Dataset Description: PhysicalAI WorldModel Synthetic Autonomous Driving Scenarios is a large scale synthetic video dataset of autonomous driving scenes generated with NVIDIA's internal Omniverse simulation platform. Each clip is a temporally consistent multi camera surround capture of one ego vehicle and surrounding traffic participants, paired with per camera VLM captions. The dataset is designed to fill gaps in real world driving data along two axes: (1) targeted long tail coverage of safety critical scenarios — emergency vehicle interactions, nudging around parked obstacles, cut ins from adjacent lanes, weather degraded visibility, and pedestrian crossings with non standard trajectories — authored declaratively from natural language prompts via a Scenario Agent; and (2) environment variation, where each authored scenario is expanded into deterministic permutations over time of day, cloud coverage, visibility, road material, and vehicle and pedestrian asset choices, so the same underlying interaction is observed under varied environment conditions. As a fully synthetic dataset, PhysicalAI WorldModel Synthetic Autonomous Driving Scenarios exhibits a sim to real appearance gap rela…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy