Intersection control for automated vehicles with MILP

Müller, Eduardo Rauh; Carlson, Rodrigo Castelan; Kraus, Werner

doi:10.1016/j.ifacol.2016.07.007

Cited by 55 publications

(31 citation statements)

References 11 publications

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“…Müller, Carlson, and Junior (2016); Yang, Guler, and Menendez (2016); Xu et al (2017) and Yu et al (2018) formulated the problem as a two-stages problem: solve intersection optimisation first, and then optimise the trajectory. Müller, Carlson, and Junior (2016) and Yu et al (2018) assumed that every vehicle can achieve the maximum speed or desire speed when entering the intersection which may not be possible for the vehicle close to the intersection, or in high demand situation. Yang, Guler, and Menendez (2016) assumed a piece-wise linear trajectory in the trajectory optimisation model which may not be realistic, but unconnected vehicles are also considered in their model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…where the safety headway h is set to 1.5 s. Various safety headways for autonomous vehicles are used in the literature around 1 ∼ 2 s (Yang, Guler, and Menendez 2016;Ghiasi et al 2017;Jia and Ngoduy 2016b). In many studies, the process time for every vehicle is usually set to be a constant value by assuming that the vehicle passes the intersection with the maximum velocity or desired velocity (Müller, Carlson, and Junior 2016;Yu et al 2018), but this may not be possible if the vehicle is too close to the intersection or the traffic volume is high. So we define the process time as a function of the entering velocity.…”

Section: Upper Level Optimisationmentioning

confidence: 99%

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A bilevel programming model for autonomous intersection control and trajectory planning

Zhao

Liu

Ngoduy

2019

Transportmetrica A: Transport Science

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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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Section: Literature Reviewmentioning

confidence: 99%

Section: Upper Level Optimisationmentioning

confidence: 99%

A bilevel programming model for autonomous intersection control and trajectory planning

Zhao

Liu

Ngoduy

2019

Transportmetrica A: Transport Science

View full text Add to dashboard Cite

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“…where t assign,i,z is the desired arrival time to the conflict subzone z for V i , t min,i,z is the minimum arrival time to the conflict subzone z when V i travels at the maximum velocity and the maximum acceleration, Z i (1) is the first element in the set Z i , n is the number of vehicles in the control zone. To directly attack Problem (1) often leads to a mixed integer linear programming (MILP) problem whose computation time increases exponentially with the increase of the number of vehicles [8], [9].…”

Section: Problem Formulationmentioning

confidence: 99%

“…There are two equivalent formulations of the problem. Most state-ofthe-art studies formulate the problem as a mixed integer linear programming problem of vehicles' passing time scheduling [1], [8]. The objective is usually set to minimize the total delay of all CAVs.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Driving at Unsignalized Intersections Using Tree Search

Zhang

et al. 2020

IEEE Trans. Intell. Transport. Syst.

158

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In this paper, we propose a new cooperative driving strategy for connected and automated vehicles (CAVs) at unsignalized intersections. Based on the tree representation of the solution space for the passing order, we combine Monte Carlo tree search (MCTS) and some heuristic rules to find a nearly global-optimal passing order (leaf node) within a very short planning time. Testing results show that this new strategy can keep a good tradeoff between performance and computation flexibility.Index Terms-Connected and Automated Vehicles (CAVs), cooperative driving, unsignalized intersection, Monte Carlo tree search (MCTS).

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“…It tries to enumerate all possible passing orders to find the global optimal solution. Most state-of-the-art studies transfer the merging problem into various optimization problems [12], [13], [14], [15], [16], such as mixed integer linear programming (MILP), receding horizon control (RHC). However, the time consumption for solving the problem increases sharply as the number of vehicle increases, which makes these methods difficult to be applied in practice.…”

Section: Introductionmentioning

confidence: 99%

A Grouping-Based Cooperative Driving Strategy for CAVs Merging Problems

Feng

Zhang

et al. 2019

IEEE Trans. Veh. Technol.

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In general, there are two kinds of cooperative driving strategies, planning based strategy and ad hoc negotiation based strategy, for connected and automated vehicles (CAVs) merging problems. The planning based strategy aims to find the global optimal passing order, but it is time-consuming when the number of considered vehicles is large. In contrast, the ad hoc negotiation based strategy runs fast, but it always finds a local optimal solution. In this paper, we propose a grouping based cooperative driving strategy to make a good tradeoff between time consumption and coordination performance. The key idea is to fix the passing orders for some vehicles whose intervehicle headways are small enough (e.g., smaller than the preselected grouping threshold). From the viewpoint of optimization, this method reduces the size of the solution space. A brief analysis shows that the sub-optimal passing order found by the grouping based strategy has a high probability to be close to the global optimal passing order, if the grouping threshold is appropriately chosen. A series of simulation experiments are carried out to validate that the proposed strategy can yield a satisfied coordination performance with less time consumption and is promising to be used in practice.Index Terms-Connected and Automated Vehicles (CAV), cooperative driving, merging problem, grouping based strategy.

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Intersection control for automated vehicles with MILP

Cited by 55 publications

References 11 publications

A bilevel programming model for autonomous intersection control and trajectory planning

A bilevel programming model for autonomous intersection control and trajectory planning

Cooperative Driving at Unsignalized Intersections Using Tree Search

A Grouping-Based Cooperative Driving Strategy for CAVs Merging Problems

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