Helm.ai Driver is trained on large-scale real-world data using Helm.ai’s proprietary Deep Teaching™ methodology—an unsupervised learning approach that improves accuracy and robustness without manual labeling.
The model exhibits complex urban driving behaviors—including intersections, turns, obstacle avoidance, passing maneuvers, and response to vehicle cut-ins—that emerge naturally from end-to-end learning, without being explicitly programmed or tuned.
Helm.ai Driver continuously responds to dynamic environments, as demonstrated in closed-loop simulation using CARLA simulator. Simulated scenes are re-rendered with GenSim-2 to produce highly realistic camera outputs, enabling scalable development and validation.
Explore Helm.ai’s AI software, foundation models, and AI-based development and validation tools.
