Waymo Considers Using Interior Camera Data for AI Training
Waymo is exploring the use of interior camera data from its robotaxis to train generative AI models, as revealed by a draft of its privacy policy discovered by researcher Jane Manchun Wong. The draft suggests that Waymo may also share this data to personalize advertisements, although the company provides options for riders to opt out of such data sharing under California's privacy laws.
The privacy policy draft indicates that Waymo may use the data to improve its services and tailor products, services, and offers to user interests. However, the company has stated that the feature is still under development and does not reflect any changes to its current privacy policy. Waymo has clarified that its machine learning systems are not designed to identify individuals or use the data for targeted ads.
Despite the potential for new revenue streams, Waymo maintains that its primary use of interior camera data is for safety and operational purposes, such as ensuring vehicle cleanliness and compliance with in-car rules. The company continues to expand its robotaxi services across major U.S. cities, logging over 200,000 paid rides weekly.
We hope you enjoyed this article
Consider subscribing to one of our newsletters like Robotics Brief, AI Policy Brief or Daily AI Brief.
Also, consider following us on social media:
More from Robotics
Sep 17 DXC Partners With LOXO on Autonomous Logistics Deployments Sep 17 ECARX Technology Reaches 12 Million Vehicles Worldwide Sep 17 Scania Launches China Venture Platform for Electric and Autonomous Transport Sep 17 D-Robotics Raises $400 Million for Robotics Chips and Software Sep 17 HL Klemove and Lenovo Partner on AI Computers for Autonomous VehiclesRobotics Brief
Weekly coverage of AI-driven robotics advances in industrial automation, autonomous vehicles, and robotic systems.
Whitepaper
Tensordyne Napier: What If One Rack Could Do the Work of Nine?
Tensordyne
This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.
Read moreYou may also like
Hyundai Activates Autonomous Driving Data Flywheel
aiMotive Launches Data Recording Systems for Automated Driving
Motional Releases nuReasoning Dataset for Autonomous Driving
Didi Starts Fully Driverless R2 Robotaxi Trials in Beijing and Guangzhou
IDrive Launches Licenseable L2+ Driver Assistance Platform
Daily AI Brief: the AI news that matters, in your inbox.