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

Understand

Simulation with realistic traffic agents is essential for validating autonomous driving systems.

• Existing data-driven simulators learn agent behavior from higher-level abstractions such as 3D bounding boxes and polylines, inferred by upstream perception pipelines.
• These lossy abstractions discard sensory context that directly shapes agent behavior, limiting the distributional realism that simulation aims to reproduce.
• To address this limitation, we propose AutoWorld, a traffic simulation framework that grounds agent behavior in raw sensor observations through a self-supervised world model trained on LiDAR occupancy data.