DrivingVQA/) | 4xA100 | 0 |
| [3DCity-LLM: Empowering Multi-modality Large Language Models for 3D City-scale Perception and Understanding](https://arxiv.org/abs/2603.23447) | 2026-03 | arXiv | [GitHub](https://github.com/SYSU-3DSTAILab/3D-City-LLM) | A100 | 0 |
| [Collision-Aware Vision-Language Learning for End-to-End Driving with Multimodal Infraction Datasets](https://arxiv.org/abs/2603.25946) | 2026-03 | arXiv | - | - | 0 |
| [Dual-Stage LLM Framework for Scenario-Centric Semantic Interpretation in Driving Assistance](https://arxiv.org/abs/2603.27536) | 2026-03 | arXiv | - | - | 0 |
| [A Semantic Observer Layer for Autonomous Vehicles: Pre-Deployment Feasibility Study of VLMs for Low-Latency Anomaly Detection](https://arxiv.org/abs/2603.28888) | 2026-03 | arXiv | - | 5090 | 0 |
| [How Well Do Vision-Language Models Understand Sequential Driving Scenes? A Sensitivity Study (VENUSS)](https://arxiv.org/abs/2604.06750) | 2026-04 | arXiv | - | API | 0 |
| [BATON: A Multimodal Benchmark for Bidirectional Automation Transition Observation in Naturalistic Driving](https://arxiv.org/abs/2604.07263) | 2026-04 | arXiv | - | API | 0 |
| [CrashSight: A Phase-Aware, Infrastructure-Centric Video Benchmark for Traffic Crash Scene Understanding and Reasoning](https://arxiv.org/abs/2604.08457) | 2026-04 | arXiv | - | 8xA100 | 0 |
| [VAGNet: Vision-based Accident Anticipation with Global Features](https://arxiv.org/abs/2604.09305) | 2026-04 | arXiv | - | RTX4090 | 0 |
| [SignReasoner: Compositional Reasoning for Complex Traffic Sign Understanding via Functional Structure Units](https://arxiv.org/abs/2604.10436) | 2026-04 | arXiv | - | 4xH100 | 0 |
| [CCTVBench: Contrastive Consistency Traffic VideoQA Benchmark for Multimodal LLMs](https://arxiv.org/abs/2604.20460) | 2026-04 | arXiv | - | API | 0 |
| [Towards Safe Mobility: A Unified Transportation Foundation Model enabled by Open-Ended Vision-Language Dataset (UniVLT / LTD)](https://arxiv.org/abs/2604.22260) | 2026-04 | arXiv | - | 8xH100 | 0 |
| [EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving](https://arxiv.org/abs/2604.22851) | 2026-04 | arXiv | - | API | 0 |
| [Zoom In, Reason Out: Efficient Far-field Anomaly Detection in Expressway Surveillance Videos via Focused VLM Reasoning Guided by Bayesian Inference (VIBES)](https://arxiv.org/abs/2604.23724) | 2026-04 | arXiv | - | RTX4090 | 0 |
| [VLM-VPI: A Vision-Language Reasoning Framework for Improving Automated Vehicle-Pedestrian Interactions](https://arxiv.org/abs/2604.23934) | 2026-04 | arXiv | - | API | 0 |
| [HiDrive: A Closed-Loop Benchmark for High-Level Autonomous Driving](https://arxiv.org/abs/2605.09972) | 2026-05 | arXiv | [GitHub](https://github.com/VDIGPKU/HiDrive) | - | 0 |
| [Bench2Drive-VL: Benchmarks for Closed-Loop Autonomous Driving with Vision-Language Models](https://arxiv.org/abs/2604.01259) | 2026-04 | arXiv | [GitHub](https://github.com/Thinklab-SJTU/Bench2Drive-VL) | - | 0 |
| [Bridging Structure and Language: Graph-Based Visual Reasoning for Autonomous Road Understanding](https://arxiv.org/abs/2605.20942) | 2026-05 | arXiv | - | - | 0 |
| [C-CoT: Counterfactual Chain-of-Thought with Vision-Language Models for Safe Autonomous Driving](https://arxiv.org/abs/2605.10744) | 2026-05 | arXiv | - | - | 0 |
| [D2-V2X: Depth-Driven Cooperative V2X Reasoning for Autonomous Driving](https://arxiv.org/abs/2605.24098) | 2026-05 | arXiv | [GitHub](https://github.com/KevinRichard1/D2-V2X) | - | 0 |
| [DRIVESPATIAL: A Benchmark for Spatiotemporal Intelligence in VLMs for Autonomous Driving](https://arxiv.org/abs/2605.23176) | 2026-05 | NeurIPS 2026 | [Project](https://uark-aicv.github.io/DriveSpatial/) | - | 0 |
| [PEDESTRIANQA: A Benchmark for Vision-Language Models on Pedestrian Intention and Trajectory Prediction](https://arxiv.org/abs/2605.24562) | 2026-05 | arXiv | [GitHub](https://github.com/botmahn/PedestrianQA) | - | 0 |
| [ReasonBreak: Probing Vulnerabilities in Reasoning-Enabled Vision-Language-Action Models for Autonomous Driving](https://arxiv.org/abs/2605.29114) | 2026-05 | arXiv | - | - | 0 |
| [Two-Pass Zero-Shot Temporal-Spatial Grounding of Rare Traffic Events in Surveillance Video](https://arxiv.org/abs/2605.01512) | 2026-05 | arXiv | - | - | 0 |
| [nuReasoning: A Reasoning-Centric Dataset and Benchmark for Long-Tail Autonomous Driving](https://arxiv.org/abs/2605.31572) | 2026-05 | arXiv | [Project](https://nureasoning.github.io/) | - | 0 |
| [Vision-Language Work Zone Intelligence for Safety-Critical Speed Regulation of Mixed-Autonomy Vehicles in Dynamic Environments](https://arxiv.org/abs/2606.08860) | 2026-06 | arXiv | [GitHub](https://github.com/Mi3-Lab/workzone) | - | 0 |
| [Multi-Stage VLM Pipeline for Zero-Shot Traffic Accident Understanding](https://arxiv.org/abs/2605.29325) | 2026-05 | arXiv | [GitHub](https://github.com/fuumin621/cvpr2026-accident-1st-place-solution) | - | 0 |
| [DriveJudge: Rethinking Autonomous Driving Evaluation with Vision-Language Models](https://arxiv.org/abs/2606.17362) | 2026-06 | arXiv | - | - | 0 |
| [GeoDrive-Bench: Benchmarking Region-Specific Multimodal Reasoning in Autonomous Driving](https://arxiv.org/abs/2606.02774) | 2026-06 | arXiv | [GitHub](https://github.com/gray311/CulturalDrive-Bench) | - | 0 |
| [Metadata-Aware Multi-Prompt Reasoning for Zero-Shot Accident Understanding](https://arxiv.org/abs/2606.12047) | 2026-06 | arXiv | - | - | 0 |
| [Robusto-2: Benchmarking Humans & VLMs for Autonomous Driving in Lima & New York City](https://arxiv.org/abs/2606.20980) | 2026-06 | arXiv | - | - | 0 |
| [AUTOPILOT VQA: Benchmarking Vision-Language Models for Incident-Centric Dashcam Understanding](https://arxiv.org/abs/2607.08745) | 2026-07 | arXiv | - | - | 0 |
| [DispatchRAG: Grounding Emergency Dispatch Decisions in Real-World Protocols from Traffic Accident Video](https://arxiv.org/abs/2607.23132) | 2026-07 | arXiv | - | - | 0 |
| [Reasoning to Regulate: Chain-of-Thought for Traffic Rule Understanding](https://arxiv.org/abs/2607.24199) | 2026-07 | arXiv | - | - | 0 |
</details>

