Orbis: Overcoming Challenges of Long-Horizon Prediction in Driving World Models

Understand

Existing world models for autonomous driving struggle with long-horizon generation and generalization to challenging scenarios.

• In this work, we develop a model using simple design choices, and without additional supervision or sensors, such as maps, depth, or multiple cameras.
• We show that our model yields state-of-the-art performance, despite having only 469M parameters and being trained on 280h of video data.
• It particularly stands out in difficult scenarios like turning maneuvers and urban traffic.