Advancing Smart Cities with the Internet of Cars
Quicker, Safer and Greener Intersections
Intersections with Dynamic Traffic Control
In road transport engineering, an intersection is defined as at-grade junction where two or more roads or streets meet or cross. Statistically, it has been evidenced that intersections present a major hurdle in traffic control as they account for the lion’s share of accidents and of overall road congestion. Present technological innovations have led to the introduction of autonomous vehicles/Connected Automated Vehicles (CAVs). Ideally, such innovations demand the development of an efficient traffic management method purposed to reduce congestion and increase safety with not affect the existing infrastructure. This can be achieved through tighter spacing of vehicles. At present, traffic light control is the prevailing method for coordinating conflicting traffic flows and ensure road safety in urban areas.
Contemporary research has resulted in the development of an adaptive traffic light control system capable of dynamic adjustment of the signal timing to various contexts. Regardless, such developments fail to meet the safety demands of CAVs. Past research by this team established a decentralized optimal control framework for coordinating on line a continuous flow of CAVs crossing an urban intersection without using explicit traffic signaling, assuming no left and right turns are allowed. In that publication however, to achieve safety, they required CAVs to have a constant speed through the merging zone while also maintaining a safe distance between them to avoid rear-end collision.
Notable progress was reported in that previous work. Needless to say, for CAVs to better fit in existing infrastructure, they ought to be able to vary their speed depending on the turn due to the fact that now, right and left turn must be included in the framework. On this account, researchers from the Division of Systems Engineering and Center for Information and Systems Engineering (CISE) at Boston University: Professor Christos Cassandras (ECE, SE) and former PhD student Yue Zhang (SE ’19) proposed to build on their earlier work for optimally controlling CAVs crossing a signal-free intersection by including all possible turns taken so as to optimize a passenger comfort metric along with energy and travel time minimization. Their work is currently published in the research journal Automatica.
In their work, they reviewed the previously proposed model and extended it to include left and right turns. Subsequently, they derived the conditions that guarantee safety for each CAV in terms of its time to reach the merging zones constrained by those of CAVs preceding it in the control zone. The authors then formulated a decentralized optimal control problem for each CAV that jointly minimized its travel time and energy consumption, proved structural properties of optimal trajectories, and derived an explicit solution for it. Lastly, they formulated and solved another optimization problem with the objective of jointly minimizing a passenger comfort metric inside the merging zone and its energy consumption.
The authors reported that despite the added complexities, the optimal solution could still be obtained in decentralized fashion, with each CAV requiring information from a subset of other CAVs. Overall, their analysis provided an optimal planned trajectory which should be viewed as a reference to be tracked by a lower level CAV controller, for example using Model Predictive Control methods.
Christos G. Cassandras is Distinguished Professor of Engineering at Boston University. He is Head of the Division of Systems Engineering, Professor of Electrical and Computer Engineering, and co-founder of Boston University’s Center for Information and Systems Engineering (CISE). He received degrees from Yale University (B.S., 1977), Stanford University (M.S.E.E., 1978), and Harvard University (S.M., 1979; Ph.D., 1982). In 1982-84 he was with ITP Boston, Inc. where he worked on the design of automated manufacturing systems. In 1984-1996 he was a faculty member at the Department of Electrical and Computer Engineering, University of Massachusetts/Amherst. He specializes in the areas of discrete event and hybrid systems, cooperative control, stochastic optimization, distributed optimization in network systems, and computer simulation, with applications to computer and sensor networks, manufacturing systems, and transportation systems.
He has published over 400 refereed papers in these areas, and six books. He has guest-edited several technical journal issues and serves on several journal Editorial Boards. In addition to his academic activities, he has worked extensively with industrial organizations on various systems integration projects and the development of decision-support software. He has most recently collaborated with The MathWorks, Inc. in the development of the discrete event and hybrid system simulator SimEvents®.
Dr. Cassandras was Editor-in-Chief of the IEEE Transactions on Automatic Control from 1998 through 2009 and has also served as Editor for Technical Notes and Correspondence and Associate Editor. He was the 2012 President of the IEEE Control Systems Society (CSS). He has also served as Vice President for Publications and on the Board of Governors of the CSS, as well as on several IEEE committees, and has chaired several conferences. He has been a plenary/keynote speaker at numerous international conferences, including the American Control Conference in 2001, the IEEE Conference on Decision and Control in 2002 and 2016, and the 20th IFAC World Congress in 2017, and has also been an IEEE Distinguished Lecturer.
He is the recipient of several awards, including the 2011 IEEE Control Systems Technology Award, the Distinguished Member Award of the IEEE Control Systems Society (2006), the 1999 Harold Chestnut Prize (IFAC Best Control Engineering Textbook) for Discrete Event Systems: Modeling and Performance Analysis, a 2011 prize and a 2014 prize for the IBM/IEEE Smarter Planet Challenge competition (for a “Smart Parking” system and for the analytical engine of the Street Bump system respectively), the 2014 Engineering Distinguished Scholar Award at Boston University, several honorary professorships, a 1991 Lilly Fellowship and a 2012 Kern Fellowship. He is a member of Phi Beta Kappa and Tau Beta Pi. He is also a Fellow of the IEEE and a Fellow of the IFAC.
Yue Zhang received a PhD degree in Systems Engineering from Boston University under the supervision of Dr. Christos G. Cassandras in 2019. Her research interests include control and optimization of hybrid systems and big data analytics with applications to intelligent transportation systems. During the summer of 2015, she worked as a research intern with the National Transportation Research Center at the Oak Ridge National Laboratory, Oak Ridge, TN. During the summer of 2018, she worked as a software development intern at Facebook, Menlo Park, CA. During the summer of 2019, she interned with the Autonomous Vehicles team at NVIDIA, Santa Clara, CA. Currently, she is working as a research scientist at Facebook on Ads Ranking.