## 🌟 Multimodal Large Language Models for Autonomous Driving

<details open>
<summary>Scenario Generation (MLLM)</summary>

| Paper | Date | Venue | Code | Hardware |  Citation |
|:------|:-----|:------|:-----|:---------|:---------|
| [Realistic Corner Case Generation for Autonomous Vehicles with Multimodal Large Language Model](https://arxiv.org/pdf/2412.00243) | 2024-11 | Tsinghua Science and Technology | - |  API&RTX3090 |12 |
| [LMM-enhanced Safety-Critical Scenario Generation for Autonomous Driving System Testing From Non-Accident Traffic Videos](https://arxiv.org/pdf/2406.10857) | 2025-01 | arXiv | [GitHub](https://anonymous.4open.science/r/CRISER/README.md) | RTX2080 | 6 |
| [Multi-modal Traffic Scenario Generation for Autonomous Driving System Testing](https://arxiv.org/abs/2505.14881) | 2025-06 | FSE 2025 | - |  API |1 |
| [Talk2Traffic: Interactive and Editable Traffic Scenario Generation for Autonomous Driving with Multimodal Large Language Model](https://openaccess.thecvf.com/content/CVPR2025W/WDFM-AD/html/Sheng_Talk2Traffic_Interactive_and_Editable_Traffic_Scenario_Generation_for_Autonomous_Driving_CVPRW_2025_paper.html) | 2025-06 | CVPR 2025 WDFM-AD  |  [GitHub](https://zihaosheng.github.io/Talk2Traffic/) | - | 3 |

| [OmniTraffic: A Controllable Generation Pipeline and Benchmark for Spatio-Temporal Traffic Reasoning](https://arxiv.org/abs/2606.15749) | 2026-06 | arXiv | - | - | 0 |
</details>

<details open>
<summary>Scenario Analysis (MLLM)</summary>

| Paper | Date | Venue | Code | Hardware | Citation |
|:------|:-----|:------|:-----|:---------|:---------|
| [DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model](https://arxiv.org/abs/2310.01412) | 2023-10 | IEEE Robotics and Automation Letters 2024 | [GitHub](https://tonyxuqaq.github.io/projects/DriveGPT4/) | - | 615 |
| [Dolphins: Multimodal Language Model for Driving](https://arxiv.org/abs/2312.00438) | 2023-12 | ECCV 2024 | [GitHub](https://vlm-driver.github.io/) | 4xA100 |139 |
| [AccidentGPT: Accident analysis and prevention from V2X Environmental Perception with Multi-modal Large Model](https://arxiv.org/abs/2312.13156) | 2023-12 | IV 2024 | [GitHub](https://deepaccident.github.io/) | - |45 |
| [Lidar-llm: Exploring the potential of large language models for 3d lidar understanding](https://arxiv.org/abs/2312.14074) | 2023-12 | AAAI 2025 | [GitHub](https://sites.google.com/view/lidar-llm) | A100 |125 |
| [LingoQA: Visual Question Answering for Autonomous Driving](https://arxiv.org/abs/2312.14115) | 2023-12 | ECCV 2024 | [GitHub](https://github.com/wayveai/LingoQA) | 8xA100 |143 |
| [Holistic Autonomous Driving Understanding by Bird's-Eye-View Injected Multi-Modal Large Models](https://arxiv.org/abs/2401.00988) | 2024-01 | CVPR 2024 | [GitHub](https://github.com/xmed-lab/NuInstruct) |- | 106 |
| [MAPLM: A Real-World Large-Scale Vision-Language Benchmark for Map and Traffic Scene Understanding](https://openaccess.thecvf.com/content/CVPR2024/papers/Cao_MAPLM_A_Real-World_Large-Scale_Vision-Language_Benchmark_for_Map_and_Traffic_CVPR_2024_paper.pdf) | 2024-01 | CVPR 2024 | [GitHub](https://github.com/LLVM-AD/MAPLM) | 8xV100&2xA100 |65|
| [Probing Multimodal LLMs as World Models for Driving](https://arxiv.org/abs/2405.05956) | 2024-05 | arXiv | - | - | 0 |
| [WTS: A Pedestrian-Centric Traffic Video Dataset for Fine-Grained Spatial-Temporal Understanding](https://arxiv.org/abs/2407.15350) | 2024-06 | ECCV 2024 | [GitHub](https://woven-visionai.github.io/wts-dataset-homepage/) |- | 19 |
| [Semantic Understanding of Traffic Scenes with Large Vision Language Models](https://ieeexplore.ieee.org/document/10588373) | 2024-06 | IV 2024 | [GitHub](https://github.com/sandeshrjain/lvlm-scene) | API |27 |
| [VLAAD: Vision and Language Assistant for Autonomous Driving](https://ieeexplore.ieee.org/document/10495690) | 2024-06 | WACVW 2024 | [GitHub](https://ieeexplore.ieee.org/document/10495690) | - |52 |
| [InternDrive: A Multimodal Large Language Model for Autonomous Driving Scenario Understanding](https://dl.acm.org/doi/10.1145/3690931.3690982) | 2024-07 | AIAHPC 2024 | - | API |4 |
| [WOMD-Reasoning: A Large-Scale Dataset for Interaction Reasoning in Driving](https://arxiv.org/abs/2407.04281) | 2024-07 | ICML 2025 | [GitHub](https://github.com/yhli123/WOMD-Reasoning) | - | 0 |
| [Using Multimodal Large Language Models for Automated Detection of Traffic Safety Critical Events](https://www.mdpi.com/2624-8921/6/3/74) | 2024-09 | Vehicles 2024 MDPI | - | API |10 |
| [MLLM-SUL: Multimodal Large Language Model for Semantic Scene Understanding and Localization in Traffic Scenarios](https://arxiv.org/abs/2412.19406) | 2024-12 | arXiv | [GitHub](https://github.com/fjq-tongji/MLLM-SUL) | A800 |7 |
| [Application of Multimodal Large Language Models in Autonomous Driving](https://arxiv.org/abs/2412.16410) | 2024-12 | arXiv | - | - | 0 |
| [Distilling Multi-modal Large Language Models for Autonomous Driving](https://arxiv.org/abs/2501.09757?utm_source=chatgpt.com) | 2025-01 | CVPR 2025 | - | - |23 |
| [TB-Bench: Training and Testing Multi-Modal AI for Understanding Spatio-Temporal Traffic Behaviors from Dashcam Images/Videos](https://arxiv.org/abs/2501.05733) | 2025-01 | arXiv | [GitHub](https://github.com/TB-AD/TB-Bench-110k-250k) | - | 0 |
| [TUMTraffic-VideoQA: A Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes](https://arxiv.org/abs/2502.02449) | 2025-02 | ICML 2025 | [GitHub](https://arxiv.org/abs/2502.02449) | 4xA100 |13 |
| [ScVLM: Enhancing Vision-Language Model for Safety-Critical Event Understanding](https://openaccess.thecvf.com/content/WACV2025W/LLVMAD/html/Shi_ScVLM_Enhancing_Vision-Language_Model_for_Safety-Critical_Event_Understanding_WACVW_2025_paper.html) | 2025-02 | WACV Workshop 2025 | [GitHub](https://github.com/datadrivenwheels/ScVLM) | 2xA100 |8 |
| [Sce2DriveX: A Generalized MLLM Framework for Scene-to-Drive Learning](https://www.arxiv.org/abs/2502.14917) | 2025-02 | IEEE RAL | - | 8xL20 |18 |
| [A Framework for a Capability-driven Evaluation of Scenario Understanding for Multimodal Large Language Models in Autonomous Driving](https://arxiv.org/abs/2503.11400) | 2025-03 | arXiv | - | - |1 |
| [HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving](https://arxiv.org/abs/2309.05186) | 2025-03 | International Journal of Computer Vision | - | 8xV100 |68 |
