mlsanityData to 14 Oct 2026

DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch

arXiv 2309.14685 ↗First posted 26 Sep 2023cs.RO cs.CVcited by 27

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Abstract (arXiv)

Realistic and diverse traffic scenarios in large quantities are crucial for the development and validation of autonomous driving systems. However, owing to numerous difficulties in the data collection process and the reliance on intensive annotations, real-world datasets lack sufficient quantity and diversity to support the increasing demand for data. This work introduces DriveSceneGen, a data-driven driving scenario generation method that learns from the real-world driving dataset and generates entire dynamic driving scenarios from scratch. DriveSceneGen is able to generate novel driving scenarios that align with real-world data distributions with high fidelity and diversity. Experimental results on 5k generated scenarios highlight the generation quality, diversity, and scalability compared to real-world datasets. To the best of our knowledge, DriveSceneGen is the first method that generates novel driving scenarios involving both static map elements and dynamic traffic participants from scratch.

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). Builds on 23 of its 28 references that are in the map. First posted on arXiv 26 Sep 2023.

Builds on

23 of 28 references.

References · 23

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
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  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
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  5. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
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  8. 2022Human Motion Diffusion Modelcited by 1,080
  9. 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,061
  10. 2019Argoverse: 3D Tracking and Forecasting with Rich Mapscited by 732
  11. 2023Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecastingcited by 707
  12. 2021Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Datasetcited by 537
11 more
  1. 2022Is Conditional Generative Modeling all you need for Decision-Making?cited by 516
  2. 2021NuPlan: A closed-loop ML-based planning benchmark for autonomous vehiclescited by 493
  3. 2022DiffuSeq: Sequence to Sequence Text Generation with Diffusion Modelscited by 450
  4. 2023Synthetic Data from Diffusion Models Improves ImageNet Classificationcited by 327
  5. 2022MapTR: Structured Modeling and Learning for Online Vectorized HD Map Constructioncited by 314
  6. 2022Motion Transformer with Global Intention Localization and Local Movement Refinementcited by 270
  7. 2022TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarioscited by 106
  8. 2021SceneGen: Learning to Generate Realistic Traffic Scenescited by 84
  9. 2021SimNet: Learning Reactive Self-driving Simulations from Real-world Observationscited by 77
  10. 2023Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Constructioncited by 50
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 6; 2025: 11; 2026: 8.

27 papers.

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  1. 2024SceneDiffuser: Efficient and Controllable Driving Simulation Initialization and Rolloutcited by 48
  2. 2025Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysiscited by 38
  3. 2025Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environmentscited by 28
  4. 2024SLEDGE: Synthesizing Driving Environments with Generative Models and Rule-Based Trafficcited by 27
  5. 2024AutoVFX: Physically Realistic Video Editing from Natural Language Instructionscited by 27
  6. 2025Genesis: Multimodal Driving Scene Generation with Spatio-Temporal and Cross-Modal Consistencycited by 22
  7. 2025SceneDiffuser++: City-Scale Traffic Simulation via a Generative World Modelcited by 20
  8. 2023Applications of Large Scale Foundation Models for Autonomous Drivingcited by 14
  9. 2025Continuous Locomotive Crowd Behavior Generationcited by 7
  10. 2023A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspectivecited by 6
  11. 2024Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Plannercited by 6
  12. 2025Trends in Motion Prediction Toward Deployable and Generalizable Autonomy: A Revisit and Perspectivescited by 5
15 more
  1. 2025AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Frameworkcited by 5
  2. 2025Generative AI for Autonomous Driving: A Reviewcited by 3
  3. 2024ADM: Accelerated Diffusion Model via Estimated Priors for Robust Motion Prediction under Uncertaintiescited by 2
  4. 2024How Could Generative AI Support Compliance with the EU AI Act? A Review for Safe Automated Driving Perceptioncited by 2
  5. 2025SceneStreamer: Continuous Scenario Generation as Next Token Group Predictioncited by 2
  6. 2026VectorWorld: Efficient Streaming World Model via Diffusion Flow on Vector Graphscited by 2
  7. 2025PrITTI: Primitive-based Generation of Controllable and Editable 3D Semantic Urban Scenescited by 1
  8. 2026DiffPlace: Street View Generation via Place-Controllable Diffusion Model Enhancing Place Recognitioncited by 1
  9. 2026ControlMap: Controllable High-Definition Map Generation for Traffic Scenario Simulationcited by 1
  10. 2025Path Diffuser: Diffusion Model for Data-Driven Traffic Simulatorcited by 0
  11. 2026Artificial Intelligence for Modeling and Simulation of Mixed Automated and Human Trafficcited by 0
  12. 2026ScenarioControl: Vision-Language Controllable Vectorized Latent Scenario Generationcited by 0
  13. 2026From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testingcited by 0
  14. 2026End-to-end Conditional Diffusion for Realistic and Controllable Visual Traffic Scenario Generationcited by 0
  15. 2026ForcingBooster: Adaptive Patch-wise Denoising for Fast Autoregressive Video Diffusioncited by 0