Session abstract and intended audience
Next-generation urban transportation systems will incorporate autonomous vehicles with intelligent infrastructure, including intelligent intersection and smart lamppost systems. The vehicles commutate with intelligent infrastructures, e.g., smart lampposts, and other surrounding vehicles via vehicular ad-hoc networks (VANETs) to improve vehicle safety and transportation efficiency. In this session, the three talks will introduce the challenges for both autonomous vehicles and intelligent infrastructures, as well as the interactions between them. The talks will also present state-of-the-art solutions. This session addresses two of the most active topics in the community – autonomous vehicles and real-time intelligent infrastructures. It should attract RTSS researchers and engineers who are interested in next-generation vehicle design and urban mobility system innovation, and should stimulate discussions and promote collaborations across different areas.
Estimated number of audience: 40.
Registration: Please register here for RTSS 2019.
Urban Mobility and Intelligent Intersection: Challenges and Opportunities
Contributors: Ram Rajagopal (speaker), Associate Professor, Civil and Environmental Engineering, Stanford University, email@example.com.
Abstract: This talk will discuss the challenges and opportunities in designing and optimizing urban transportation systems, with large penetration of electric vehicles and autonomous vehicles, with intelligent infrastructure support.
Real-time Driver Motion Sensing for Next-Generation Intelligent Vehicles
Contributors: Guoliang Xing (speaker), Professor, Information Engineering, The Chinese University of Hong Kong, firstname.lastname@example.org.
Abstract: The next-generation vehicles are envisioned to intelligently sense driver behavior and enable unobtrusive in-vehicle interactions, leading to improved driving safety and experience. For instance, smartwatches of driver can monitor the action of the driver, detect possible secondary distracting tasks, and allow driver to use gestures for in-vehicle controls. The key enabling technology for such a vision is to accurately track the real-time attitude of mobile devices in the driving vehicle, despite the significant dynamics and inference from vehicle mobility. In this talk, I will discuss our recent work on a system called Real-time Attitude and Motion Tracking (RAMT) that can enable a mobile device to accurately learn the coordinate system of a moving vehicle, and hence track its attitude and motion in real time. RAMT consists of a series of novel sensing and learning algorithms to sense the vehicle's movement and calculate the device's attitude, enabling trajectory-based gesture recognition and driver-vehicle interaction. The performance of RMAT has been validated through real driving trips.
Real-Time Edge Computing for Cyber-Physical Systems
Contributors: Chenyang Lu (speaker), Fullgraf Professor, Washington University in St. Louis
Abstract: Real-time edge computing is essential to support cyber-physical systems such as smart infrastructure for urban mobility. This talk will discuss the challenges and directions to meet diverse timing and reliability requirements in a real-time edge computing architecture.
Ram Rajagopal, Stanford University
Ram Rajagopal is an Associate Professor of Civil and Environmental Engineering at Stanford University, where he directs the Stanford Sustainable Systems Lab (S3L), focused on large-scale monitoring, data analytics and stochastic control for infrastructure networks, in particular, power networks. His current research interests in power systems are in the integration of renewables, smart distribution systems, and demand-side data analytics. He holds a Ph.D. in Electrical Engineering and Computer Sciences and an M.A. in Statistics, both from the University of California Berkeley, Masters in Electrical and Computer Engineering from University of Texas, Austin and Bachelors in Electrical Engineering from the Federal University of Rio de Janeiro. He is a recipient of the NSF CAREER Award, Powell Foundation Fellowship, Berkeley Regents Fellowship and the Makhoul Conjecture Challenge award. He holds more than 30 patents and several best paper awards from his work and has advised or founded various companies in the fields of sensor networks, power systems, and data analytics.
Guoliang Xing, The Chinese University of Hong Kong
Guoliang Xing is a Professor in the Department of Information Engineering, the Chinese University of Hong Kong. Prof. Guoliang Xing received the B.S. and M.S degrees from Xi’an Jiao Tong University, China, in 1998 and 2001, and the D.Sc. degree from Washington University in St. Louis, in 2006. Professor Xing’s research lies at the intersection between systems, embedded AI, data/information processing algorithms, and domain sciences, with a focus on interdisciplinary applications in health, environment, and energy. His research group develops new technologies at the frontier of mobile health, Cyber-Physical Systems (CPS), Internet of Things (IoT), wireless networks, security and privacy. Several mobile health technologies developed in his lab have won several Best App Awards at the MobiCom conference and been successfully transferred to the industry. Prof. Xing led the development of several large-scale cyber physical systems, including 20+ seismic monitoring systems that were field tested and deployed on two live volcanoes in South America. Since 2006, he has been awarded 2 Hong Kong CERG grants and 10 US NSF grants (8 as PI), including 4 large multi-institute grants from major NSF interdisciplinary programs on smart health, Cyber Physical Systems, and sustainability. Prof. Xing is currently an Editor for ACM Transactions on Sensor Networks (TOSN), and has served as the Steering Committee member, General Chair, and TPC Co-Chair of several international conferences including IPSN – the IEEE/ACM’s premier conference on information processing in sensor systems, and RTSS – the IEEE’s premier conference on real-time systems. He received the Withrow Distinguished Scholar Award from Michigan State University in 2014 and the Faculty Early Career Development (CAREER) Award from the US National Science Foundation in 2010. His group received two Best Paper Awards and five Best Paper Nominations from prestigious international conferences including ICNP, IPSN, and PerCom.
Chenyang Lu, Washington University in St. Louis
Chenyang Lu is the Fullgraf Endowed Chair Professor at Washington University in St. Louis. His research interests include Internet of Things, real-time and embedded systems, and cyber-physical systems. Professor Lu's current work focuses on real-time cloud, industrial cyber-physical systems and Internet of Medical Things. In the area of real-time cloud, he led the research on RT-Xen, a real-time scheduler that has been incorporated in the Xen hypervisor. In the area of industrial cyber-physical systems, his research advanced real-time wireless networks and cyber-physical co-design for dependable wireless control systems. In the area of Internet of Medical Things, he piloted one of the world's first large-scale wireless sensor networks for clinical monitoring. The author and co-author of over 170 research papers with over 20,000 citations and an h-index of 64, he is among the most published researchers at top conferences in the embedded and real-time systems area. Professor Lu served as Editor-in-Chief of ACM Transactions on Sensor Networks from 2011 to 2017 and currently chairs the IEEE Technical Committee on Real-Time Systems (TCRTS). He received the Ph.D. degree from University of Virginia in 2001. He is a Fellow of IEEE.