| [NuPlanQA: A Large-Scale Dataset and Benchmark for Multi-View Driving Scene Understanding in Multi-Modal Large Language Models](https://arxiv.org/abs/2503.12772) | 2025-03 | ICCV 2025 | [GitHub](https://github.com/sungyeonparkk/NuPlanQA)| - |8|
| [Tracking Meets Large Multimodal Models for Driving Scenario Understanding](https://arxiv.org/abs/2503.14498) | 2025-03 | arXiv | [GitHub](https://github.com/mbzuai-oryx/TrackingMeetsLMM) | 2xA6000 |4 |
| [SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment](https://arxiv.org/abs/2503.09594) | 2025-03 | CVPR 2025 | [GitHub](https://github.com/RenzKa/simlingo) | 8xA100 |34 |
| [V3LMA: Visual 3D-enhanced Language Model for Autonomous Driving](https://arxiv.org/abs/2505.00156) | 2025-04 | CVPR DriveX 2025 | [GitHub](https://github.com/jalu1870/V3LMA-Visual-3D-enhanced-Language-Model-for-Autonomous-Driving) | H100 |3 |
| [Are Vision LLMs Road-Ready? A Comprehensive Benchmark for Safety-Critical Driving Video Understanding](https://arxiv.org/abs/2504.14526) | 2025-04 | arXiv | [GitHub](https://github.com/tong-zeng/DVBench) | - |7 |
| [V2V-LLM: Vehicle-to-Vehicle Cooperative Autonomous Driving with Multi-Modal Large Language Models](https://arxiv.org/abs/2502.09980) | 2025-04 | arXiv | [GitHub](https://eddyhkchiu.github.io/v2vllm.github.io/) | 8xA100 |22 |
| [ALN-P3: Unified Language Alignment for Perception, Prediction, and Planning in Autonomous Driving](https://arxiv.org/abs/2505.15158?utm_source=chatgpt.com) | 2025-05 | arXiv | - | 2xH200 |3 |
| [DriveSOTIF: Advancing SOTIF Through Multimodal Large Language Models](https://arxiv.org/abs/2505.07084) | 2025-05 | arXiv | [GitHub](https://github.com/s95huang/DriveSOTIF) | - | 0 |
| [X-Driver: Explainable Autonomous Driving with Vision-Language Models](https://arxiv.org/abs/2505.05098?utm_source=chatgpt.com) | 2025-06 | arXiv | - | - |5 |
| [STSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving](https://arxiv.org/abs/2506.06218) | 2025-06 | NeurIPS 2025 | [GitHub](https://lrp-ivc.github.io/STSBench/) | - |2 |
| [SafePLUG: Empowering Multimodal LLMs with Pixel-Level Insight and Temporal Grounding for Traffic Accident Understanding](https://arxiv.org/abs/2508.06763) | 2025-08 | arXiv | - | 8xA100 |2 |
| [RoboTron-Drive: All-in-One Large Multimodal Model for Autonomous Driving](https://arxiv.org/abs/2412.07689v5) | 2025-08 | ICCV2025 | [GitHub](https://github.com/zhijian11/RoboTron-Drive) | 32xA100 |9 |
| [Passing the Driving Knowledge Test (DriveQA)](https://arxiv.org/abs/2508.21824) | 2025-08 | ICCV 2025 | - | - | 0 |
| [EMMA: End-to-End Multimodal Model for Autonomous Driving](https://arxiv.org/abs/2410.23262) | 2025-09 | TMLR | - | - |161 |
| [Investigating Traffic Accident Detection Using Multimodal Large Language Models](https://arxiv.org/abs/2509.19096) | 2025-09 | IAVVC 2025 | - | - |1 |
| [AccidentBench: Benchmarking Multimodal Understanding and Reasoning in Vehicle Accidents and Beyond](https://arxiv.org/abs/2509.26636) | 2025-09 | arXiv | [GitHub](https://github.com/SafeRL-Lab/AccidentBench) | - | 0 |
| [Addressing Corner Cases in Autonomous Driving: A World Model-based Approach with Mixture of Experts and LLMs](https://www.sciencedirect.com/science/article/abs/pii/S0968090X25004607) | 2025-10 | Transportation Research Part C: Emerging Technologies | - | 4090 |0 |
| [BeLLA: End-to-End Birds Eye View Large Language Assistant for Autonomous Driving](https://www.arxiv.org/abs/2512.06096v1) | 2025-12 | arXiv | - | 4xH100 |0 |
| [MMDrive: Interactive Scene Understanding Beyond Vision with Multi-representational Fusion](https://arxiv.org/abs/2512.13177) | 2025-12 | arXiv | - | - | 0 |
| [Wild-Drive: Off-Road Scene Captioning and Path Planning via Robust Multi-modal Routing and Efficient Large Language Model](https://arxiv.org/pdf/2603.00694) | 2026-02 | arXiv | - | 4090 |0 |
| [DriveXQA: Cross-modal Visual Question Answering for Adverse Driving Scene Understanding](https://arxiv.org/abs/2603.11380) | 2026-03 | arXiv | [GitHub](https://github.com/jtjmd/DRIVEXQA) | 4xA100 |0 |
| [Interpretable Traffic Responsibility from Dashcam Video via Legal Multi-Agent Reasoning](https://arxiv.org/abs/2603.17930) | 2026-03 | arXiv | - | - |0 |
| [ExpressMind: A Multimodal Pretrained Large Language Model for Expressway Operation](https://arxiv.org/abs/2603.16495) | 2026-03 | arXiv | [GitHub](https://wanderhee.github.io/ExpressMind/) | 8xH20 |0 |
| [AITP: Traffic Accident Responsibility Allocation via Multimodal Large Language Models](https://arxiv.org/abs/2604.20878) | 2026-04 | CVPR 2026 Findings | - | 8xA100 | 0 |
| [XEmbodied: A Foundation Model with Enhanced Geometric and Physical Cues for Large-Scale Embodied Environments](https://arxiv.org/abs/2604.18484) | 2026-04 | arXiv | - | - | 0 |
| [V2X-QA: A Comprehensive Reasoning Dataset and Benchmark for MLLMs in Autonomous Driving Across Ego, Infrastructure, and Cooperative Views](https://arxiv.org/abs/2604.02710) | 2026-04 | arXiv | [GitHub](https://github.com/junwei0001/V2X-QA) | - | 0 |
| [DriveSafe: A Framework for Risk Detection and Safety Suggestions in Driving Scenarios](https://arxiv.org/abs/2605.16892) | 2026-05 | arXiv | - | - | 0 |
| [Enhancing Multimodal Large Language Models for Safety-Critical Driving Video Analysis](https://arxiv.org/abs/2605.22185) | 2026-05 | arXiv | - | - | 0 |
| [GuardAD: Safeguarding Autonomous Driving MLLMs via Markovian Safety Logic](https://arxiv.org/abs/2605.10386) | 2026-05 | arXiv | - | - | 0 |
| [Where Does the Answer Come From? Benchmarking View-Level Visual Evidence Identification in Multi-View MLLMs for Autonomous Driving](https://arxiv.org/abs/2606.09644) | 2026-06 | arXiv | - | - | 0 |
| [OmniSpace: Efficient Geometry Awareness for Autonomous Vehicles MLLMs](https://arxiv.org/abs/2606.22617) | 2026-06 | arXiv | - | - | 0 |
| [Beyond Scene Priors: Fine-Grained Traffic Scene Reasoning with Benchmarking and Query-Guided Small-Object Focus](https://arxiv.org/abs/2607.04149) | 2026-07 | arXiv | - | - | 0 |
| [D3VL: Understanding Driving Scenes from 3D Time Series Data and Video with Language Models](https://arxiv.org/abs/2607.19528) | 2026-07 | arXiv | [Project](https://automotivesafety-lvlm.github.io) | - | 0 |
</details>

