  We present a methodology of cooperative driving in vehicular traffic, in
which for short-time traffic prediction rather than one of the statistical
approaches of artificial intelligence (AI), we follow a qualitative different
microscopic traffic prediction approach developed recently [Phys. Rev. E 106
(2022) 044307]. In the microscopic traffic prediction approach used for the
planning of the subject vehicle trajectory, no learning algorithms of AI are
applied; instead, microscopic traffic modeling based on the physics of vehicle
motion is used. The presented methodology of cooperative driving is devoted to
application cases in which microscopic traffic prediction without cooperative
driving cannot lead to a successful vehicle control and trajectory planning.
For the understanding of the physical features of the methodology of
cooperative driving, a traffic city scenario has been numerically studied, in
which a subject vehicle, which requires cooperative driving, is an automated
vehicle. Based on microscopic traffic prediction, in the methodology first a
cooperating vehicle(s) is found; then, motion requirements for the cooperating
vehicle(s) and characteristics of automated vehicle control are predicted and
used for vehicle motion; to update predicted characteristics of vehicle motion,
calculations of the predictions of motion requirements for the cooperating
vehicle and automated vehicle control are repeated for each next time instant
at which new measured data for current microscopic traffic situation are
available. With the use of microscopic traffic simulations, the evaluation of
the applicability of this methodology is illustrated for a simple case of
unsignalized city intersection, when the automated vehicle wants to turn right
from a secondary road onto the priority road.
