Open AccessJournal Article10.1080/23249935.2018.1563921

A bilevel programming model for autonomous intersection control and trajectory planning

- 01 Jan 2021

- Vol. 17, Iss: 1, pp 34-58

TL;DR: A centralised way to jointly integrate an intersection control problem with vehicle trajectory planning is proposed as a bilevel optimisation problem in which the upper level is designed to minimise the total travel time by a mixed integer linear programming (MILP) model.

Abstract: Advances in autonomous and connected vehicles bring new opportunities for intelligent intersection control strategies. In this paper, we propose a centralised way to jointly integrate an intersection control problem with vehicle trajectory planning. It is formulated as a bilevel optimisation problem in which the upper level is designed to minimise the total travel time by a mixed integer linear programming (MILP) model. In contrast, the lower level is a linear programming (LP) model with an objective function to maximise the total speed entering the intersection. The two levels are coupled by the arrival time and terminal speed. By using the relationship between the safe time headway and the process time, a novel platoon-based method is developed to reduce the computational burden. Finally, simulation tests are carried out to investigate the control performance under different demands, intersection lengths, communication ranges and traffic compositions.

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1. What are the contributions in "A bilevel programming model for autonomous intersection control and trajectory planning" ?

In this paper, the authors propose a centralised way to jointly integrate an intersection control problem with vehicle trajectory planning.

2. What are the future works in "A bilevel programming model for autonomous intersection control and trajectory planning" ?

The authors showed that integrating the trajectory planning into the signal optimisation can significantly improve the intersection control performance.. The models and findings established in this paper can be extended in the following aspects in the future.. First, although the number of binary variables is greatly reduced due to the dynamic platoon formation, a more efficient heuristic method is still needed to enable a real-time application in the future.. In this case, how to develop a robust and efficient intersection control method will be an interesting research question ( Yang, Guler, and Menendez 2016 ; Li and Zhou 2017 ), which should be left in their future research.

Table 2.: The basic parameters used in simulations

Figure 1.: A simple intersection with two approaches

Figure 7.: Simulated trajectories under different control methods

Figure 8.: Simulation results under different intersection lengths

Figure 5.: The speed pattern when s0i,j = s̃i,j and Ti,j = v 0 i,j/amin + vmax/amax

Figure 3.: Calculation of minimum travel time Tmini,j

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Congested traffic states in empirical observations and microscopic simulations

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A multiagent approach to autonomous intersection management

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A bilevel programming model for autonomous intersection control and trajectory planning

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1. What are the contributions in "A bilevel programming model for autonomous intersection control and trajectory planning" ?

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Citations

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Bilevel Optimization: Theory, Algorithms, Applications and a Bibliography

A Hierarchical Model-Based Optimization Control Approach for Cooperative Merging by Connected Automated Vehicles

Cooperative Platoon Formation of Connected and Autonomous Vehicles: Toward Efficient Merging Coordination at Unsignalized Intersections

Optimal roundabout control under fully connected and automated vehicle environment

References

Congested traffic states in empirical observations and microscopic simulations

Integer and Combinatorial Optimization

Foundations of Bilevel Programming

A multiagent approach to autonomous intersection management

Practical Bilevel Optimization: Algorithms and Applications

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