Helm.ai, a leader in AI software for advanced driver-assistance systems (ADAS), autonomous vehicles, and robotics, has unveiled Helm.ai Driver, a cutting-edge, real-time neural network designed to predict vehicle paths using only vision-based perception. Aimed at supporting Level 2 through Level 4 autonomy, this system marks a significant advancement in scalable, camera-first solutions for both highway and complex urban driving scenarios.
At the core of Helm.ai Driver is a transformer-based deep neural network (DNN) that predicts a vehicle’s future trajectory without relying on traditional components such as high-definition (HD) maps, Lidar, or additional sensors. Instead, it integrates seamlessly with Helm.ai’s production-grade perception stack, enabling a modular approach that enhances both validation efficiency and interpretability.
The system is powered by Helm.ai’s proprietary Deep Teaching methodology and is trained on massive amounts of real-world driving data. This unique training approach allows the model to develop intelligent, human-like driving behavior—such as navigating intersections, avoiding obstacles, executing turns and passes, and responding to dynamic traffic situations like vehicle cut-ins. Remarkably, these behaviors emerge organically from the model’s end-to-end learning process rather than being explicitly programmed.
To demonstrate the system’s capabilities, Helm.ai conducted rigorous testing within a closed-loop simulation environment using the open-source CARLA simulator. Within this setup, Helm.ai Driver was able to react in real time to changes in the simulated world, mimicking real-world driving conditions. Enhancing this simulation, Helm.ai’s generative AI foundation model, GenSim-2, was used to re-render visual scenes, generating hyper-realistic camera data that closely replicates what autonomous vehicles would perceive on actual roads.
“Our team is thrilled to showcase real-time path prediction for urban driving powered solely by vision,” said Helm.ai CEO and founder Vladislav Voroninski. “Using our transformer-based neural network architecture, we’ve trained a system that exhibits sophisticated driving behavior by learning directly from real-world data—without the need for hand-coded rules.”
Voroninski emphasized the compatibility of Helm.ai Driver with their existing surround-view vision perception system, highlighting the value of a fully integrated, camera-centric architecture. “By combining path prediction with our generative AI tools for sensor simulation, we’re creating a development platform that accelerates validation and improves safety—critical for scaling autonomous driving across various use cases.”
Helm.ai’s latest release underscores its AI-first strategy in the autonomous driving landscape. By focusing on foundational models—both for path prediction and sensor simulation—the company aims to deliver solutions that generalize across different vehicle platforms, global environments, and driving conditions. This positions Helm.ai as a key player in advancing the next generation of autonomous mobility, with an emphasis on software-driven, sensor-efficient innovation.
