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2-D Decompositions of High-Dimensional Configurations for Efficient Multi-Vehicle Coordination at Intelligent Intersections

Akbari, Amirreza LU and Thunberg, Johan LU orcid (2026) In IEEE Transactions on Intelligent Transportation Systems
Abstract

For multi-vehicle complex traffic scenarios in shared spaces such as intelligent intersections, safe coordination and trajectory planning is challenging due to computational complexity. To meet this challenge, we introduce a computationally efficient method for generating collision-free trajectories along predefined vehicle paths. We reformulate a constrained minimum-time trajectory planning problem as a problem in a high-dimensional configuration space, where conflict zones are modeled by high-dimensional polyhedra constructed from two-dimensional rectangles. Still, in such a formulation, as the number of vehicles involved increases, the computational complexity increases significantly. To address this, we propose two algorithms for... (More)

For multi-vehicle complex traffic scenarios in shared spaces such as intelligent intersections, safe coordination and trajectory planning is challenging due to computational complexity. To meet this challenge, we introduce a computationally efficient method for generating collision-free trajectories along predefined vehicle paths. We reformulate a constrained minimum-time trajectory planning problem as a problem in a high-dimensional configuration space, where conflict zones are modeled by high-dimensional polyhedra constructed from two-dimensional rectangles. Still, in such a formulation, as the number of vehicles involved increases, the computational complexity increases significantly. To address this, we propose two algorithms for near-optimal local optimization that significantly reduce the computational complexity by decomposing the high-dimensional problem into a sequence of 2D graph search problems. The resulting trajectories are then incorporated into a Nonlinear Model Predictive Control (NMPC) framework to ensure safe and smooth vehicle motion. We furthermore show in numerical evaluation that this approach significantly outperforms existing MILP-based time-scheduling; both in terms of objective-value and computational time.

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type
Contribution to journal
publication status
epub
subject
keywords
Autonomous intersection management (AIM), cooperative motion planning, model predictive control (MPC), trajectory planning, unsignalized intersections
in
IEEE Transactions on Intelligent Transportation Systems
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:105035711007
ISSN
1524-9050
DOI
10.1109/TITS.2026.3679938
language
English
LU publication?
yes
id
faff3fdd-1b5c-405d-a6d4-0be1f9bbf8eb
date added to LUP
2026-06-24 09:20:20
date last changed
2026-06-24 09:21:05
@article{faff3fdd-1b5c-405d-a6d4-0be1f9bbf8eb,
  abstract     = {{<p>For multi-vehicle complex traffic scenarios in shared spaces such as intelligent intersections, safe coordination and trajectory planning is challenging due to computational complexity. To meet this challenge, we introduce a computationally efficient method for generating collision-free trajectories along predefined vehicle paths. We reformulate a constrained minimum-time trajectory planning problem as a problem in a high-dimensional configuration space, where conflict zones are modeled by high-dimensional polyhedra constructed from two-dimensional rectangles. Still, in such a formulation, as the number of vehicles involved increases, the computational complexity increases significantly. To address this, we propose two algorithms for near-optimal local optimization that significantly reduce the computational complexity by decomposing the high-dimensional problem into a sequence of 2D graph search problems. The resulting trajectories are then incorporated into a Nonlinear Model Predictive Control (NMPC) framework to ensure safe and smooth vehicle motion. We furthermore show in numerical evaluation that this approach significantly outperforms existing MILP-based time-scheduling; both in terms of objective-value and computational time.</p>}},
  author       = {{Akbari, Amirreza and Thunberg, Johan}},
  issn         = {{1524-9050}},
  keywords     = {{Autonomous intersection management (AIM); cooperative motion planning; model predictive control (MPC); trajectory planning; unsignalized intersections}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Transactions on Intelligent Transportation Systems}},
  title        = {{2-D Decompositions of High-Dimensional Configurations for Efficient Multi-Vehicle Coordination at Intelligent Intersections}},
  url          = {{http://dx.doi.org/10.1109/TITS.2026.3679938}},
  doi          = {{10.1109/TITS.2026.3679938}},
  year         = {{2026}},
}