The Waymo World Model is a frontier generative AI model developed by Waymo for large-scale, hyper-realistic autonomous driving simulation. It is a critical component of Waymo's AI ecosystem, enhancing the demonstrability of safety by allowing the Waymo Driver to virtually navigate billions of miles, mastering complex and rare scenarios long before encountering them in the real world.

The core methodology of the Waymo World Model is built upon Google DeepMind's Genie 3, an advanced general-purpose world model capable of generating photorealistic and interactive 3D environments. This foundation provides the Waymo World Model with immense world knowledge, acquired through Genie 3's pre-training on an extremely large and diverse dataset of 2D videos. Unlike traditional autonomous driving simulation models that typically learn from scratch using limited on-road data, the Waymo World Model leverages this broad, pre-existing knowledge. Through specialized post-training, this vast 2D video knowledge is transferred and adapted into the 3D domain, specifically tailored to generate multi-modal outputs unique to Waymo's hardware suite, including both high-fidelity camera imagery and precise lidar data. This allows the model to generate virtually any scene, from routine daily driving to exceedingly rare, long-tail events, across multiple sensor modalities.

The model's architecture emphasizes strong simulation controllability, achieved through three primary mechanisms:

1. Driving Action Control: This enables a responsive simulator that adheres to specific driving inputs. It allows for the simulation of counterfactual "what if" scenarios, such as exploring alternative driving behaviors (e.g., a more confident maneuver versus yielding). Crucially, the fully learned Waymo World Model maintains realism and consistency even when simulating routes significantly different from original recorded drives, a capability where purely reconstructive simulation methods (e.g., based on 3D Gaussian Splats) often exhibit visual breakdowns due to missing observations. The generative nature ensures robustness beyond mere reconstruction.

2. Scene Layout Control: This mechanism provides fine-grained control over environmental elements. Engineers can customize road layouts, modify traffic signal states, and dictate the behavior of other road users. This allows for the creation of bespoke scenarios through selective placement of agents or the application of specific mutations to the road infrastructure.

3. Language Control: This is the most flexible control mechanism. It permits the adjustment of global scene attributes like time-of-day or weather conditions via natural language prompts. Furthermore, it enables the generation of entirely synthetic scenes, including complex long-tail scenarios not directly observed in real-world data.

The Waymo World Model generates multi-sensor outputs (camera and lidar data) that provide both visual details and precise depth information. This multi-modal realism is crucial for training and validating the Waymo Driver. The model's ability to simulate diverse and challenging scenarios—including extreme weather conditions (e.g., snow, tornados, floods, fires), rare and safety-critical events (e.g., reckless drivers, vehicle malfunctions, precariously loaded vehicles), and encounters with unusual objects (e.g., elephants, lions, T-Rex costumes, large tumbleweeds)—significantly expands the scope of virtual testing. Additionally, the model can convert generic dashcam or camera videos into Waymo-specific multi-modal simulations, grounding virtual environments in real-world footage for enhanced factuality.

For large-scale simulations, an efficient variant of the Waymo World Model allows for long simulation rollouts with a dramatic reduction in computational cost while maintaining high realism and fidelity. This scalable inference capability is vital for rigorous safety benchmarking, proactively preparing the Waymo Driver for an expansive range of complex challenges it might face in reality, thereby ensuring and improving road safety.