AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models | Paper Citation Record

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This paper cites In: 2025 IEEE Intelligent Vehicles Symposium (IV).

AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: 2025 IEEE Intelligent Vehicles Symposium (IV)

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This paper cites CVPR Workshop on Autonomous Driving (WAD) (2025).

AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models CVPR Workshop on Autonomous Driving (WAD) (2025)

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This paper cites CVPR Workshop on Autonomous Driving (WAD) (2025).

AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models CVPR Workshop on Autonomous Driving (WAD) (2025)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models Com- puter22(6), 46–57 (2002)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: Proceedings of the IEEE/CVF international conference on computer vision (CVPR) (2021)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models Advances in Neural Information Processing Systems (NeurIPS) (2024)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: Deep Reinforcement Learning Work- shop NeurIPS 2022 (2022)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models Advances in Neural Information Process- ing Systems (NeurIPS) (2023)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: Inter- national Conference on Learning Representations (ICLR) (2026)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models Ad- vances in Neural Information Processing Systems35, 20703–20716 (2022)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models GAIA-1: A Generative World Model for Autonomous Driving

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This paper cites In: Proceedings of the AAAI Conference on Artificial Intelligence (2025).

AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: Proceedings of the AAAI Conference on Artificial Intelligence (2025)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: 2024 IEEE International Conference on Robotics and Automation (ICRA)

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This paper cites arXiv preprint arXiv:2404.02524 (2024) 16 M.

AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models arXiv preprint arXiv:2404.02524 (2024) 16 M

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models ADriver-I: A General World Model for Autonomous Driving

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition (2023)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models Advances in Neural Information Processing Systems (NeurIPS) (2024)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models 3D and 4D World Modeling: A Survey

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models Foun- dations and Trends®in Machine Learning (2012)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: European conference on computer vision

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: European Conference on Computer Vision

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: Conference on Robot Learning (CoRL) (2021)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: 2024 IEEE International Conference on Robotics and Automation (ICRA)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: European con- ference on computer vision

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models UniWorld: Autonomous Driving Pre-training via World Models

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models Advances in Neural Information Processing Systems (NeurIPS) (2023)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: Proceedings of the Computer Vision and Pattern Recognition Confer- ence (CVPR) (2025) AutoWorld 17

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models Scene Transformer: A unified architecture for predicting multiple agent trajectories

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models Advances in neural information pro- cessing systems (2022)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: International Conference on Learning Representations (ICLR) (2026)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models Ad- vances in Neural Information Processing Systems (2023)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models Advances in Neural Information Pro- cessing Systems (NeurIPS) (2025)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models Pourkeshavarz et al

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models BITS: Bi-level Imitation for Traffic Simulation

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: Proceedings of the AAAI Conference on Artificial Intelligence (2025)

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: European conference on computer vision

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Observation 7306b219-0c18-47f5-8292-5c962e50fcf9 · outbound

This paper cites GenAD: Generalized Predictive Model for Autonomous Driving.

AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models GenAD: Generalized Predictive Model for Autonomous Driving

Reference 59

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Observation 68d9de9b-f539-4bec-8893-61d7159728c3 · outbound

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: Conference on robot learning (CoRL) (2023)

Reference 60

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Observation 5fbdcd87-4075-4536-aa58-93b32adfd7c5 · outbound

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: 2023 IEEE international conference on robotics and automation (ICRA)

Reference 61

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models Advances in Neural Information Processing Systems (NeurIPS) (2024)

Reference 62

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Observation df147307-ba78-4d3e-a608-d8699f90179d · outbound

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: Proceedings of the IEEE/CVF conference on computer vision and pattern recog- nition (2023)

Reference 63

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Observation 51632dd3-c46e-46fa-a8ce-52908fb95f4b · outbound

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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models arXiv preprint arXiv:2405.03520 (2024)

Reference 64

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This paper cites In: 2025 IEEE International Conference on Robotics and Automation (ICRA).

AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models In: 2025 IEEE International Conference on Robotics and Automation (ICRA)

Reference 65

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