Multiobjective optimization framework for cooperative adaptive cruise control vehicles in the automated vehicle platooning environment

Zijia Zhong, Joyoung Lee, Liuhui Zhao

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Scopus citations

Abstract

Automated longitudinal control technology has been tested through cooperative adaptive cruise control (CACC), which is envisioned to improve highway mobility drastically by forming a vehicle platoon with short headway while maintaining stable traffic flow under disturbances. Compared with previous research efforts with the pseudomulti-objective optimization process, this paper proposes an automated longitudinal control framework based on multiobjective optimization (MOOP) for CACC by taking into consideration four optimization objectives: mobility, safety, driver comfort, and fuel consumption. Of the target time headways that have been tested, the proposed CACC platoon control method achieved the best performance with 0.9- and 0.6-s target time headways. Compared with a non-optimization-based CACC, the MOOP CACC achieved 98%, 93%, 42%, and 33% objective value reductions of time headway deviation, unsafe condition, jitter, and instantaneous fuel consumption, respectively. In comparison with a single-objective-optimization-based approach, which optimized only one of the four proposed objectives, it was shown that the MOOP-based CACC maintained a good balance between all of the objective functions and achieved Pareto optimality for the entire platoon.

Original languageEnglish (US)
Title of host publicationConnected and Automated Vehicles
PublisherNational Research Council
Pages32-42
Number of pages11
ISBN (Electronic)9780309441551
DOIs
StatePublished - 2017

Publication series

NameTransportation Research Record
Volume2625
ISSN (Print)0361-1981

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Mechanical Engineering

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