Architecture comparison
Autonomous
Waymo’s production driver uses a modular six-layer stack (sensor processing, perception, world modeling, prediction, planning, control) integrated with HD maps, simulation, and a foundation-model-based end-to-end driving architecture that fuses camera, lidar, and radar into a unified world representation.[1][2][8][9][10] Training, evaluation, and large-scale data handling run on Google/Alphabet’s internal and Google Cloud infrastructure, with closed-source models and tooling.[1][4][5][8][10]
Autonomous
Wayve runs a proprietary end-to-end embodied driving stack on Microsoft Azure, using large vision-world models (AI Driver, GAIA-3) trained on petabyte-scale fleet and simulated data via Azure Kubernetes Service, Azure Machine Learning, Databricks, and GPU clusters, then deploys a compressed model on in-vehicle compute for production driving.[7][9][12] The architecture replaces modular perception/mapping/planning with a single neural policy and related foundation models (driving, simulation, scenario classification, language explanation), all controlled via internal MLOps pipelines on Azure.[1][4][9][12]