## 🌟 Diffusion Models for Autonomous Driving

<details open>
<summary>Scenario Generation (Diffusion Models)</summary>

| Paper | Date | Venue | Code | Hardware | Citation |
|:------|:-----|:------|:-----|:---------|:---------|
| [Guided Conditional Diffusion for Controllable Traffic Simulation](https://aiasd.github.io/ctg.github.io/) | 2022-10 | ICRA 2023 | [GitHub](https://github.com/NVlabs/CTG) | - |223 |
| [Generating Driving Scenes with Diffusion](https://arxiv.org/abs/2305.18452) | 2023-05 | ICRA Workshop | - | - |25 |
| [DiffScene: Guided Diffusion Models for Safety-Critical Scenario Generation](https://openreview.net/forum?id=hclEbdHida) | 2023-06 | AdvML-Frontiers 2023 | - | RTX3090 |76 |
| [BEVControl: Accurately Controlling Street-view Elements with Multi-perspective Consistency via BEV Sketch Layout](https://arxiv.org/abs/2308.01661) | 2023-09 | arXiv | - | - |45 |
| [DriveSceneGen: Generating Diverse and Realistic Driving Scenarios From Scratch](https://arxiv.org/abs/2309.14685) | 2023-09 | IEEE Robotics and Automation Letters 2024 | [GitHub](https://github.com/SS47816/DriveSceneGen) | 4xV100 |22 |
| [MagicDrive: Street View Generation with Diverse 3D Geometry Control](https://arxiv.org/abs/2310.02601) | 2023-10 | ICLR 2024 | [GitHub](https://github.com/cure-lab/MagicDrive) | V100 |219 |
| [DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion model](https://arxiv.org/abs/2310.07771) | 2023-10 | ECCV 2024 | [GitHub](https://github.com/shalfun/DrivingDiffusion)| 8xA100 |80 |
| [Language-guided traffic simulation via scene-level diffusion](https://research.nvidia.com/labs/avg/publication/zhong.rempe.etal.corl23/) | 2023-11 | CoRL 2023 | - | RTX 3090 |121 |
| [Scenario Diffusion: Controllable Driving Scenario Generation With Diffusion](https://neurips.cc/virtual/2023/poster/72611) | 2023-11 | NeurIPS 2023 | - | 2xA6000 |64 |
| [Panacea: Panoramic and Controllable Video Generation for Autonomous Driving](https://arxiv.org/abs/2311.16813) | 2023-11 | CVPR 2024 | [GitHub](https://github.com/wenyuqing/panacea) | - |127 |
| [SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/03157-supp.pdf) | 2023-12 | ECCV 2024 | [GitHub](https://github.com/jxmmy7777/safe-sim) | 4xA6000 |28 |
| [Text2Street: Controllable Text-to-image Generation for Street Views](https://arxiv.org/abs/2402.04504) | 2024-02 | ICPR 2024 | - | - |12 |
| [ChatTraffic: Text-to-Traffic Generation via Diffusion Model](https://arxiv.org/abs/2311.16203) | 2024-02 | IEEE Transactions on Intelligent Transportation Systems | [GitHub](https://github.com/ChyaZhang/ChatTraffic) | RTX4090 |19 |
| [GEODIFFUSION: Text-Prompted Geometric Control for Object Detection Data Generation](https://arxiv.org/pdf/2306.04607) | 2024-02 | LCLR 2024 | [GitHub](https://kaichen1998.github.io/projects/geodiffusion/) | - |56 |
| [GenDDS: Generating Diverse Driving Video Scenarios with Prompt-to-Video Generative Model](https://arxiv.org/abs/2408.15868) | 2024-04 | ITSC 2024 | - | RTX3090Ti |6 |
| [Versatile Behavior Diffusion for Generalized Traffic Agent Simulation](https://arxiv.org/abs/2404.02524) | 2024-04 | RSS 2024 | [GitHub](https://github.com/SafeRoboticsLab/VBD) | 8xL40 |13 |
| [SceneControl: Diffusion for Controllable Traffic Scene Generation](https://waabi.ai/scenecontrol/) | 2024-05 | ICRA 2024 | - | - |33 |
| [SLEDGE: Synthesizing Driving Environments with Generative Models and Rule-Based Traffic](https://arxiv.org/abs/2403.17933) | 2024-07 | ECCV 2024 | [GitHub](https://github.com/autonomousvision/sledge) | - |24 |
| [DrivingGen: Efficient Safety-Critical Driving Video Generation with Latent Diffusion Models](https://ieeexplore.ieee.org/document/10688119) | 2024-07 | ICME 2024 | - | - |9 |
| [Controllable Traffic Simulation through LLM-Guided Hierarchical Chain-of-Thought Reasoning](https://arxiv.org/abs/2409.15135) | 2024-09 | IROS 2025 | - | - |7 |
| [AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion](https://ieeexplore.ieee.org/abstract/document/10802408) | 2024-10 | IROS 2024 | - | 4xRTX3090 |21 |
| [Data-driven Diffusion Models for Enhancing Safety in Autonomous Vehicle Traffic Simulations](https://arxiv.org/abs/2410.04809) | 2024-10 | arXiv | - | - |4 |
| [DiffRoad: Realistic and Diverse Road Scenario Generation for Autonomous Vehicle Testing](https://arxiv.org/abs/2411.09451) | 2024-11 | arXiv | - | - |7 |
| [SceneDiffuser: Efficient and Controllable Driving Simulation Initialization and Rollout](https://arxiv.org/pdf/2412.12129) | 2024-12 | NeurIPS 2024 | - |- |48 |
| [Direct Preference Optimization-Enhanced Multi-Guided Diffusion Model for Traffic Scenario Generation](https://arxiv.org/abs/2502.12178) | 2025-02 | arXiv | - | - |2|
| [Causal Composition Diffusion Model for Closed-loop Traffic Generation](https://arxiv.org/abs/2412.17920) | 2025-02 | CVPR 2025 | [GitHub](https://github.com/HenryLHH/CCDiff) | 4xV100 |11 |
| [Rolling Ahead Diffusion for Traffic Scene Simulation](https://www.arxiv.org/abs/2502.09587) | 2025-02 | AAAI 2025 Workshop| - | V100 |2 |
| [AVD2: Accident Video Diffusion for Accident Video Description](https://arxiv.org/pdf/2502.14801) | 2025-03 | ICRA 2025 | [GitHub](https://github.com/An-Answer-tree/AVD2) | - |16 |
| [DualDiff+: Dual-Branch Diffusion for High-Fidelity Video Generation with Reward Guidance](https://arxiv.org/abs/2503.03689) | 2025-03 | arXiv | [GitHub](https://github.com/yangzhaojason/DualDiff) | 8xA800 |4 |
| [Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments](https://arxiv.org/abs/2503.22496) | 2025-03 | CVPR 2025 | [GitHub](https://github.com/princeton-computational-imaging/scenario-dreamer) | 8xA100 |16 |
| [DriveGen: Towards Infinite Diverse Traffic Scenarios with Large Models](https://arxiv.org/abs/2504.19614) | 2025-03 | arXiv | - | - |4 |
| [DiVE: Efficient Multi-View Driving Scenes Generation Based on Video Diffusion Transformer](https://arxiv.org/abs/2504.00000) | 2025-04 | arXiv | - | 8xA800 |4 |
| [Decoupled Diffusion Sparks Adaptive Scene Generation](https://arxiv.org/abs/2504.10485) | 2025-04 | ICCV 2025 | [GitHub](https://github.com/OpenDriveLab/Nexus) | A100 |10 |
| [DualDiff: Dual-branch Diffusion Model for Autonomous Driving with Semantic Fusion](https://arxiv.org/abs/2505.01857) | 2025-05 | ICRA 2025 | [GitHub](https://github.com/yangzhaojason/DualDiff) | - |3 |
| [LD-Scene: LLM-Guided Diffusion for Controllable Generation of Adversarial Safety-Critical Driving Scenarios](https://arxiv.org/abs/2505.11247) | 2025-05 | arXiv | - | RTX4090 |7 |
| [Dual-Conditioned Temporal Diffusion Modeling for Driving Scene Generation](https://ieeexplore.ieee.org/document/11128056) | 2025-05 | ICAR 2025 | [GitHub](https://github.com/PeteBai/DcTDM) | V100 |1 |
| [Diffusion Models for Safety Validation of Autonomous Driving Systems](https://www.arxiv.org/abs/2506.08459) | 2025-06 | arXiv | - | GTX1080Ti |1 |
| [Diffusion-Based Generation and Imputation of Driving Scenarios from Limited Vehicle CAN Data](https://arxiv.org/abs/2509.12375) | 2025-09 | ITSC 2025 | - | A100 |0 |
| [Path Diffuser: Diffusion Model for Data-Driven Traffic Simulator](https://arxiv.org/pdf/2509.24995) | 2025-09 | - | [GitHub](https://github.com/CL2-UWaterloo/PathDiffuser/) | - |0 |
| [3D: Boosting Feed-Forward Driving Scene Generation with Efficient Video Diffusion](https://arxiv.org/abs/2510.15264) | 2025-10 | NeurIPS 2025 Workshop | - | H20 |3 |
| [VLM as Strategist: Adaptive Generation of Safety-critical Testing Scenarios via Guided Diffusion](https://arxiv.org/abs/2512.02844) | 2025-12 | arXiv | - | 8x4090 |0 |
| [FAR-Drive: Frame-AutoRegressive Video Generation in Closed-Loop Autonomous Driving](https://arxiv.org/abs/2603.14938) | 2026-03 | arXiv | - | - |0 |
| [Controllable Latent Diffusion for Traffic Simulation](https://arxiv.org/abs/2503.11771) | 2026-03 | arXiv | [GitHub](https://github.com/RoboSafe-Lab/BLM) | - |0 |
| [ScenarioControl: Vision-Language Controllable Vectorized Latent Scenario Generation](https://arxiv.org/abs/2604.17147) | 2026-04 | arXiv | [Project](https://light.princeton.edu/ScenarioControl) | - | 0 |
| [DriveCtrl: Conditioned Sim-to-Real Driving Video Generation](https://arxiv.org/abs/2605.15116) | 2026-05 | arXiv | - | L40 | 0 |
| [AnyScene: Towards Highly Controllable Driving Scene Generation at Anywhere and Beyond](https://arxiv.org/abs/2605.26113) | 2026-05 | arXiv | [Project](https://mind-omni.github.io/) | 8xA100 | 0 |
| [SafeGen: Goal-Conditioned Video Diffusion of Safety-Critical Scenarios for VLM-Based Autonomous Driving](https://arxiv.org/abs/2607.19701) | 2026-07 | arXiv | [GitHub](https://github.com/JoFrc/SafeGen) | - | 0 |
</details>

