Optimal control and coordination of connected and automated vehicles at urban traffic intersections

Zhang, Yue J.; Malikopoulos, Andreas A.; Cassandras, Christos G.

doi:10.1109/acc.2016.7526648

Cited by 179 publications

(177 citation statements)

References 12 publications

(13 reference statements)

Supporting

Mentioning

168

Contrasting

Order By: Relevance

“…. The unconstrained and constrained arcs are pieced together to determine the 3+1+1 quantities above along with the constants of integration in (14)- (16) while the Hamiltonian at the corner is H…”

Section: Problemmentioning

confidence: 99%

A Closed-Form Analytical Solution for Optimal Coordination of Connected and Automated Vehicles

Malikopoulos

Zhao

2019

2019 American Control Conference (ACC)

Self Cite

View full text Add to dashboard Cite

In earlier work, a decentralized optimal control framework was established for coordinating online connected and automated vehicles (CAVs) in merging roadways, urban intersections, speed reduction zones, and roundabouts. The dynamics of each vehicle were represented by a double integrator and the Hamiltonian analysis was applied to derive an analytical solution that minimizes the L 2 -norm of the control input. However, the analytical solution did not consider the rear-end collision avoidance constraint. In this paper, we derive a complete, closed-form analytical solution that includes the rear-end safety constraint in addition to the state and control constraints. We augment the double integrator model that represents a vehicle with an additional state corresponding to the distance from its preceding vehicle. Thus, the rear-end collision avoidance constraint is included as a state constraint. The effectiveness of the solution is illustrated through simulation.

show abstract

Section: Problemmentioning

confidence: 99%

A Closed-Form Analytical Solution for Optimal Coordination of Connected and Automated Vehicles

Malikopoulos

Zhao

2019

2019 American Control Conference (ACC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…We begin with a brief review of the model introduced in [21] and fully developed in [19]. We consider an intersection ( Fig.…”

Section: The Intersection Modelmentioning

confidence: 99%

“…is the lower bound constrained by the speed and control constraints and t 1 i , t 2 i were derived in [21]:…”

Section: The Intersection Modelmentioning

confidence: 99%

Joint Time and Energy-Optimal Control of Connected Automated Vehicles at Signal-Free Intersections with Speed-Dependent Safety Guarantees

Zhang

Cassandras

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

Self Cite

View full text Add to dashboard Cite

We extend earlier work establishing a framework for optimally controlling Connected Automated Vehicles (CAVs) crossing a signal free intersection by jointly optimizing energy and travel time. We derive explicit optimal control solutions in a decentralized manner that guarantee both a speed-dependent rear-end safety constraint and a time-dependent lateral collision constraint, in addition to lower/upper bounds on speed and acceleration. Extensive simulation examples are included to illustrate this framework. arXiv:1903.05692v3 [math.OC]

show abstract

“…In the approach [9] proposed by Wu et al, the estimated arrival time is shared wirelessly among vehicles to obtain the best passing sequence. The problem of coordinating online a continuous flow of Connected and Autonomous Vehicles crossing two adjacent intersections was formulated as a decentralized optimal control problem in [10]. The solution gives the optimal acceleration/deceleration for each vehicle at any time to minimize fuel consumption.…”

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm Approach for Expedited Crossing of Emergency Vehicles in Connected and Autonomous Intersection Traffic

Qiang

Kim

2017

Journal of Advanced Transportation

View full text Add to dashboard Cite

This paper proposes an intersection control algorithm which aims to determine an efficient vehicle-passing sequence that allows the emergency vehicle to cross an intersection as soon as possible while the travel times of other vehicles are minimally affected. When there are no emergency vehicles within the intersection area, the vehicles are controlled by the DICA that we proposed in our earlier work. When there are emergency vehicles entering the communication range, we prioritize emergency vehicles through optimal ordering of vehicles. Since the number of possible vehicle-passing sequences increases rapidly with the number of vehicles, finding an efficient sequence of vehicles in a short time is the main challenge of the study. A genetic algorithm is proposed to solve the optimization problem which finds the optimal vehicle sequence that gives the emergency vehicles the highest priority. The efficiency of the proposed approach for expedited crossing of emergency vehicles is validated through comparisons with DICA and a reactive traffic light algorithm through extensive simulations. The results show that the proposed genetic algorithm is able to decrease the travel times of emergency vehicles significantly in light and medium traffic volumes without causing any noticeable performance degradation of normal vehicles.

show abstract

Optimal control and coordination of connected and automated vehicles at urban traffic intersections

Cited by 179 publications

References 12 publications

A Closed-Form Analytical Solution for Optimal Coordination of Connected and Automated Vehicles

A Closed-Form Analytical Solution for Optimal Coordination of Connected and Automated Vehicles

Joint Time and Energy-Optimal Control of Connected Automated Vehicles at Signal-Free Intersections with Speed-Dependent Safety Guarantees

A Genetic Algorithm Approach for Expedited Crossing of Emergency Vehicles in Connected and Autonomous Intersection Traffic

Contact Info

Product

Resources

About