@article{faff3fdd-1b5c-405d-a6d4-0be1f9bbf8eb, 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 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.

}}, 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}}, }