<details open>
<summary>Scenario Analysis (Diffusion Models)</summary>

| Paper | Date | Venue | Code | Hardware |  Citation |
|:------|:-----|:------|:-----|:---------|:---------|
| [AVD2: Accident Video Diffusion for Accident Video Description](https://arxiv.org/pdf/2502.14801) | 2025-03 | ICRA 2025 | [GitHub](https://github.com/An-Answer-tree/AVD2) | - |16 |

</details>

## 🌟 World Models for Autonomous Driving

<details open>
<summary>World Models for Autonomous Driving</summary>

| Paper | Date | Venue | Code | Hardware | Citation |
|:------|:-----|:------|:-----|:---------|:---------|
| [DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving](https://arxiv.org/abs/2309.09777) | 2023-09 | ECCV 2024 | [GitHub](https://github.com/JeffWang987/DriveDreamer) | A800 | 376 |
| [GAIA-1: A Generative World Model for Autonomous Driving](https://arxiv.org/abs/2309.17080) | 2023-09 | arXiv Wayve | - | 32xA100 | 428 |
| [TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction](https://arxiv.org/abs/2303.04116) | 2023-09 | ICRA 2023 | [GitHub](https://github.com/zhejz/TrafficBots) | 6xRTX2080Ti |70 |
| [Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion](https://arxiv.org/abs/2311.01017) | 2023-11 | ICLR 2024 | - | - |102 |
| [MUVO: A Multimodal Generative World Model for Autonomous Driving with Geometric Representations](https://arxiv.org/abs/2311.11762) | 2023-11 | IV 2025 | - | - |4 |
| [Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving](https://arxiv.org/abs/2311.17918) | 2023-11 | CVPR 2024 | [GitHub](https://github.com/BraveGroup/Drive-WM) | A40 |280 |
| [Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability](https://arxiv.org/abs/2405.17398) | 2024-03 | NeurIPS 2024 | [GitHub](https://github.com/OpenDriveLab/Vista) | 8xA100 |181 |
| [DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation](https://arxiv.org/abs/2403.06845) | 2024-05 | AAAI 2025 | [GitHub](https://github.com/f1yfisher/DriveDreamer2) | A800 |162 |
| [UniScene: Multi-Camera Unified Pre-training via 3D Scene Reconstruction for Autonomous Driving](https://arxiv.org/abs/2305.18829) | 2024-08 | RAL 2024 | [GitHub](https://github.com/chaytonmin/UniScene) | 8xA40 |3 |
| [WoVoGen: World Volume-aware Diffusion for Controllable Multi-camera Driving Scene Generation](https://arxiv.org/abs/2312.02934) | 2024-08 | ECCV 2024 | [GitHub](https://github.com/fudan-zvg/WoVoGen) | 8xA6000 |84 |
| [Panacea+: Panoramic and Controllable Video Generation for Autonomous Driving](https://arxiv.org/abs/2408.07605) | 2024-08 | CVPR 2024 | [GitHub](https://github.com/wenyuqing/panacea) | - |127 |
| [DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving](https://arxiv.org/abs/2408.00415) | 2024-08 | arXiv | [GitHub](https://github.com/PJLab-ADG/DriveArena) | 8xA100 |58 |
| [DriveDreamer4D: World Models Are Effective Data Machines for 4D Driving Scene Representation](https://arxiv.org/abs/2410.13571) | 2024-11 | CVPR 2025 | [GitHub](https://github.com/GigaAI-research/DriveDreamer4D) | H20 |66 |
| [ReconDreamer: Crafting World Models for Driving Scene Reconstruction via Online Restoration](https://arxiv.org/abs/2411.19548) | 2024-11 | CVPR 2025 | [GitHub](https://github.com/GigaAI-research/ReconDreamer) | - |55 |
| [MagicDrive3D: Controllable 3D Generation for Any-View Rendering in Street Scenes](https://arxiv.org/abs/2405.14475) | 2024-11 | arXiv | [GitHub](https://gaoruiyuan.com/magicdrive3d/) | - |56 |
| [MagicDrive-V2: High-Resolution Long Video Generation for Autonomous Driving with Adaptive Control](https://arxiv.org/abs/2411.13807) | 2024-11 | ICCV 2025 | [GitHub](https://gaoruiyuan.com/magicdrive-v2/) | 8xA800 |18 |
| [ACT-Bench: Towards Action Controllable World Models for Autonomous Driving](https://arxiv.org/abs/2412.05337) | 2024-12 | arXiv | - | 8xH100 |7 |
| [GEM: A Generalizable Ego-Vision Multimodal World Model for Fine-Grained Ego-Motion, Object Dynamics, and Scene Composition Control](https://arxiv.org/abs/2412.11198) | 2024-12 | CVPR 2025 | [GitHub](https://github.com/vita-epfl/GEM) | H100 |37 |
| [DrivingWorld: Constructing World Model for Autonomous Driving via Video GPT](https://arxiv.org/abs/2412.19505) | 2024-12 | arXiv | [GitHub](https://github.com/YvanYin/DrivingWorld) | 32xRTX4090&64xA100 |41 |
| [Driving in the Occupancy World: Vision-Centric 4D Occupancy Forecasting and Planning via World Models for Autonomous Driving](https://arxiv.org/abs/2408.14197) | 2025-01 | AAAI 2025 | [GitHub](https://github.com/yuyang-cloud/Drive-OccWorld) | 8xA100 |25 |
| [MaskGWM: A Generalizable Driving World Model with Video Mask Reconstruction](https://arxiv.org/abs/2502.11663) | 2025-02 | arXiv | [GitHub](https://github.com/SenseTime-FVG/OpenDWM) | 32xA800 |20 |
| [Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning](https://arxiv.org/abs/2503.15558) | 2025-03 | arXiv | [GitHub](https://github.com/nvidia-cosmos/cosmos-reason1) | - |57 |
| [GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving](https://arxiv.org/abs/2503.20523) | 2025-03 | arXiv | - | 256xH100 |66 |
| [Other Vehicle Trajectories Are Also Needed: A Driving World Model Unifies Ego-Other Vehicle Trajectories in Video Latent Space](https://arxiv.org/abs/2503.09215) | 2025-03 | arXiv | - | 64xA800 |5 |
