New Way Now: Nuro is creating a safe path to a driverless future with Google Cloud

𝗦𝘂𝗺𝗺𝗮𝗿𝘆: Andrew Clare, Chief Technology Officer of Nuro, shares how the innovative robotics company is using Google Cloud to develop the Nuro Driver — its cutting-edge driverless autonomous technology. Leveraging Google Cloud, Nuro can utilize all of its sensor data at scale for training and improving the state-of-the-art AI models that enable the system to understand and safely navigate public roads. 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲: To support its mission of deploying safe and reliable technology, Nuro needed the right AI infrastructure to create an efficient data cycle and data engine capable of powering its AI and scaling the Nuro Driver across multiple cities and geographies. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Google Cloud’s AI-optimized infrastructure is built to deliver global scale and performance for serving billions of users, providing the hardware and software capabilities Nuro needs to scale its autonomy stack. Using Google Cloud TPUs, Nuro can now process petabytes worth of real-world driving data and train AI models for producing city-scale maps and detecting obstacles on the road twice as fast without incremental cost. The company also leverages Google Kubernetes Engine to run large-scale simulations for validating the safety and performance of AI models before release. 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: Now, Nuro can fuel rapid iteration cycles to continuously train and improve its autonomy stack to deliver AI-first driverless technology that can operate and navigate any kind of vehicle — from passenger cars to semi-trucks — safely on the road. 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀 𝗮𝗻𝗱 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 𝗳𝗿𝗼𝗺 𝗔𝗻𝗱𝗿𝗲𝘄 𝗖𝗹𝗮𝗿𝗲, 𝗖𝗵𝗶𝗲𝗳 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗢𝗳𝗳𝗶𝗰𝗲𝗿, 𝗡𝘂𝗿𝗼: → “We wanted to move to a more modern architecture. We moved from doing training on Google Cloud GPUs over to TPU v5, and what that enabled us to do was to accelerate the training time of that model by 2x without any incremental cost.” → “We've been a proud partner of Google Cloud