HybridWorldSim: A Scalable and Controllable High-fidelity Simulator for Autonomous Driving

• URL: http://arxiv.org/abs/2511.22187v2
• Date: Mon, 01 Dec 2025 08:14:40 GMT
• Title: HybridWorldSim: A Scalable and Controllable High-fidelity Simulator for Autonomous Driving
• Authors: Qiang Li, Yingwenqi Jiang, Tuoxi Li, Duyu Chen, Xiang Feng, Yucheng Ao, Shangyue Liu, Xingchen Yu, Youcheng Cai, Yumeng Liu, Yuexin Ma, Xin Hu, Li Liu, Yu Zhang, Linkun Xu, Bingtao Gao, Xueyuan Wang, Shuchang Zhou, Xianming Liu, Ligang Liu,
• Abstract summary: HybridWorldSim is a hybrid simulation framework that integrates multi-traversal neural reconstruction for static backgrounds with generative modeling for dynamic agents.<n>We release a new multi-traversal dataset MIRROR that captures a wide range of routes and environmental conditions across different cities.
• Score: 59.55918581964678
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving [49.11389494068169]
  We present DrivingGen, the first comprehensive benchmark for generative driving world models.<n>DrivingGen combines a diverse evaluation dataset curated from both driving datasets and internet-scale video sources.<n>General models look better but break physics, while driving-specific ones capture motion realistically but lag in visual quality.
  arXiv  Detail & Related papers  (2026-01-04T13:36:21Z)
• MR-LDM -- The Merge-Reactive Longitudinal Decision Model: Game Theoretic Human Decision Modeling for Interactive Sim Agents [0.9883562565157391]
  We aim to improve the simulation of the highway merge scenario by targeting a game theoretic model for tactical decision-making.<n>We couple this with an underlying dynamics model to have a unified decision and dynamics model that can capture more realistic interactions.
  arXiv  Detail & Related papers  (2025-07-15T20:41:00Z)
• The Sound of Simulation: Learning Multimodal Sim-to-Real Robot Policies with Generative Audio [138.07247714782412]
  MultiGen is a framework that integrates large-scale generative models into traditional physics simulators.<n>We demonstrate effective zero-shot transfer to real-world pouring with novel containers and liquids.
  arXiv  Detail & Related papers  (2025-07-03T17:59:58Z)
• RealEngine: Simulating Autonomous Driving in Realistic Context [60.55873455475112]
  RealEngine is a novel driving simulation framework that holistically integrates 3D scene reconstruction and novel view synthesis techniques.<n>By leveraging real-world multi-modal sensor data, RealEngine reconstructs background scenes and foreground traffic participants separately, allowing for highly diverse and realistic traffic scenarios.<n>RealEngine supports three essential driving simulation categories: non-reactive simulation, safety testing, and multi-agent interaction.
  arXiv  Detail & Related papers  (2025-05-22T17:01:00Z)
• RIFT: Group-Relative RL Fine-Tuning for Realistic and Controllable Traffic Simulation [13.319344167881383]
  We introduce a dual-stage AV-centric simulation framework that conducts imitation learning pre-training in a data-driven simulator.<n>We then learn fine-tuning in a physics-based simulator to enhance style-level controllability.<n>In the fine-tuning stage, we propose RIFT, a novel group-relative RL fine-tuning strategy.
  arXiv  Detail & Related papers  (2025-05-06T09:12:37Z)
• A Survey of World Models for Autonomous Driving [55.520179689933904]
  Recent breakthroughs in autonomous driving have been propelled by advances in robust world modeling.<n>World models offer high-fidelity representations of the driving environment that integrate multi-sensor data, semantic cues, and temporal dynamics.<n>Future research must address key challenges in self-supervised representation learning, multimodal fusion, and advanced simulation.
  arXiv  Detail & Related papers  (2025-01-20T04:00:02Z)
• Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses [130.15554653948897]
  In vehicular mixed reality (MR) Metaverse, distance between physical and virtual entities can be overcome. Large-scale traffic and driving simulation via realistic data collection and fusion from the physical world is difficult and costly. We propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data in simulations.
  arXiv  Detail & Related papers  (2023-02-16T16:54:10Z)
• Cycle-Consistent World Models for Domain Independent Latent Imagination [0.0]
  High costs and risks make it hard to train autonomous cars in the real world. We propose a novel model-based reinforcement learning approach called Cycleconsistent World Models.
  arXiv  Detail & Related papers  (2021-10-02T13:55:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.