| [Seeing the Future, Perceiving the Future: A Unified Driving World Model for Future Generation and Perception](https://arxiv.org/abs/2503.13587) | 2025-03 | arXiv | [GitHub](https://github.com/dk-liang/UniFuture) | - |5 |
| [Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control](https://arxiv.org/abs/2503.14492) | 2025-04 | arXiv | [GitHub](https://github.com/nvidia-cosmos/cosmos-transfer1) | 1024xH100 |33 |
| [DriVerse: Navigation World Model for Driving Simulation via Multimodal Trajectory Prompting and Motion Alignment](https://arxiv.org/abs/2504.18576) | 2025-04 | ACMMM 2025 | [GitHub](https://github.com/shalfun/DriVerse) | - |6 |
| [OccSora: 4D Occupancy Generation Models as World Simulators for Autonomous Driving](https://arxiv.org/abs/2405.20337) | 2025-05 | arXiv | [GitHub](https://github.com/wzzheng/OccSora) | 8xA100 |64 |
| [PosePilot: Steering Camera Pose for Generative World Models with Self-supervised Depth](https://arxiv.org/abs/2505.01729) | 2025-05 | IROS 2025 | - | 8xA100 |2 |
| [ProphetDWM: A Driving World Model for Rolling Out Future Actions and Videos](https://arxiv.org/abs/2505.18650) | 2025-05 | arXiv | - | 4xA100 |4 |
| [ReSim: Reliable World Simulation for Autonomous Driving](https://arxiv.org/abs/2506.09981) | 2025-06 | arXiv | [GitHub]([https://opendrivelab.com/ReSim](https://opendrivelab.com/ReSim)) | 40xA100 |10 |
| [SceneDiffuser++: City-Scale Traffic Simulation via a Generative World Model](https://arxiv.org/abs/2412.12129) | 2025-06 | CVPR 2025 | - | - |6 |
| [Epona: Autoregressive Diffusion World Model for Autonomous Driving](https://arxiv.org/abs/2506.24113) | 2025-06 | ICCV 2025 | [GitHub](https://kevin-thu.github.io/Epona/) | 48xA100 |22 |
| [STAGE: A Stream-Centric Generative World Model for Long-Horizon Driving-Scene Simulation](https://www.arxiv.org/abs/2506.13138) | 2025-06 | IROS 2025 | - | - |3 |
| [DeepVerse: 4D Autoregressive Video Generation as a World Model](https://arxiv.org/abs/2506.01103) | 2025-06 | arXiv | [GitHub](https://github.com/SOTAMak1r/DeepVerse) | A100 | 11 |
| [World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving](https://arxiv.org/abs/2507.12762) | 2025-07 | Commun Eng | [GitHub](https://github.com/humanlabmembers/Anticipation-of-Traffic-Accident) | RTX4090 |2 |
| [HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene Understanding and Generation](https://arxiv.org/abs/2501.14729) | 2025-08 | ICCV 2025 | [GitHub](https://github.com/LMD0311/HERMES) | 32xH20 |27 |
| [Driving scenario generation and evaluation using a structured layer representation and foundational models](https://www.arxiv.org/abs/2511.01541v1) | 2025-11 |arXiv | [GitHub](https://github.com/Valgiz/5LMSG) | API |0 |
| [GaussianDWM: 3D Gaussian Driving World Model for Unified Scene Understanding and Multi-Modal Generation](https://arxiv.org/abs/2512.23180) | 2025-12 | CVPR 2026 | [GitHub](https://github.com/dtc111111/GaussianDWM) | 16xA100 |0 |
| [VistaGEN: Consistent Driving Video Generation with Fine-Grained Control Using Multiview Visual-Language Reasoning](https://arxiv.org/abs/2603.28353) | 2026-03 |arXiv | | |0 |
| [X-World: Controllable Ego-Centric Multi-Camera World Models for Scalable End-to-End Driving](https://arxiv.org/abs/2603.19979) | 2026-03 |arXiv | [GitHub](https://x-world-1.github.io/)| |0 |
| [Toward Physically Consistent Driving Video World Models under Challenging Trajectories](https://arxiv.org/abs/2603.24506) | 2026-03 |arXiv | [GitHub](https://wm-research.github.io/PhyGenesis/) | 48xH20 |0 |
| [DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving](https://arxiv.org/abs/2601.01528) | 2026-03 |ICLR2026 | [GitHub](https://drivinggen-bench.github.io/) | |0 |
| [Composing Driving Worlds through Disentangled Control for Adversarial Scenario Generation](https://arxiv.org/abs/2603.12864) | 2026-03 |arXiv | [GitHub](https://github.com/Yifever20002/CompoSIA) | 16xH100 |0 |
| [Bridging Scene Generation and Planning: Driving with World Model via Unifying Vision and Motion Representation](https://arxiv.org/pdf/2603.14948) | 2026-03 |arXiv | [GitHub](https://github.com/TabGuigui/WorldDrive) | 16xA100 |0 |
| [LMGenDrive: Bridging Multimodal Understanding and Generative World Modeling for End-to-End Driving](https://arxiv.org/abs/2604.08719) | 2026-04 | arXiv | - | - | 0 |
| [HERMES++: Toward a Unified Driving World Model for 3D Scene Understanding and Generation](https://arxiv.org/abs/2604.28196) | 2026-04 | arXiv | [GitHub](https://github.com/H-EmbodVis/HERMESV2) | - | 0 |
| [Is Your Driving World Model an All-Around Player? (WorldLens)](https://arxiv.org/abs/2605.10858) | 2026-05 | arXiv | - | - | 0 |
| [Xiaomi Auto World Model: A Joint World Model Integrating Reconstruction and Generation for Autonomous Driving](https://arxiv.org/abs/2605.18137) | 2026-05 | arXiv | [Project](https://JointWM.github.io) | - | 0 |
| [Diffusion Transformer World-Action Model for AV Scene Prediction](https://arxiv.org/abs/2606.12987) | 2026-06 | arXiv | [GitHub](https://github.com/dlcv-team/latent-world-models-av) | - | 0 |
| [ReactSim-Bench: Benchmarking Reactive Behavior World Model Simulation in Autonomous Driving](https://arxiv.org/abs/2606.14058) | 2026-06 | arXiv | [GitHub](https://github.com/Thinklab-SJTU/ReactSim-Bench) | - | 0 |
</details>

