Helm.ai’s Post

Join us next week at SIGGRAPH 2024 in NVIDIA's Inception Startup Innovation Zone! We’re excited to showcase demos of our latest AI-based video generation and simulation capabilities. Plus, meet our talent team to learn about open roles!

Stop by our booth at NVIDIA's Innovation Zone during SIGGRAPH 2024 (July 30-August 1) to see how we're using generative AI to create high-fidelity, hyper-realistic videos of driving scenes for autonomous vehicle development!

Drop by our booth (1404) at #CVPR to learn about our AI software and generative AI video model for autonomous driving. We're actively hiring for multiple roles in AI/ML research and engineering!

We’re pleased to officially introduce VidGen-1, our state-of-the-art generative AI video model designed for high-end ADAS through L4 autonomous driving development and validation. Trained on thousands of hours of video, VidGen-1 generates a diversity of driving scenarios covering different geographies, landscapes, camera types, and vehicle perspectives. VidGen-1 is based on our innovations in generative DNN architectures and Deep Teaching technology, enabling it to generate highly realistic driving video, which has significance for both prediction and simulation tasks. When leveraged for generative simulation, VidGen-1 can effectively bridge the "sim-to-real" gap, and the technology behind it is adaptable to real-time AI-based intent prediction and path planning software across the AV stack. Our video generation approach is furthermore versatile, in that it can be applied to autonomous driving, robotics, and any other industry that also relies on video data. Learn more about VidGen-1 here: https://lnkd.in/gwe_shF9 #autonomousdriving #selfdrivingcars #artificialintelligence #generativeai #adas #computervision #helmai

Believe it or not, all of these driving scenes in the video are AI-generated 🤯 Visit our booth (1404) at #CVPR sponsored by IEEE Computer Society and Computer Vision Foundation to learn more about our AI work and see demos of our latest generative AI capabilities!

💡 How will unsupervised learning evolve in autonomous driving? 🚙 How does Helm.ai provide OEMs with unique differentiation advantages? Tune into the latest episode of SAE International's Tomorrow Today podcast, where our CEO and co-founder Vladislav Voroninski discusses autonomous driving trends and the pivotal roles that unsupervised learning and Helm.ai play in advancing AV software toward production deployment. A huge thanks to Grayson Brulte for hosting the discussion! Listen to the full episode here: https://lnkd.in/dQSifjnk

Helm.ai will be at CVPR 2024 in Seattle! Visit our booth to see the latest demos of our AV software and foundation models, meet our team, and explore job opportunities in AI and machine learning research. Register for CVPR here: https://lnkd.in/extQABtZ

Navigating through urban environments, in particular handling intersections and traffic lights, is a complex challenge for an autonomous vehicle. Beyond the basic 'go' and 'stop' actions based on traffic light state, there are many nuanced scenarios that require a combination of understanding more complex traffic rules, negotiating the right of way, and carefully judging the intent of other agents. Such scenarios, for example, include yielding before making a left turn or slowing down for a lead vehicle that’s about to make a right turn. How does your ADAS or L4 stack ensure safe navigation in these situations? Incorporating hand-crafted rules simply doesn’t scale to tackle the countless possibilities. Helm.ai's foundation models for intent and path prediction, trained through Deep Teaching (unsupervised learning), are able to learn the complexities of how vehicles and pedestrians interact with traffic lights, different intersection geometries, and each other, based purely on observing real driving data. Our foundation models predict how each agent, including the autonomous vehicle, might plan its path in a wide variety of relevant urban scenarios. In the demo below, you’ll see our foundation models predicting 9 seconds into the future based on 3 seconds of observed real driving data, at 5 frames per second. This advanced prediction capability allows the autonomous vehicle (the “ego vehicle”) to anticipate the maneuvers of surrounding vehicles, and plan a safe path compliant with traffic rules, all without relying on hand crafted features nor traditional simulators. As we expand our world model representation and train larger models on more driving data, the resulting intent and path prediction models will continue to improve with additional scalable and cost-effective generalization capabilities for ADAS and L4. Stay tuned for more updates! #helmai #generativeai #selfdrivingcars #artificialintelligence #ai #autonomousdriving #adas #computervision