Digital’s Post

Good approach to explore digital twin and human-centric simulation.

Using generative AI, Scenario Diffusion crafts realistic, complex driving scenarios for its robotaxi. Learn why researchers at Zoox believe this technology will become "foundational to the future of safety validation for autonomous vehicles." #GenerativeAI #AutonomousRobots

The automated creation of synthetic traffic scenarios is integral to validating the safety of autonomous vehicles. We met with Amazon Science to discuss a paper we presented at the 2023 Conference on Neural Information Processing Systems (NeurIPS) where we address this with a method we call Scenario Diffusion. Kai Wang Ethan Pronovost Nicholas Roy Meghana Reddy Ganesina Nour Hendy Andres Morales #GenerativeAI #MachineLearning #Simulation #Robotaxi #AutonomousVehicles

This is an innovative example of a diffusion model used to create critical edge cases for simulations! No doubt this will become an industry standard in years to come. I believe that generative AI will become key to Product Validation and Testing in the not so distant future.

The system comprises a novel ML architecture based on latent diffusion, an ML technique used in image generation in which a model learns to convert random noise into detailed images: https://lnkd.in/gfAwvxmJ #AmazonScience #Zoox #GenerativeAI #LLMs #ML #GenAI

One of the challenges faced by autonomous vehicles is the sometimes unpredictable nature of traffic and drivers. This new approach allows Zoox to "produce safety-critical driving scenarios" by relying on, in part, generative AI. #robotaxi #autonomousvehicles #robotics #AmazonScience

How can we ensure that the AI driving our future autonomous vehicles treats everyone fairly? As autonomous vehicles (AVs) become increasingly prevalent, the integration of artificial intelligence is pivotal in enabling these systems to make real-time decisions. However, as engineers employ machine learning to power AVs, ensuring fairness and preventing bias is critical. Recent studies highlight the challenges of AI discrimination, particularly against underrepresented groups, raising important questions about safety and inclusivity in AV technology. With the new EU AI Act classifying transport AI as high-risk, it's essential that our engineering approaches, like Quality Function Deployment (QFD), prioritize fairness alongside traditional metrics like safety and performance (Link in the comment). As we develop AVs, incorporating comprehensive safety features that prevent discrimination is not just a regulatory compliance issue—it is a moral imperative. Let us ensure our technological advances benefit everyone equally. Daniel Wentzel Süleyman Y. Levin U. Dr. Stefan Raff Stefan RoseToyota Deutschland RWTH Marketing Group DEXLab #AutonomousVehicles #AI #MachineLearning #InclusiveInnovation #TechEthics

Unlocking the Challenges of Real-World Reinforcement Learning in Autonomous Vehicles I'm excited to share my latest video where I delve into the intricate and fascinating world of reinforcement learning (RL) applied to autonomous vehicles. This deep dive covers the major hurdles we face when deploying RL algorithms in real-world driving scenarios and UAVs. Key Topics Discussed: 🔹 High-Dimensional Continuous State and Action Spaces   🔹 Partial Observability and Non-Stationarity   🔹 Unspecified and Multi-Objective Reward Functions   🔹 Explainability   🔹 Real-Time Inference   🔹 System Delays   🔹 Safety Considerations  📽️ Watch the full video here: https://lnkd.in/datjH_pS Whether you're an AI enthusiast, a professional in the field, or simply curious about the future of autonomous vehicles, this video is packed with insights that you won't want to miss! For example, insights on the #IEEE #review #process and practical points. Let’s drive the conversation forward—what do you think are the biggest challenges in implementing RL for autonomous vehicles? Share your thoughts in the comments! You can find the Farsi version of my video on my YouTube channel. #ReinforcementLearning #AutonomousVehicles #AI #MachineLearning #DeepLearning #Explainability #SafetyInAI #UAVs

Scenario Diffusion uses generative AI to navigate safety-critical situations in autonomous vehicles by creating realistic driving scenarios to enhance safety testing, and ensure the reliability of the company’s robotaxis across diverse environments. Learn more about the method from Zoox. Paper: https://lnkd.in/e9n4iVBF Blog post: https://lnkd.in/eNnuQM4e #GenerativeAI #AutonomousVehicles #ML #Zoox

I'm happy to share that I have recently published a new research paper with Anat Meir and Einat Grimberg on the topic of the role of tele-drivers in improving safety in several use cases. Our study examines the difficulties that highly skilled tele-drivers (TEDs) face while performing remote interventions for Autonomous Vehicles (AVs). We compare these challenges with those faced by In-Vehicle Drivers (IVDs) and explore technological solutions that can help mitigate them. Our paper also identifies gaps in knowledge and suggests areas for future research, which can provide insights for practitioners and researchers working in the field of autonomous technology. If you're interested in reading our research paper, it is now available in the Journal of Ergonomics at: https://lnkd.in/d3XnNjV6.