## 📊 Datasets Comparison

The following figure shows the usage distribution of different foundation model types across autonomous driving datasets:

<details open>
<summary>Datasets Comparison</summary>

| Dataset | Year | Img | View | Real | Lidar | Radar | Traj | 3D | 2D | Lane | Weather | Time | Region | Company |
|:--------|:-----|:----|:-----|:-----|:------|:------|:-----|:---|:---|:-----|:--------|:-----|:-------|:--------|
| [CamVid](https://service.tib.eu/ldmservice/dataset/camvid-dataset) | 2009 | RGB | FPV | ✔ | ✖️ | ✖️ | ✖️ | ✖️ | ✔ | ✔ | ✔ | D | U | - |
| [KITTI](https://www.cvlibs.net/datasets/kitti/) | 2013 | RGB/S | FPV | ✔ | ✔ | ✖️ | ✔ | ✔ | ✔ | ✔ | ✔ | D | U/R/H | - |
| [Cyclists](https://www.ifi-mec.tu-clausthal.de/ctv-dataset) | 2016 | RGB | FPV | ✔ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | D | U | - |
| [Cityscapes](https://www.cityscapes-dataset.com/login/) | 2016 | RGB/S | FPV | ✔ | ✖️ | ✖️ | ✖️ | ✔ | ✔ | ✔ | ✖️ | D | U | - |
| [SYNTHIA](https://service.tib.eu/ldmservice/dataset/bibtex/synthia) | 2016 | RGB | FPV | ✖️ | ✖️ | ✖️ | ✖️ | ✔ | ✔ | ✔ | ✔ | D/N | U | - |
| [Campus](https://paperswithcode.com/dataset/campus-shelf) | 2016 | RGB | BEV | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | D | C | - |
| [RobotCar](https://universe.roboflow.com/robotcar-lnnmb/robotcar-kj2cb) | 2016 | RGB | FPV | ✔ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | D/N | U | - |
| [Mapillary](https://www.kaggle.com/c/mapillary-vistas-detection-challenge/data) | 2017 | RGB | FPV | ✔ | ✖️ | ✖️ | ✖️ | ✖️ | ✔ | ✔ | ✔ | D/N | U | - |
| [P.F.B.](https://scispace.com/pdf/brno-urban-dataset-the-new-data-for-self-driving-agents-and-3songw9bsn.pdf) | 2017 | RGB | FPV | ✔ | ✖️ | ✖️ | ✖️ | ✖️ | ✔ | ✔ | ✔ | D/N | U | - |
| [BDD100K](https://datasetninja.com/bdd100k) | 2018 | RGB | FPV | ✔ | ✖️ | ✖️ | ✖️ | ✔ | ✔ | ✔ | ✔ | D | U/H | - |
| [HighD](https://levelxdata.com/highd-dataset/) | 2018 | RGB | BEV | ✔ | ✖️ | ✖️ | ✖️ | ✖️ | ✔ | ✔ | ✖️ | D | H | - |
| [Udacity](https://www.kaggle.com/datasets/evilspirit05/cocococo-dataset) | 2018 | RGB | FPV | ✔ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | D | U | - |
| [KAIST](https://msc.kaist.ac.kr/bbs/board.php?bo_table=CAG&amp;wr_id=25) | 2018 | RGB/S | FPV | ✔ | ✔ | ✖️ | ✖️ | ✖️ | ✔ | ✔ | ✔ | D/N | U | - |
| [Argoverse](https://docs.ultralytics.com/datasets/detect/argoverse/) | 2019 | RGB/S | FPV | ✔ | ✔ | ✖️ | ✖️ | ✖️ | ✔ | ✔ | ✔ | D/N | U | - |
| [TRAF](https://tum-traffic-dataset.github.io/tumtraf-v2x/) | 2019 | RGB | FPV | ✔ | ✖️ | ✖️ | ✖️ | ✖️ | ✔ | ✔ | ✔ | D | U | - |
| [ApolloScape](https://service.tib.eu/ldmservice/dataset/305aef79-4191-4717-bb6a-496fa1f5ac4c) | 2019 | RGB/S | FPV | ✔ | ✖️ | ✖️ | ✖️ | ✔ | ✔ | ✔ | ✔ | D | U | - |
| [ACFR](https://datasetninja.com/acfr-multifruit-2016) | 2019 | RGB | BEV | ✔ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | D | RA | - |
| [H3D](https://paperswithcode.com/dataset/h3d) | 2019 | RGB | FPV | ✔ | ✖️ | ✖️ | ✖️ | ✖️ | ✔ | ✔ | ✔ | D | U | - |
| [INTERACTION](https://interaction-dataset.com/) | 2019 | RGB | BEV | ✔ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | D | I/RA | - |
| [Comma2k19](https://github.com/commaai/comma2k19) | 2019 | RGB | FPV | ✔ | ✖️ | ✖️ | ✔ | ✔ | ✖️ | ✖️ | ✖️ | D/N | U/S/R/H | - |
| [InD](https://www.ind-dataset.com/) | 2020 | RGB | BEV | ✔ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | D | I | - |
| [RounD](https://levelxdata.com/round-dataset/) | 2020 | RGB | BEV | ✔ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | D | RA | - |
| [nuScenes](https://www.nuscenes.org/nuscenes) | 2020 | RGB | FPV | ✔ | ✔ | ✔ | ✖️ | ✔ | ✔ | ✔ | ✔ | D/N | U | - |
| [Lyft Level 5](https://hyper.ai/en/datasets/9036) | 2020 | RGB | FPV | ✔ | ✔ | ✔ | ✖️ | ✔ | ✔ | ✔ | ✔ | D/N | U/S | - |
| [Waymo Open](https://waymo.com/open/download/) | 2020 | RGB | FPV | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | D/N | U | - |
| [A*3D](https://hyper.ai/en/datasets/17161) | 2020 | RGB | FPV | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | D/N | U | - |
| [RobotCar Radar](https://oxford-robotics-institute.github.io/oord-dataset/) | 2020 | RGB | FPV | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | D/N | U | - |
| [Toronto3D](https://onedrive.live.com/?authkey=%21AKEpLxU5CWVW%2DPg&id=E9CE176726EB5C69%216398&cid=E9CE176726EB5C69&parId=root&parQt=sharedby&o=OneUp) | 2020 | RGB | BEV | ✔ | ✔ | ✖️ | ✔ | ✔ | ✖️ | ✔ | ✖️ | D/N | U | University of Waterloo |
| [A2D2](https://registry.opendata.aws/aev-a2d2/) | 2020 | RGB | FPV | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✖️ | ✔ | ✔ | D | U/H/S/R | Audi |
| [WADS](https://bitbucket.org/autonomymtu/dsor_filter) | 2020 | RGB | FPV | ✔ | ✔ | ✔ | ✔ | ✔ | ✖️ | ✖️ | ✔ | D/N | U/S/R | Michigan Technological University |
| [Argoverse 2](https://www.argoverse.org/av2.html) | 2021 | RGB/S | FPV | ✔ | ✔ | ✖️ | ✖️ | ✔ | ✔ | ✔ | ✔ | D/N | U | - |
| [PandaSet](https://scale.com/open-datasets/pandaset) | 2021 | RGB | FPV | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | D/N | U | - |
| [ONCE](https://www.once-for-auto-driving.com) | 2021 | RGB | FPV | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | D/N | U | - |
| [Leddar PixSet](https://leddartech.com/datasets/leddarpixset-download-form/) | 2021 | RGB | FPV | ✔ | ✔ | ✖️ | ✔ | ✔ | ✔ | ✖️ | ✔ | D/N | U/S/R | Leddar |
| [ZOD](https://zod.zenseact.com/) | 2022 | RGB | FPV | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | D/N | U/R/S/H | Zenseact |
| [IDD-3D](https://idd3d.github.io/) | 2022 | RGB | FPV | ✔ | ✔ | ✖️ | ✖️ | ✔ | ✔ | ✖️ | ✖️ | - | R | INAI |
| [CODA](https://paperswithcode.com/dataset/coda) | 2022 | RGB | FPV | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | D/N | U/S/R | Huawei |
| [SHIFT](https://vis.xyz/shift) | 2022 | RGB | FPV | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | D/N | U/S/R/H | ETH Zürich |
| [DeepAccident](https://arxiv.org/html/2304.01168v5) | 2023 | RGB/S | FPV/BEV | ✖️ | ✔ | ✖️ | ✖️ | ✔ | ✔ | ✔ | ✔ | D/N | U/S/R/H | HKU, Huawei, CARLA |
| [Dual_Radar](https://github.com/adept-thu/Dual-Radar) | 2023 | RGB | FPV | ✔ | ✔ | ✔ | ✔ | ✔ | ✖️ | ✔ | ✔ | D/N | U | Tsinghua University |
| [V2V4Real](https://mobility-lab.seas.ucla.edu/v2v4real/) | 2023 | RGB | FPV | ✔ | ✔ | ✖️ | ✔ | ✔ | ✖️ | ✔ | ✖️ | - | U/H/S | UCLA Mobility Lab |
| [SCaRL](https://fhr-ihs-sva.pages.fraunhofer.de/asp/scarl/) | 2024 | RGB/S | FPV/BEV | ✖️ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | D/N | U/S/R/H | Fraunhofer CARLA |
| [MARS](https://data.nasa.gov/dataset/ai4mars-a-dataset-for-terrain-aware-autonomous-driving-on-mars) | 2024 | RGB | FPV | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | D/N | U/S/H | NYU, MAY Mobility |
| [Scenes101](https://wayve.ai/science/wayvescenes101/) | 2024 | RGB | FPV | ✔ | ✖️ | ✖️ | ✔ | ✖️ | ✖️ | ✔ | ✔ | D/N | U/S/R/H | Wayve |
| TruckScenes | 2025 | RGB | FPV | ✔ | ✔ | ✔ | ✔ | ✔ | ✖️ | ✔ | ✔ | D/N | H/U | MAN |

*Notes: View: FPV=First-Person, BEV=Bird's-Eye; Time: D=Day, N=Night; Region: U=Urban, R=Rural, H=Highway, S=Suburban, C=Campus, I=Intersection, RA=Road Area; Img: RGB/S=RGB+Stereo*

</details open>

## 🎮 Simulators

The following figure shows the usage distribution of different foundation model types across autonomous driving simulators:

