The mapless software stack delivers complex city driving capabilities with orders of magnitude reduction in data requirements—powering advanced Level 2+ and serving as the software brain for the transition to Level 3 and Level 4 autonomy.

Helm.ai, a leading provider of advanced AI software for autonomous driving and robotics automation, today announced a major capability expansion of Helm.ai Driver: a production-ready, vision-only software stack designed to scale seamlessly from advanced Level 2+ systems through Level 4 urban autonomy.

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Built on Helm.ai’s proprietary Factored Embodied AI architecture, the system delivers seamless, human-like driving in complex city traffic without reliance on high-definition (HD) maps or lidar sensors. Because the core foundation model is level-agnostic, it enables automotive OEMs to deploy high-end Level 2+ systems immediately, while utilizing the exact same software architecture to unlock certified Level 3 "eyes-off" and Level 4 fully autonomous capabilities as their hardware and regulatory roadmaps evolve.

To mark the announcement, the company released a demonstration video of Helm.ai Driver navigating the urban environment of Redwood City, California. The video showcases the system autonomously handling left and right turns at intersections, complex traffic light compliance, and dynamic interactions with other road users—all safely supervised by a safety driver in accordance with standard testing and validation protocols for production-intent autonomous systems.

Solving the "Data Wall" for Level 3 and Level 4 Certification

The automotive industry is currently hitting a "Data Wall"—the point where autonomous driving approaches require exponentially more rare and expensive real-world data to improve performance in edge-case scenarios. Additionally, even if such data were available, monolithic, pixel-to-control "end-to-end" models function as "black boxes" that lack the interpretability required for rigorous safety certification at Level 3 and beyond.

In contrast, Helm.ai Driver utilizes a Factored Embodied AI architecture that addresses data scarcity and interpretability simultaneously. This approach splits the autonomy problem into two distinct, interpretable layers: Perception and Policy. By solving Perception separately, the system converts raw sensor data into information-rich and highly structured semantic segmentation and 3D information. Helm.ai's end-to-end Policy model then takes this interpretable semantic geometry as its input—rather than raw pixels—to "reason" about road structure and traffic rules.

This factored approach unlocks massive training on internet-scale datasets and enables highly data-efficient training of the end-to-end Policy model to help break the “Data Wall.” Crucially, this structure provides the transparency critical for automotive OEMs, offering a clear, auditable software foundation capable of scaling from supervised Level 2+ to ISO 26262-certifiable Level 3 and Level 4 deployments.

"The industry has reached a tipping point where brute-force data collection is no longer commercially viable for high-end autonomy," said Vladislav Voroninski, CEO and founder of Helm.ai. "With Helm.ai Driver, we have fundamentally changed the unit economics of scalable autonomy. By delivering a vision-first system that powers advanced Level 2+ today, and serves as the software brain for the transition to Level 3 and Level 4 autonomy, we are providing OEMs with the only realistic path to deploying next-generation autonomy on mass-market compute platforms."

Orders of Magnitude Efficiency via Deep Teaching™ and Semantic Simulation

While traditional approaches typically require billions of dollars in capital expenditure and millions of miles of training data to achieve urban capability, Helm.ai Driver’s planner reached this level of maturity using only 1,000 hours of real-world driving data.

This breakthrough is powered by Deep Teaching™—Helm.ai’s proprietary unsupervised learning technique that enables neural networks to learn directly from massive amounts of easily available non-driving data, bypassing the need for costly human annotation on internet-scale vision datasets. Paired with semantic simulation, the system can train on practically infinite geometric scenarios without the computational overhead of rendering photorealistic pixels. By training the system on the "semantic geometry" of the world rather than raw pixels, Helm.ai bypasses the traditional cost and time barriers of autonomous development.

Generalization Across Geographies: The “Zero-Shot” Advantage

The true test of an autonomous system for mass-production vehicles is its ability to handle "unseen" environments without manual tuning or HD maps. To validate this, Helm.ai recently demonstrated the system’s generalization capability by deploying the software in Torrance, California (the Greater Los Angeles area).

Without any prior training on the area's specific streets, Helm.ai Driver was able to perform "zero-shot" autonomous steering. This ability to generalize across geographies ensures that Helm.ai’s OEM partners can scale Level 2+ through Level 4 features globally without the prohibitive cost of city-by-city data collection or geofencing.

About Helm.ai

Helm.ai develops AI software for ADAS, autonomous driving, and robotics automation. Founded in 2016, the company delivers full-stack driving software for on-car deployment and simulation tools powered by Deep Teaching™ and generative AI. Helm.ai partners with global automakers on production-bound programs.

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