Our AI models are built on innovations in deep neural networks (DNNs) and generative AI to power real-time perception and planning stacks for deployment, as well as generative models for large-scale training and validation.
Our models adapt to geographic variation, edge cases, and diverse driving conditions to enable a flexible, scalable path from ADAS (Level 2) to fully autonomous (Level 4) systems.
Deep Teaching™, developed by Helm.ai since 2016, is a highly efficient and scalable unsupervised learning method that combines large-scale data, deep learning, and applied mathematics. Our foundation models generalize effectively, resolve corner cases, and learn human-like driving behaviors.
Our models handle variations in:
Automakers can fine-tune our models to resolve rare corner cases quickly and tailor performance to specific regions, driving styles, or sensor configurations.
Our generative simulation models, GenSim-2, VidGen-2, and WorldGen-1, produce large volumes of realistic driving scenes for training and validation from text, image, and video input. Unlike traditional simulation, they can quickly generate an unlimited range of scene variations, regardless of the diversity or complexity of urban driving environments.
Explore Helm.ai’s AI software, foundation models, and AI-based development and validation tools.