Energy-Centric Cooperative Onramp Merging Strategy: An Analytical Solution

Zejiang Wang, Anye Zhou, Adian Cook, Yunli Shao, Guanhao Xu, Max Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Cooperative highway onramp merging plays an important role in mitigating highway traffic congestion. A crucial component of a cooperative merging system is the merging sequence strategy, which determines each merging participant's order to reach the merging point. Existing merging sequence strategies can be classified into rule-based and optimization-based approaches. The rule-based strategies can be effortlessly implemented with a light online computational burden. However, they may not achieve the optimal energy efficiency. In contrast, the optimization-based strategies can yield the optimal merging sequence to minimize fuel consumption, but typically involve computationally expensive numerical optimization. To leverage the advantages from both sides, we propose a novel merging sequence strategy that can minimize fuel consumption while avoiding online numerical optimization. The key idea is to analytically formulate the expected fuel consumption of each merging participant. Using a realistic highway onramp scenario based on the NGSIM dataset, we validate the performance and the computational efficiency of the proposed merging sequence strategy via SUMO/SIMULINK joint simulation.

Original languageEnglish
Title of host publicationIAVVC 2023 - IEEE International Automated Vehicle Validation Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350322538
DOIs
StatePublished - 2023
Event2023 IEEE International Automated Vehicle Validation Conference, IAVVC 2023 - Austin, United States
Duration: Oct 16 2023Oct 18 2023

Publication series

NameIAVVC 2023 - IEEE International Automated Vehicle Validation Conference, Proceedings

Conference

Conference2023 IEEE International Automated Vehicle Validation Conference, IAVVC 2023
Country/TerritoryUnited States
CityAustin
Period10/16/2310/18/23

Funding

This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

  • cooperative merging
  • fuel consumption model
  • merging sequence
  • optimal control

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