Cooperative Merging Speed Planning: A Vehicle-Dynamics-Free Method

Zejiang Wang, Adian Cook, Yunli Shao, Guanhao Xu, Jianfei Max Chen

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

2 Scopus citations

Abstract

Various cooperative merging control strategies at on-ramp have been proposed in the last decade. Approximated vehicle longitudinal motion models, e.g., kinematics model, have been broadly adopted for controller synthesis because of their simplicity. However, what appears problematic is that the models used for controller validation remain, in many cases, the same as the ones used for controller design. Indeed, actual vehicle dynamics contain rich behaviors that the simplified models cannot fully cover. In this paper, we first demonstrate that the actual vehicle speed can be dissimilar to the reference from a speed planner once vehicle dynamics is considered. Then, we propose two data-driven speed generators agnostic to vehicle dynamics. SUMO/Simulink joint simulations demonstrate that the proposed reference speed planners can successfully merge vehicles with distinct dynamics characteristics by following the desired sequence, speed, and intervehicle distance at the merging point while avoiding collisions.

Original languageEnglish
Title of host publicationIV 2023 - IEEE Intelligent Vehicles Symposium, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350346916
DOIs
StatePublished - 2023
Event34th IEEE Intelligent Vehicles Symposium, IV 2023 - Anchorage, United States
Duration: Jun 4 2023Jun 7 2023

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings
Volume2023-June

Conference

Conference34th IEEE Intelligent Vehicles Symposium, IV 2023
Country/TerritoryUnited States
CityAnchorage
Period06/4/2306/7/23

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 driving automation
  • model predictive control
  • model-free control
  • on-ramp merging
  • speed planning

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