<details open>
<summary>Simulators</summary>

| Simulator | Year | Back-end | Open Source | Realistic Perception | Custom Scenario | Real World Map | Human Design Map | Python API | C++ API | ROS API | Company |
|:----------|:-----|:---------|:------------|:---------------------|:----------------|:---------------|:-----------------|:-----------|:--------|:--------|:--------|
| [TORCS](http://torcs.sourceforge.net/) | 2000 | None | ✔ | ✔ | ✔ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | - |
| [Webots](https://cyberbotics.com/) | 2004 | ODE | ✔ | ✔ | ✔ | ✔ | ✖️ | ✔ | ✔ | ✖️ | - |
| [CarRacing](https://www.gymlibrary.dev/environments/box2d/car_racing/) | 2017 | None | ✔ | ✖️ | ✖️ | ✖️ | ✔ | ✔ | ✖️ | ✖️ | - |
| [CARLA](http://carla.org/) | 2017 | UE4 | ✔ | ✔ | ✔ | ✖️ | ✔ | ✔ | ✔ | ✔ | - |
| [SimMobilityST](https://www.researchgate.net/publication/313289844_SimMobility_Short-term_An_Integrated_Microscopic_Mobility_Simulator) | 2017 | None | ✔ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | - |
| [GTA-V](https://github.com/aitorzip/DeepGTAV) | 2017 | RAGE | ✖️ | ✔ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | ✖️ | - |
| [highway-env](https://github.com/eleurent/highway-env) | 2018 | None | ✔ | ✖️ | ✔ | ✖️ | ✔ | ✔ | ✖️ | ✖️ | - |
| [Deepdrive](https://deepdrive.io/) | 2018 | UE4 | ✔ | ✔ | ✔ | ✖️ | ✔ | ✔ | ✔ | ✖️ | - |
| [esmini](https://cloe.readthedocs.io/en/latest/reference/plugins/esmini.html) | 2018 | Unity | ✔ | ✖️ | ✖️ | ✖️ | ✖️ | ✔ | ✖️ | ✖️ | - |
| [AutonoViSim](https://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w14/Best_AutonoVi-Sim_Autonomous_Vehicle_CVPR_2018_paper.pdf) | 2018 | PhysX | ✖️ | ✔ | ✔ | ✖️ | ✖️ | ✔ | ✖️ | ✖️ | - |
| [AirSim](https://microsoft.github.io/AirSim/) | 2018 | UE4 | ✔ | ✔ | ✔ | ✖️ | ✔ | ✔ | ✔ | ✖️ | - |
| [SUMO](https://www.eclipse.org/sumo/) | 2018 | None | ✔ | ✖️ | ✔ | ✔ | ✔ | ✖️ | ✔ | ✖️ | - |
| [Apollo](http://apollo.auto/) | 2018 | Unity | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✖️ | - |
| [Sim4CV](https://sim4cv.org/) | 2018 | UE4 | ✔ | ✔ | ✔ | ✖️ | ✔ | ✔ | ✖️ | ✖️ | - |
| [MATLAB](https://www.mathworks.com/products/automated-driving.html) | 2018 | MATLAB | ✖️ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | Mathworks |
| [Scenic](https://scenic-lang.org/) | 2019 | None | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✖️ | ✖️ | Toyota Research Institute, UC Berkeley |
| [SUMMIT](https://github.com/AdaCompNUS/summit) | 2020 | UE4 | ✔ | ✔ | ✔ | ✖️ | ✔ | ✔ | ✔ | ✖️ | - |
| [MultiCarRacing](https://github.com/ananya183/collaborative-multi-car-racing) | 2020 | None | ✔ | ✖️ | ✔ | ✖️ | ✔ | ✔ | ✖️ | ✖️ | - |
| [SMARTS](https://smarts.readthedocs.io/en/latest/) | 2020 | None | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✖️ | ✖️ | - |
| [LGSVL](https://www.lgsvlsimulator.com/) | 2020 | Unity | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | - |
| [CausalCity](https://arxiv.org/html/2306.03354v2) | 2020 | UE4 | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✖️ | ✖️ | - |
| [Vista](https://vista.csail.mit.edu/) | 2020 | None | ✔ | ✔ | ✔ | ✔ | ✖️ | ✔ | ✖️ | ✖️ | MIT |
| [MetaDrive](https://metadriverse.github.io/metadrive/) | 2021 | Panda3D | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✖️ | - |
| [L2R](https://github.com/learn-to-race/l2r) | 2021 | UE4 | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✖️ | - |
| [AutoDRIVE](https://autodrive-ecosystem.github.io/) | 2021 | Unity | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | - |
| [Nuplan](https://www.nuplan.org/) | 2021 | None | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✖️ | ✖️ | Motional |
| [AWSIM](https://autowarefoundation.github.io/AWSIM-Labs/) | 2021 | Unity | ✔ | ✔ | ✔ | ✔ | ✔ | ✖️ | ✖️ | ✔ | Autoware |
| [InterSim](https://tsinghua-mars-lab.github.io/InterSim/) | 2022 | None | ✔ | ✔ | ✔ | ✔ | ✖️ | ✔ | ✖️ | ✖️ | Tsinghua |
| [Nocturne](https://github.com/facebookresearch/nocturne) | 2022 | None | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✖️ | Facebook |
| [BeamNG.tech](https://beamng.tech/) | 2022 | Soft-body physics | ✖️ | ✔ | ✔ | ✖️ | ✔ | ✔ | ✖️ | ✔ | BeamNG GmbH |
| [Waymax](https://github.com/waymo-research/waymax) | 2023 | JAX | ✔ | ✔ | ✔ | ✖️ | ✔ | ✔ | ✖️ | ✖️ | Waymo |
| [UNISim](https://github.com/Sense-X/UNISim) | 2023 | None | ✖️ | ✔ | ✔ | ✔ | ✖️ | ✖️ | ✔ | ✖️ | Waabi |
| [TBSim](https://github.com/NVlabs/traffic-behavior-simulation) | 2023 | None | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✖️ | ✖️ | NVIDIA |
| [Nvidia DriveWorks](https://developer.nvidia.com/drive/driveworks) | 2024 | Nvidia GPU | ✖️ | ✔ | ✔ | ✔ | ✖️ | ✔ | ✔ | ✖️ | NVIDIA |

</details>

## 🏆 Foundation Model Benchmark Challenges (2022–2025)

<details open>
<summary>Benchmark Challenges</summary>

### Autonomous Driving

| Name                              | Host                    |
|-----------------------------------|-------------------------|
| [CARLA AD Challenge](https://leaderboard.carla.org/)                | CARLA                   |
| [DRL4Real](https://drl4real-workshop.github.io/)                          | ICCV                    |
| [Waymo Open Dataset Challenge](https://waymo.com/open/challenges/)      | Waymo / CVPR WAD       |
| [Argoverse 2: Scenario Mining](https://eval.ai/web/challenges/challenge-page/1857/overview)      | ArgoAI                  |
| [Roboflow-20VL](https://eval.ai/web/challenges/challenge-page/2459/overview)                     | Roboflow-VL / CVPR     |
| [AVA Challenge](https://research.google.com/ava/challenge.html)                     | AVA Challenge Team      |

### Other Fields Related to Generation and Analysis

| Name                              | Host                               |
|-----------------------------------|------------------------------------|
| [IGLU Challenge](https://iglu-challenge.net/)                    | NeurIPS / IGLU Team                |
| [LLM Efficiency Challenge](https://mlcommons.org/en/news/mlc-announces-neurips-2022-llm-efficiency-challenge-winners/)          | NeurIPS                            |
| [Trojan Detection](https://trojandetection.ai/)                  | NeurIPS / CAIS                     |
| [SMART-101](https://eval.ai/web/challenges/challenge-page/1681/overview)                         | CVPR                               |
| [NICE Challenge](https://nice.lgresearch.ai/)                    | CVPR / LG Research                 |
| [SyntaGen](https://syntagen-challenge.github.io/)                          | CVPR                               |
| [Habitat Challenge](https://aihabitat.org/challenge/)                 | CVPR / FAIR                        |
| [BIG-bench](https://github.com/google/BIG-bench)                         | Google Research                    |
| [BIG-bench Hard (BBH)](https://github.com/suzgunmirac/BIG-Bench-Hard)              | Google Research                    |
| [HELM](https://crfm.stanford.edu/helm/latest/)                              | Stanford CRFM                      |
| [MMBench](https://github.com/open-compass/MMBench)                           | OpenCompass                        |
| [MMMU](https://mmmu-benchmark.github.io/)                              | CVPR / U-Waterloo / OSU           |
| [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)              | VILA-Lab                           |
| [Text-to-Image Leaderboard](https://www.artificialanalysis.ai/leaderboards/text-to-image)         | Artificial Analysis                |
| [Ego4D](https://ego4d-data.org/)                             | FAIR                               |
| [VizWiz Grand Challenge](https://vizwiz.org/)            | CVPR VizWiz Workshop               |
| [MedFM](https://medfm.github.io/)                             | NeurIPS / Shanghai AI Laboratory   |
| [3D Scene Understanding](http://www.scan-net.org/)            | CVPR                               |

</details>

## 🔗 Useful Resources and Links

<details>
<summary>Common Tools and Frameworks</summary>

This section provides links to commonly used tools, frameworks, and resources for working with foundation models in autonomous driving.

### Model Repositories and Leaderboards

- [Hugging Face Models](https://huggingface.co/models) - Large collection of pre-trained foundation models
- [Hugging Face Vision-Language Models Leaderboard](https://huggingface.co/spaces/opencompass/open_vlm_leaderboard) - Benchmark and leaderboard for VLMs
- [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) - Comprehensive LLM benchmarking
- [Awesome LLM](https://github.com/Hannibal046/Awesome-LLM) - Curated list of Large Language Models resources

### Model Inference Frameworks

- [vLLM](https://github.com/vllm-project/vllm) - High-throughput and memory-efficient inference engine for LLMs
- [LMDeploy](https://github.com/InternLM/lmdeploy) - Toolkit for compressing, deploying, and serving LLMs
- [Ollama](https://ollama.ai/) - Run large language models locally
- [Text Generation Inference](https://github.com/huggingface/text-generation-inference) - Production-ready inference container by Hugging Face
- [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) - High-performance inference library by NVIDIA

### Training and Fine-tuning

- [Hugging Face Transformers](https://github.com/huggingface/transformers) - State-of-the-art ML for PyTorch, TensorFlow, and JAX
- [LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory) - Unified framework for fine-tuning LLMs
- [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) - Tool for fine-tuning large language models
- [PEFT](https://github.com/huggingface/peft) - Parameter-Efficient Fine-Tuning methods

</details>

## Contributing
We welcome contributions from the community! If you have research papers, tools, or resources to add, please create a pull request or open an issue.

## License
This repository is released under the [Apache 2.0 license](https://github.com/TUM-AVS/FM-AD-Survey/blob/main/LICENSE).

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