aiqbench analysis
HybridWorldSim advances autonomous driving research by combining neural reconstruction and generative modeling to solve view synthesis and geometric consistency issues in simulation. The release of the MIRROR dataset provides a critical benchmark for training end-to-end models under diverse, high-fidelity conditions. This work directly supports the development of more robust AI-driven vehicle control systems through improved synthetic data generation.
HybridWorldSim: A Scalable and Controllable High-fidelity Simulator for Autonomous Driving
arXiv:2511.22187v4 Announce Type: replace-cross Abstract: Realistic and controllable simulation is critical for advancing end-to-end autonomous driving, yet existing approaches often struggle to support novel view synthesis under large viewpoint changes or to ensure geometric consistency. We introduce HybridWorldSim, a hybrid simulation framework that integrates multi-traversal neural reconstruction for static backgrounds with generative modeling for dynamic agents. This unified design addresses key limitations of previous methods, enabling the creation of diverse and high-fidelity driving scenarios with reliable visual and spatial consistency. To facilitate robust benchmarking, we further release a new multi-traversal dataset MIRROR that captures a wide range of routes and environmental conditions across different cities. Extensive experiments demonstrate that HybridWorldSim surpasses previous state-of-the-art methods, providing a practical and scalable solution for high-fidelity simulation and a valuable resource for research and development in autonomous driving.
Abstract:Realistic and controllable simulation is critical for advancing end-to-end autonomous driving, yet existing approaches often struggle to support novel view synthesis under large viewpoint changes or to ensure geometric consistency. We introduce HybridWorldSim, a hybrid simulation framework that integrates multi-traversal neural reconstruction for static backgrounds with generative modeling for dynamic agents. This unified design addresses key limitations of previous methods, enabling the creation of diverse and high-fidelity driving scenarios with reliable visual and spatial consistency. To facilitate robust benchmarking, we further release a new multi-traversal dataset MIRROR that captures a wide range of routes and environmental conditions across different cities. Extensive experiments demonstrate that HybridWorldSim surpasses previous state-of-the-art methods, providing a practical and scalable solution for high-fidelity simulation and a valuable resource for research and development in autonomous driving.
Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
arXiv:2511.22187 [cs.CV]
(or arXiv:2511.22187v4 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2511.22187 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 226 words
