TR2019-163

Bi-level Optimal Edge Computing Model for On-ramp Merging in Connected Vehicle Environment


    •  Ye, F., Guo, J., Kim, K.J., Orlik, P.V., Ahn, H., Di Cairano, S., "Bi-level Optimal Edge Computing Model for On-ramp Merging in Connected Vehicle Environment", IEEE Intelligent Vehicle Symposium, DOI: 10.1109/​IVS.2019.8814096, June 2019, pp. 2005-2011.
      BibTeX TR2019-163 PDF
      • @inproceedings{Ye2019jun,
      • author = {Ye, Fei and Guo, Jianlin and Kim, Kyeong Jin and Orlik, Philip V. and Ahn, Heejin and Di Cairano, Stefano},
      • title = {Bi-level Optimal Edge Computing Model for On-ramp Merging in Connected Vehicle Environment},
      • booktitle = {IEEE Intelligent Vehicle Symposium},
      • year = 2019,
      • pages = {2005--2011},
      • month = jun,
      • doi = {10.1109/IVS.2019.8814096},
      • url = {https://www.merl.com/publications/TR2019-163}
      • }
  • MERL Contacts:
  • Research Areas:

    Communications, Control

Abstract:

The coordinated on-ramp merging is one of the most common but critical vehicular applications that require complex data transmission and low-latency communication in the Connected and Automated Vehicles (CAVs) environment. An effective way to address on-ramp merging is to leverage the edge computing to optimize the coordination among vehicles to achieve overall minimum vehicle travel time and energy consumption. In this study, we propose an Bi-level Optimal Edge Computing (BOEC) model for on-ramp merging in the CAVs environment to optimize both merge time and vehicle trajectory. The simulation results show that the proposed BOEC model achieves great benefits in vehicle mobility, energy saving and air pollutant emission reduction by providing an energy-efficient trajectory following the optimal merge time without compromising safety.