Helm.ai, a leading provider of advanced AI software for high-end ADAS, Level 4 autonomous driving and robotics automation, Generative AI A model that generates highly realistic video sequences of driving scenes for autonomous driving development and validation. This innovative AI technique follows Helm.ai's GenSim-1 announcement for AI-generated labeled images, which is important for both predictive tasks and generative simulation.
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“Predicting the next frame of a video is like predicting the next word in a sentence, but on a much higher dimensional level.”
Helm.ai's generative AI video models are trained on thousands of hours of diverse driving footage, combining an innovative Deep Neural Network (DNN) architecture with Deep Teaching, a highly efficient unsupervised training technique, to create realistic video sequences of driving scenes. Videos generated at 384×640 resolution, variable frame rate (up to 30 frames per second), and up to several minutes in length can be generated randomly with no input prompts, or prompted with a single image or input video.
VidGen-1 can generate videos of driving scenes from different geographies and from multiple types of cameras and vehicle perspectives. The model not only generates highly realistic appearance and temporally consistent object motion, but also learns and reproduces human-like driving behaviors, generating motions of the ego-vehicle and surrounding agents that obey traffic rules. The model simulates realistic video footage of a variety of scenarios in multiple cities around the world, including urban and suburban environments, different vehicles, pedestrians, cyclists, intersections, corners, weather conditions (rain, fog, etc.), lighting effects (glare, night driving, etc.), as well as wet road surfaces, reflective building walls, and accurate reflections on the ego-vehicle hood.
Video data comes from cameras, which are the most informative sensory modality and the most cost-effective sensor in autonomous driving. However, the high dimensionality of video data makes AI video generation a challenging task. Achieving high image quality while accurately modeling the dynamics of a moving scene, i.e., achieving video realism, is a well-known challenge in video generation applications.
“Our breakthrough in video generative AI has led us to develop VidGen-1 and set a new standard in the autonomous driving space. Our years of deep teaching technology, combined with our in-house innovation in generative DNN architectures, have resulted in a highly effective and scalable way to create realistic AI-generated videos. Our technology is versatile and can be directly applied to autonomous driving, robotics and other video generation domains,” said Vladislav Voroninski, CEO and co-founder of Helm.ai.
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By enabling rapid asset generation and imbuing agents in the simulation with sophisticated real-world behaviors, VidGen-1 offers automakers significant scalability advantages compared to traditional non-AI simulation. Helm.ai's approach not only reduces development time and cost, but also provides a highly realistic and efficient solution that effectively bridges the “simulation-to-reality” gap, greatly expanding the reach of simulation-based training and validation.
“Predicting the next frame of a video is similar to predicting the next word in a sentence, but at a much higher dimensional level,” Voroninski added. “Generating realistic video sequences of driving scenes is the most advanced form of prediction for autonomous driving, because it accurately models what the real world looks like and includes both intent prediction and path planning as implicit subtasks at the top level of the stack. This capability is critical for autonomous driving, because driving is fundamentally about predicting what will happen next.”
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