Core42, a G42 company, announced a 42MW expansion of its U.S. AI infrastructure at the Lake Mariner site in New York, increasing total site capacity from 18MW to 60MW. This expansion reflects G42’s continued capital investment in scaling AI infrastructure across the United States, supporting both frontier training and high-speed inference. The facility integrates AMD and NVIDIA infrastructure, strengthening its heterogeneous design for workload optimization across multiple accelerator platforms. Why it matters: This significant investment by a UAE-backed company underscores the growing global reach of Middle Eastern AI players and their crucial role in building foundational AI infrastructure worldwide.
The A2RL Vₘₐₓ dataset is an open-source resource designed for perception tasks in high-speed autonomous driving and multi-vehicle interaction. Captured during the 2024 Abu Dhabi Autonomous Racing League (A2RL) at the Yas Marina F1 Circuit, it includes data from single-vehicle, multi-vehicle, and final race scenarios with participation from all competing teams. The dataset comprises almost 30,000 professionally annotated LiDAR point clouds along with RADAR point clouds, making it the first large-scale autonomous racing dataset with professional LiDAR annotations. Why it matters: This dataset provides crucial, high-quality data to advance research in autonomous driving perception, particularly addressing the underexplored challenges of high-speed and multi-vehicle environments, further positioning Abu Dhabi as a hub for advanced AI and robotics research.
This work presents a dual pose-graph architecture for robust real-time localization in autonomous drone racing. The system fuses monocular visual-inertial odometry with semantic gate detections, using a temporary graph to optimize multiple observations into refined constraints before promoting them to a persistent main graph. Evaluated on the TII-RATM dataset and deployed in the A2RL competition, it achieved a 56-74% reduction in Absolute Trajectory Error (ATE) compared to standalone VIO and reduced odometry drift by up to 4.2 meters per lap. Why it matters: This research significantly improves the reliability and accuracy of vision-based localization for high-speed autonomous drones, crucial for advanced robotics applications and competitive racing.
Researchers at the Technology Innovation Institute (TII) have released a fully-annotated dataset for autonomous drone racing, called "Race Against the Machine." The dataset includes high-resolution visual, inertial, and motion capture data from both autonomous and piloted flights, along with commands, control inputs, and corner-level labeling of drone racing gates. The specifications to recreate their flight platform using commercial off-the-shelf components and the Betaflight controller are also released. Why it matters: This comprehensive resource aims to support the development of new methods and establish quantitative comparisons for approaches in robotics and AI, democratizing drone racing research.
This paper introduces a minimalistic autonomous racing stack designed for high-speed time-trial racing, emphasizing rapid deployment and efficient system integration with minimal on-track testing. Validated on real speedways, the stack achieved a top speed of 206 km/h within just 11 hours of practice, covering 325 km. The system performance analysis includes tracking accuracy, vehicle dynamics, and safety considerations. Why it matters: This research offers insights for teams aiming to quickly develop and deploy autonomous racing stacks with limited track access, potentially accelerating innovation in autonomous vehicle technology within the A2RL and similar racing initiatives.
KAUST's Terragraph Connectivity Project received second rank in the Social Project category of the Global Excellence Awards by the Project Management Institute (PMI) in Saudi Arabia. The project, in collaboration with Meta and the Communications, Space and Technology Commission (CST), provided high-speed Wi-Fi to a camp of 3000+ people outside KAUST. The deployed hybrid radio frequency and free space optics technology offers reliable internet connection to a remote community. Why it matters: The award and project showcase KAUST's contribution to bridging the digital divide in line with Saudi Vision 2030's goals for sustainable development and digital inclusion.
KAUST Professor Boon S. Ooi has been named an IEEE Fellow for his contributions to broadband light emitters and visible light communications. Ooi's work in semiconductor photonics has led to the establishment of successful startups. He is among the first to propose energy-efficient lighting and ultra-high-speed visible light communication using semiconductor lasers. Why it matters: Recognition of KAUST faculty demonstrates the institution's growing prominence in advanced technology research and its impact on global innovation.
KAUST, in collaboration with the Communications, Space and Technology Commission (CST) and Meta, has launched a Terragraph Wi-Fi project to bring high-speed internet to the Modern Architectural Contracting Company (MACC) camp near KAUST. The project utilizes Meta's Terragraph technology, a gigabit wireless system operating in the 57-71GHz band, to provide a low-cost, high-speed alternative to fiber. Weather stations will monitor climate variables affecting the hybrid RF/FSO links, validating KAUST's research in extreme bandwidth communication. Why it matters: This deployment demonstrates a practical solution for delivering affordable, high-speed internet access to underserved communities in the region, leveraging advanced wireless technologies and KAUST's research capabilities.