Year: 2026
Type: preprint
Abstract: We consider the coordination of autonomous vehicles at unsignalized intersections, where trajectory planning and crossing-order decisions are intrinsically coupled, thereby making their simultaneous optimization intractable in practice. To this end, we propose a heuristic based on a hierarchical structure consisting of a series of nonlinear programs (NLP) in the lower level, which optimizes trajectories of each vehicle for a given crossing order, and a high-level heuristic to design new crossing schedules that exploits sensitivity information from the lower-level NLPs. Extensive numerical simulations across a broad range of traffic scenarios demonstrate that our strategy achieves substantially improved scheduling with respect to first-in-first-out ordering while preserving computational tractability.
Source: arXiv (Cornell University)
Authors: Shahi, Saugat, Fabiani, Filippo, Zanon, Mario
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7224087619