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Platoon Merging Approach Based on Hybrid Trajectory Planning and CACC Strategies

  • University of California at Berkeley

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

35 Citas (Scopus)
1 Descargas (Pure)

Resumen

Currently, the increase of transport demands along with the limited capacity of the road network have increased traffic congestion in urban and highway scenarios. Technologies such as Cooperative Adaptive Cruise Control (CACC) emerge as efficient solutions. However, a higher level of cooperation among multiple vehicle platoons is needed to improve, effectively, the traffic flow. In this paper, a global solution to merge two platoons is presented. This approach combines: (i) a longitudinal controller based on a feed-back/feed-forward architecture focusing on providing CACC capacities and (ii) hybrid trajectory planning to merge platooning on straight paths. Experiments were performed using Tecnalia’s previous basis. These are the AUDRIC modular architecture for automated driving and the highly reliable simulation environment DYNACAR. A simulation test case was conducted using five vehicles, two of them executing the merging and three opening the gap to the upcoming vehicles. The results showed the good performance of both domains, longitudinal and lateral, merging multiple vehicles while ensuring safety and comfort and without propagating speed changes.
Idioma originalInglés
Número de artículo2626
Páginas (desde-hasta)2626
Número de páginas1
PublicaciónSensors
Volumen21
N.º8
DOI
EstadoPublicada - 8 abr 2021

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

  1. ODS 11: Ciudades y comunidades sostenibles
    ODS 11: Ciudades y comunidades sostenibles

Palabras clave

  • Hybrid trajectory planning approach
  • CACC
  • Cooperative merging

Project and Funding Information

  • Project ID
  • info:eu-repo/grantAgreement/EC/H2020/875530/EU/SHared automation Operating models for Worldwide adoption/SHOW
  • Funding Info
  • This research was supported by the European Project SHOW from the Horizon 2020 program under Grant Agreement No. 875530.

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