Helm.ai, a provider of AI software for high-end ADAS, Level 4 autonomous driving, and robotics, launched a multi-sensor generative AI foundation model for simulating the entire autonomous vehicle stack.
WorldGen-1 synthesizes highly realistic sensor and perception data across multiple modalities and perspectives simultaneously, extrapolates sensor data from one modality to another, and predicts the behavior of the ego-vehicle and other agents in the driving environment. These AI-based simulation capabilities streamline the development and validation of autonomous driving systems.
Leveraging innovation in generative DNN architectures and Deep Teaching, a highly efficient unsupervised training technology, WorldGen-1 is trained on thousands of hours of diverse driving data, covering every layer of the autonomous driving stack including vision, perception, lidar, and odometry.
WorldGen-1 simultaneously generates highly realistic sensor data for surround-view cameras, semantic segmentation at the perception layer, lidar front-view, lidar bird’s-eye-view, and the ego-vehicle path in physical coordinates. By generating sensor, perception, and path data consistently across the entire AV stack, WorldGen-1 accurately replicates potential real-world situations from the perspective of the self-driving vehicle. This comprehensive sensor simulation capability enables the generation of high-fidelity multi-sensor labeled data to resolve and validate a myriad of challenging corner cases.
Furthermore, WorldGen-1 can extrapolate from real camera data to multiple other modalities, including semantic segmentation, lidar front-view, lidar bird’s-eye-view, and the path of the ego vehicle. This capability allows for the augmentation of existing camera-only datasets into synthetic multi-sensor datasets, increasing the richness of camera-only datasets and reducing data collection costs.
Beyond sensor simulation and extrapolation, WorldGen-1 can predict, based on an observed input sequence, the behaviors of pedestrians, vehicles, and the ego-vehicle in relation to the surrounding environment, generating realistic temporal sequences up to minutes in length. This enables AI-generation of a wide range of potential scenarios, including rare corner cases.
WorldGen-1 can model multiple potential outcomes based on observed input data, demonstrating its ability for advanced multi-agent planning and prediction. WorldGen-1’s understanding of the driving environment and its predictive capability make it a valuable tool for intent prediction and path planning, both as a means of development and validation, as well as the core technology that makes real-time driving decisions.
Source from Green Car Congress
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