4.4 Article

Real-time coordination of connected vehicles at intersections using graphical mixed integer optimization

Journal

IET INTELLIGENT TRANSPORT SYSTEMS
Volume 15, Issue 6, Pages 795-807

Publisher

WILEY
DOI: 10.1049/itr2.12061

Keywords

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Funding

  1. Ministry of Science and Technology of the People's Republic of China [2018YFB1600600]
  2. FujianUniversity of Technology (FJUT) [KF-X19013]

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This paper proposes a real-time coordination scheme for managing connected vehicles at unsignalised intersections, consisting of three stages: target velocity optimisation, vehicle subgraph extraction, and velocity profile synchronisation. The scheme reduces computation time and driving delay effectively.
Management of connected vehicles at unsignalised intersections is a large-scale complex problem with safety constraints and time-varying unsolved variables, which is crucial but hard to solve online. A faster coordination system, however, not only benefits from smaller time granularity to find optimum, but also has more robustness towards a scenario with fast-moving vehicle nodes. This paper proposes a real-time coordination scheme consisting of three stages. (a) Target velocity optimisation: collision-free passage is formulated as a mixed integer linear programming problem, each approaching lane corresponding to an independent variable; (b) vehicle subgraph extraction: a directed graph is built and pruned based on the optimisation result, determining a subgraph wherein vehicle nodes pass without redundant time slot; (c) velocity profile synchronisation: velocity profile of the selected vehicles is planned synchronously, respecting inter-subgraph constraints. The main contribution of this study is to propose a fast hierarchical optimization-based coordination method, of which the complexity is invariant with the traffic density. Simulation has verified the effectiveness of the scheme from both microscopic behaviour and statistical characteristics, reducing single-step computation time to 0.02 s, and saving average driving delay by 59.83% compared to the benchmark method.

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