INSUBCONTINENT EXCLUSIVE:
With GenSim-2, developers can modify weather and lighting conditions such as rain, fog, snow, glare, and time of day or night in video data
| Source: Helm.aiHelm.ai last week introduced the Helm.ai Driver, a real-time deep neural network, or DNN, transformer-based path-prediction
system for highway and urban Level 4 autonomous driving
The company demonstrated the models capabilities in a closed-loop environment using its proprietary GenSim-2 generative AI foundation model
to re-render realistic sensor data in simulation.“Were excited to showcase real-time path prediction for urban driving with Helm.ai
Driver, based on our proprietary transformer DNN architecture that requires only vision-based perception as input,” stated Vladislav
Voroninski, Helm.ais CEO and founder
“By training on real-world data, we developed an advanced path-prediction system which mimics the sophisticated behaviors of human
drivers, learning end to end without any explicitly defined rules.”“Importantly, our urban path prediction for [SAE] L2 through L4 is
compatible with our production-grade, surround-view vision perception stack,” he continued
“By further validating Helm.ai Driver in a closed-loop simulator, and combining with our generative AI-based sensor simulation, were
enabling safer and more scalable development of autonomous driving systems.”Founded in 2016, Helm.ai develops artificial intelligence
software for advanced driver-assist systems (ADAS), autonomous vehicles, and robotics
The company offers full-stack, real-time AI systems, including end-to-end autonomous systems, plus development and validation tools powered
by its Deep Teaching methodology and generative AI.Redwood City, Calif.-based Helm.ai collaborates with global automakers on
production-bound projects
In December, it unveiled GenSim-2, its generative AI model for creating and modifying video data for autonomous driving.Helm.ai Driver
learns in real timeHelm.ai said its new model predicts the path of a self-driving vehicle in real time using only camera-based
perception—no HD maps, lidar, or additional sensors required
It takes the output of Helm.ais production-grade perception stack as input, making it directly compatible with highly validated software
This modular architecture enables efficient validation and greater interpretability, said the companyTrained on large-scale, real-world data
using Helm.ais proprietary Deep Teaching methodology, the path-prediction model exhibits robust, human driver-like behaviors in complex
urban driving scenarios, the company claimed
This includes handling intersections, turns, obstacle avoidance, passing maneuvers, and response to vehicle cut-ins
These are emergent behaviors from end-to-end learning, not explicitly programmed or tuned into the system, Helm.ai noted.To demonstrate the
models path-prediction capabilities in a realistic, dynamic environment, Helm.ai deployed it in a closed-loop simulation using the
open-source CARLA platform (see video above)
In this setting, Helm.ai Driver continuously responded to its environment, just like driving in the real world.In addition, Helm.ai said
GenSim-2 re-rendered the simulated scenes to produce realistic camera outputs that closely resemble real-world visuals.Helm.ai said its
foundation models for path prediction and generative sensor simulation “are key building blocks of its AI-first approach to autonomous
The company plans to continue delivering models that generalize across vehicle platforms, geographies, and driving conditions.Register now
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