KAUST researchers have developed a dual-use wireless sensor system that monitors both traffic congestion and flood incidents in cities. The system combines ultrasonic range finders and infrared thermal sensors to provide real-time, accurate data on traffic flow and roadway flooding. Data is sent to central servers and assimilated with satellite data to form real-time maps and forecasts. Why it matters: This technology can provide up-to-the-minute warnings for flash floods and traffic, enabling rapid emergency response and potentially saving lives in urban environments.
Dr. Jeffrey Schnapp from Harvard University discussed the shift from mobility to movability and human-centric autonomy in robotics at KAUST's 2018 Winter Enrichment Program. He presented Gita, a cargo robot designed to move like humans and support pedestrian lifestyles. Piaggio Fast Forward, Schnapp's company, aims to create robots that coexist with humans and enhance the quality of life in pedestrian-friendly environments. Why it matters: This highlights KAUST's engagement with innovative robotics research and its focus on exploring human-robot interaction for future urban development in Saudi Arabia.
Siemens CTO Rainer Speh spoke at KAUST about smart cities, noting that urban populations are growing, especially in cities like Riyadh and Jeddah. Cities consume two-thirds of the world's energy and generate 70% of CO2 emissions. Siemens is working on a driverless subway system in Riyadh as part of its smart city initiatives. Why it matters: Smart city initiatives are crucial for managing resources and reducing emissions in rapidly growing urban centers in Saudi Arabia.
KAUST, Intel, and Brightskies have launched REDD, a collaborative self-driving mobility platform, converting a conventional car into a self-driving vehicle with integrated AI software. Brightskies developed the self-driving system, powered by Intel® NUC platforms, utilizing their BrightDrive system. KAUST researchers will use the vehicle to test new techniques, leveraging real-world data to improve self-driving technologies. Why it matters: This partnership advances autonomous vehicle research in Saudi Arabia, aligning with the Kingdom's Vision 2030 by creating a platform for innovation and testing in a real-world environment.
Saudi Arabia's Ministry of Transport and Logistic Services (MOTLS) and KAUST, in collaboration with the Ministry of Industry and Mineral Resources, are launching the Future Mobility Sandbox, a 1.56 square kilometer testbed on the KAUST campus. It will enable testing of air, land, and sea transport innovations. The initiative aims to create safer, more efficient, and sustainable mobility solutions. Why it matters: The sandbox will foster innovation in autonomous, sustainable, and connected transport, positioning Saudi Arabia as a hub for advanced mobility technology and attracting global investment.
Giuseppe Loianno from NYU presented research on creating "Super Autonomous" robots (USARC) that are Unmanned, Small, Agile, Resilient, and Collaborative. The research focuses on learning models, control, and navigation policies for single and collaborative robots operating in challenging environments. The talk highlighted the potential of these robots in logistics, reconnaissance, and other time-sensitive tasks. Why it matters: This points to growing research interest in advanced robotics in the region, especially given the focus on smart cities and automation.
Khaled Alrashed, president and CEO of Saudi Electricity Company for Projects Development, discussed the challenges of future smart cities at a KAUST event. He emphasized the importance of smart grids, AI, and large-scale optimization for improving urban living. The Saudi Electricity Company is partnering with KAUST, including using the Shaheen supercomputer, to develop these technologies and predict grid load. Why it matters: This collaboration highlights Saudi Arabia's ambition to become a leader in smart city technology and renewable energy, leveraging local expertise and resources.
This article discusses the application of uncertain time series (UTS) approach to manage and analyze big traffic data for high-resolution vehicular transportation services. The study addresses challenges such as data sparseness, decision-making among multiple UTSs, and future forecasting with spatio-temporal correlations. Jilin Hui, previously a Research Associate at the Inception Institute of Artificial Intelligence (UAE), is applying this approach to solve problems related to increased congestion, greenhouse gas emissions, and reduced air quality in urban environments. Why it matters: The application of AI techniques to traffic management could significantly improve urban mobility and environmental sustainability in the GCC region and beyond.