Trajectory Planning for Multiple Autonomous Vehicles at Short‐Distance Tandem Signalized Intersections Based on Rule‐Free Framework

High‐level autonomous vehicles (AVs) have more possibilities for improving traffic efficiency. The improvement of traffic efficiency for mixed flow at near‐saturated short‐distance tandem signalized intersections (STSI) needs attention. Most of the existing studies design a generalized control rule for AVs, ignoring the heterogeneity among different AVs. Herein, a multivehicle trajectory planning framework based on a multiagent reinforcement learning (MRL) algorithm is designed to heuristically explore the optimal traffic efficiency of mixed flow at STSI. The core algorithm of this framework is improved from the classical MRL algorithm multi‐agent proximal policy optimization based on the idea of the virtual group instead of designing control rules. The trajectories planned by the framework show outstanding performance in improving throughputs and reducing emissions at the global system level, comparing natural driving, classic adaptive cruise control model and cooperative adaptive cruise control model. The framework can be used to explore optimal traffic efficiency for mixed flow and better heterogeneous rules for high‐level AVs.

Standort
Deutsche Nationalbibliothek Frankfurt am Main
Umfang
Online-Ressource
Sprache
Englisch

Erschienen in
Trajectory Planning for Multiple Autonomous Vehicles at Short‐Distance Tandem Signalized Intersections Based on Rule‐Free Framework ; day:26 ; month:05 ; year:2024 ; extent:16
Advanced intelligent systems ; (26.05.2024) (gesamt 16)

Urheber
Lin, Wenfeng
Hu, Xiaowei
Wang, Jian

DOI
10.1002/aisy.202300692
URN
urn:nbn:de:101:1-2405271405245.621075052939
Rechteinformation
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Letzte Aktualisierung
14.08.2025, 10:54 MESZ

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Beteiligte

  • Lin, Wenfeng
  • Hu, Xiaowei
  • Wang, Jian

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