Liuqing YANG
INTRHighlight of Research Projects
Collective Intelligence for Proactive Autonomous Driving (CI-PAD)
Future transportation is promising safer and more efficient traveling via connected intelligence among vehicles as well as with the transportation infrastructure. At the same time, travelers can also be relieved from tedious driving and use vehicles as offices or entertainment rooms on the move. Essential to all these is a reliable and resilient Internet of Vehicles (IoV). Given the unique transportation environment, a satisfactorily functioning IoV is confronted with many challenges. For example, data services for transportation safety/efficiency and/or traveler convenience/comfort are often very sensitive to delays and require large bandwidth. The vehicular environments are also filled with various communication services as well as active sensing devices, which can potentially cause interferences with each other. In addition, the high mobility inherent to transportation and the fluctuation of the transportation information exchange depending on the specific traffic scenario can both lead to fast-changing and possibly unpredictable dynamics. To address these challenges, this project organizes collaborative efforts to enhance spectrum utilization, sharing, and management in IoV. The project will promote the interactions among multi-disciplinary experts such as electromagnetic waves, electronics, signal processing, and wireless communications to create wireless innovations at different network layers. The developed technologies will provide valuable tools for foundational science and engineering research and promote societal embracing of the emergent cognitive IoV technologies. The project also has an integrated education plan that aims to prepare the workforce to address future challenges of spectrum utilization and wireless communications, while promoting and embracing diversified participation in science and engineering.
This project aims to develop a cognitive IoV framework with simultaneous sensing and communications via a novel dynamic RF front end. Targeting the aforementioned challenges, the proposed IoV research has three distinctive features. First, the proposed research is centered at simultaneous communications and sensing. Based on a dynamic RF front end that is innovatively designed to facilitate full duplex modes, communications and simultaneous monitoring of multiple spectrum bands with tunable granularity become possible. Secondly, the resultant IoV framework is cognitive in two counts: i) Cognitive in the spectrum environment. The spectrum sensing information from devices equipped with the dynamic RF front end is used to develop algorithms to learn and track the spatiotemporal radio tomography with quantifiable uncertainty; and ii) Cognitive in the physical environment. With judiciously designed waveforms that enable simultaneous communications and active physical environment sensing, the acquired information will be leveraged to enhance communications. Last but not least, the proposed cognitive IoV framework is dynamics-ready via hardware, architecture, and algorithm design: the dynamic RF front end boasts real-time tuning and control capability, the network architecture incorporates unmanned aerial vehicles (UAV) to mobilize on-demand support for transportation/traveler data service hotspots, and reinforcement learning algorithms developed to achieve closed-loop control and management of spectrum resources will remain robust when the dynamics are unknown or unpredictable.
Collective Intelligence for Proactive Autonomous Driving (CI-PAD)
The aim of this project is to develop real-time situational awareness that is shared via vehicle-to-vehicle (V2V) and vehicle-to-network (V2X). The approach is to combine the perception of sensors with the interpretation of their situation to enable safer decisions, and take into account the limitations of the communication between vehicles and infrastructure. A highway system that supports autonomous and self-driven vehicles will include infrastructure sensors and onboard vehicle sensors, with massive connectivity among them and distributed intelligence across the entire transportation network. The resulting collective intelligence is one where autonomous vehicles serve as mobile sensors that augment one another, along with fixed infrastructure sensors, to construct a real-time picture of traffic. This real-time picture is used to develop proactive driving actions that optimize traffic flow and minimize accident risk. The broader impacts include focused mentoring of undergraduate students who are interested in careers that require graduate training, to broaden participation in the fields of computing and engineering.
The PIs organize an interdisciplinary project in signal processing and machine learning, control and optimization, communication, and network science. The collective intelligence framework for proactive driving includes the following modules: 1) Scene Construction, consisting of signal processing and machine learning for constructing a representation of the driving environment from multi-modal multi-view sensors; 2) Situational Interpretation, consisting of driving environment dynamic analysis at progressive levels; 3) Decision Making, consisting of optimization and control to support proactive driving for safety and optimized flow; and 4) A Failsafe Network, consisting of communication and network science that supports optimized traffic flow under nominal conditions of sensing and communication, and moderated flow under conditions of compromised sensing and communication.
Seamless Connectivity and Connected Intelligence (SC2I)(Guangzhou Municipal Key Lab)
With the development of communications and networking technology, and the miniaturization and low-power consumption of intelligent devices, the distributed multi-entity intelligence is rapidly replacing the traditional centralized intelligence. The communication network and the so-facilitated collabrative multi-entity environment perception and interaction have become the key factors determining the intelligence, efficiency, and effectiveness of the overall system. The interdisciplinary research in these areas consists of an essential component of the national science and technology development strategy. The fundamental research at our laboratory will cover seamless and ubiquitous connectivity and networked intelligence while facilitating the technology transfer of the interdisciplinary research outcomes to various real-world cyber-physical-social systems.