Signal-free Path-free Intersection Control for Connected Vehicles under Automated Driving

Elham Ahmadi, Alireza Olama, Rodrigo Castelan Carlson, Eduardo Camponogara

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

We present a method for the signal-free path-free intersection control (SPIC) problem which involves coordinating connected vehicles under automated driving (CVAD) at urban intersections. A receding horizon planner is formulated as an extension of the nonlinear model predictive contouring control (NMPCC), referred to as ENMPCC. The ENMPCC method involves the inclusion of additional cost terms, traffic safety constraints, and the generation of multiple time-independent reference paths. The proposed method drops the constraints on the allowed paths within the intersection. This allows CVAD to move freely along optimal trajectories and paths, maximizing efficiency without compromising safety. Utilizing a polytopic representation of each CVAD and incorporating duality theory to formulate collision avoidance constraints, our method enables optimal and collision-free traversal of CVAD within the intersections. We demonstrate the proposed method in an urban driving scenario involving both straight and turning movements at an intersection. In comparison to the conventional path-based (lane-based) driving of the CVAD, our method shows a better use of intersection space resulting in lower travel time and fuel consumption as demonstrated through our simulation results.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalIEEE Transactions on Intelligent Vehicles
DOIs
Publication statusPublished - 8 May 2024
MoE publication typeA1 Journal article-refereed

Keywords

  • Aerospace electronics
  • collision avoidance
  • Connected vehicles
  • connected vehicles under automated driving
  • model predictive contouring control
  • Numerical models
  • Predictive models
  • Space exploration
  • Space vehicles
  • Traffic control

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