HybridWorldSim: A Scalable and Controllable High-fidelity Simulator for Autonomous Driving
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2606. 02956v1 Announce Type: cross Abstract: Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity.
arXiv:2603. 28963v2 Announce Type: replace-cross Abstract: Simulation with realistic traffic agents is essential for validating autonomous driving systems.
arXiv:2606. 15930v1 Announce Type: cross Abstract: Simulation is central to validating autonomous driving systems, yet current pipelines are limited by insufficient scenario diversity due to costly High Definition (HD) map creation.
HelloWorld is a 2B-parameter driving world model that learns from diverse video data to generate controllable driving scenarios. It uses ego pose, HD maps, and 3D boxes to produce coherent multi‑sensor outputs, including synchronized seven‑camera RGB and conditional LiDAR. The system is designed for efficient repeated inference and is evaluated on visual quality, control fidelity, cross‑view consistency, robustness, and inference speed.
DriveWorld3D is a framework that integrates temporal video learning, multi‑sensor measurement constraints, and offline generative simulation to build a unified 3D world model for autonomous driving. Its online component tracks road surfaces, volumetric occupancy, dynamic entities, and road relations while explicitly managing physical occupancy, observability, and evidence provenance. The offline component reconstructs scenes with neural radiance fields or 3D Gaussian Splatting and expands rare conditions using generative models, but limits generated content from being used as geometric ground truth.
arXiv:2606. 03159v1 Announce Type: cross Abstract: As autonomous vehicle capabilities advance, the safe evaluation of driving policies in long-tail scenarios remains a critical bottleneck.