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 at Boston University: Professor Christos Cassandras and Yue Zhang (PhD candidate) 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.
In summary, Professor Christos Cassandras and Yue Zhang extended earlier works on the establishment of a decentralized optimal control framework for optimally controlling CAVs crossing an urban intersection. The presented approach was inclusive of left and right turns and considered a new optimal control problem formulation where the tradeoff between energy and travel time is explicitly quantified for CAVs in the control zone and all safety constraints were incorporated. In a statement to Advances in Engineering, distinguished Professor Christos Cassandras explained that their new approach successfully expanded the optimal control framework previously presented by Zhang et al. (2016).
Yue Zhang, Christos G. Cassandras. Decentralized optimal control of Connected Automated Vehicles at signal-free intersections including comfort-constrained turns and safety guarantees. Automatica, volume 109 (2019) 108563.
Zhang, Y., Malikopoulos, A. A., & Cassandras, C. G. (2016). Optimal control and coordination of connected and automated vehicles at urban traffic intersections. In Proceedings of the American control conference (pp. 6227–6232).