Affiliation:
1. Illinois Institute of Technology, USA
Abstract
<div>This article presents a merge-aware cruise control method that incorporates
vehicle-to-vehicle (V2V) information and aims at improving the energy efficiency
of vehicles and reducing speed disruptions of merging traffic during highway
merges. During the events of highway merges, the gap between the ego and the
preceding vehicle reduces drastically, which can result in sudden braking of the
ego vehicle and thus reduction of its energy efficiency. We propose a rather
simple cruise control algorithm to eliminate such sudden variations in the gap
and velocity with respect to the preceding vehicle during highway merges, thus
reducing the large accelerations and braking during such events and thereby
improving energy efficiency. The proposed algorithm incorporates future traffic
information and has computational requirements similar to adaptive cruise
control methods, hence it is real-time applicable. Data used in this article are
taken from on-road experiments using a 2020 Tesla Model 3. Simulation results
show the efficacy of our proposed control algorithm.</div>
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