The AV industry is hitting a "Data Wall." Brute force training on petabytes of data is hitting a dead end. The better the models get, the harder they are to improve. At https://t.co/F1BeFcSBqa, we bypassed this wall with Factored Embodied AI—achieving Zero-Shot autonomous

8/8: With Factored Embodied AI, we are moving from the era of Brute Force to the era of Data Efficiency and Scalable Simulation. Read the full technical deep dive here: https://t.co/PItnq1pbEF #AutonomousDriving #ComputerVision #Robotics #DeepLearning #HelmAI

7/8: There is a massive safety dividend, too. Unlike monolithic "Black Box" models, our architecture is interpretable by design. Transparency is built-in, allowing us to trace exactly why a decision was made. This is the bridge to ISO 26262 and SOTIF compliance—delivering the

6/8: To prove our model captures universal geometric priors, we took this perception stack and fine-tuned it for an Open-Pit Mine. It successfully identified drivable surfaces and obstacles in this alien environment. When you learn the signal rather than the noise, adaptation

5/8: We close the loop using World Models. By projecting "ghost trails" to anticipate the intent of pedestrians and vehicles, the system generates its own adversarial scenarios. This creates a data flywheel that improves the model without needing more real-world miles.

4/8: This allows us to train in Semantic Space. Because our perception engine converts the world into geometry, we skip the heavy lift of rendering photorealistic pixels. A simulated lane line is mathematically identical to a real one, allowing us to train on infinite

3/8: Think of a 16-year-old learning to drive. They don't need millions of miles to master the road because they already understand the physics and geometry of the world. We replicate this advantage using Geometric Reasoning. By extracting clean 3D structure first, we separate

2/8: The industry is racing toward monolithic "End-to-End" models. But there is a paradox: As models improve, the edge-case data required to solve the final 1% becomes exponentially rarer to find in the real world. The solution isn't more data—it's better architecture.

3/ By combining https://t.co/F1BeFcSBqa’s AI technologies with Honda’s engineering expertise, we’re building production-ready ADAS systems targeted for future EV and HEV models in North America and Japan around 2027. Read Honda’s full announcement 👉

2/ Our partnership began through Honda Xcelerator in 2019, followed by Honda’s initial investment in 2022 and a multi-year joint development agreement signed this July. This next phase deepens our collaboration to develop AI-powered mass market driver assistance technology

(4/4): This collaboration is an important step toward bringing next-gen ADAS and self-driving capabilities to mass-market consumer vehicles. 👉 Full announcement:

(3/4): Powered by Deep Teaching™ technology, our systems and foundation models are pre-trained on large-scale, diverse, multi-modal datasets. Additionally, they can be improved to meet any OEM’s specifications for safe, reliable, and scalable deployment.

(2/4): The partnership initially focuses on Advanced Driver Assistance Systems (ADAS) for production vehicles. https://t.co/F1BeFcSBqa will contribute: 🔹 Real-time AI software ( https://t.co/F1BeFcSBqa Vision, https://t.co/F1BeFcSBqa Driver) 🔹 Generative simulation models

Big news: https://t.co/F1BeFcSBqa has entered a multi-year joint development agreement with @Honda 🎉 Together, we’ll accelerate Honda’s next-gen self-driving capabilities, including its Navigate on Autopilot (NOA) platform. See thread for details 👇 #ADAS #AutonomousDriving

(4/4) Validated for mass production and fully compatible with the end-to-end https://t.co/F1BeFcSBqa Driver path planning stack, https://t.co/F1BeFcSBqa Vision enables reduced validation effort and increased interpretability, streamlining deployment of full-stack AI software.

(3/4) Modular by design, https://t.co/F1BeFcSBqa Vision is optimized for deployment on leading hardware platforms including Nvidia, Qualcomm, TI, and Ambarella.

(2/4) New features include bird’s-eye view (BEV) generation from multi-camera input, ISO 26262 ASIL-B(D) certified components, and an ASPICE Level 2 assessment. These enhancements support more accurate downstream planning and confirm the system’s readiness for integration into

(1/4) https://t.co/F1BeFcSBqa Vision builds on our surround-view architecture and is trained on real-world data using our proprietary Deep Teaching™ methodology. It uses camera input and scales across diverse geographies, road geometries, and traffic behaviors.

We’re announcing https://t.co/F1BeFcSBqa Vision: our enhanced, production-ready urban perception system for Level 3 autonomous driving. Our updated vision-first system now includes BEV fusion functionality, ISO 26262 ASIL-B(D) certification, ASPICE Level 2 assessment, and

3/ https://t.co/F1BeFcSBqa Driver is fully compatible with our production-grade vision perception stack, enabling a modular, interpretable, and scalable self-driving system across geographies, vehicle platforms, and driving conditions. Learn more: