Open AccessReview of trajectory optimisation for connected automated vehicles
Author(s) -
Wang Yu,
Li Xiaopeng,
Yao Handong
Publication year - 2019
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5184
Subject(s) - trajectory , fuel efficiency , context (archaeology) , transport engineering , computer science , engineering , automotive engineering , risk analysis (engineering) , operations research , business , physics , astronomy , paleontology , biology
The connected automated vehicle (AV) technologies provide unprecedented opportunities for precisely controlling and optimising vehicle trajectories to improve traffic performance from the aspects of travel time reduction, driving comfort improvement, fuel consumption and emission savings and safety enhancement. Recently, connected and automated vehicle (CAV) trajectory optimisation research has become a hot topic. This study provides an overview of studies on CAV trajectory optimisation in the road traffic context, with a focus on the literature in the past decade. Rather than exhausting all related studies, this review focuses on categorising representative studies with several relevant criteria. On the basis of the review outcomes, research gaps and needs are discussed to facilitate future research.