IEEE.A.Motion.Cueing.Algorithm.With.Look-Ahead.And.Driver.Characterization.Application.To.Vertical.Car.Dynamics.2018.RETAiL.eBook-KLIP
PACKS________: idib-aamoti
RELEASE DATE_: 2019.09.26
YEAR_________: 2018
LANGUAGE_____: English
RELEASE SIZE_: 0 Mb - 01 files
PAGES________: 11

SOURCE_______: https://ieeexplore.ieee.org/document/8170303
PROTECTION___: -(removed)
FORMAT_______: .pdf

ISBN CODE____: 2168-2291
GENRE________: Scientific Papers

NOTES________: Enjoy

PLOT_________: Abstract: Driving simulators are nowadays a
widely used tool in the automotive industry.
In particular, the need for safe and
repeatable conditions in automated driving
testing is now defining a new challenge: to
extend the use of the tool to nonprofessional
drivers. Quality of the motion control
strategies in generating both realistic and
feasible inputs to the driver is therefore,
more than ever, a crucial aspect. The motion
strategies are implemented in the so-called
motion cueing algorithms (MCAs). A recently
proposed effective approach to MCA is based
on model predictive control (MPC), as it is
well suited to solve constrained optimal
control problems and to take advantage of
models of the human sensing system. However,
the predictive aspect of the algorithm has
not been exploited yet, due to the hard
real-time requirement when using long
prediction windows. In this paper, a
real-time implementation of an MPC-based MCA
with predictive feature is presented, endowed
with an on-line switching policy to a
nonpredictive algorithm when the expected
driver behavior is considered unreliable. The
motion action based on the actual driver
behavior and the expected one are considered
in the same procedure, thus fully exploiting
the availability of a perceptive model. An
optimal tuning procedure is also proposed,
based on a multiobjective optimization, where
both performance improvement due to the
prediction exploitation, and robustness to
varying driver behaviour are considered.
Finally, a characterization of the driver
skill level is proposed and validated in an
experimental environment for the specific
case of the vertical DOF.

KNOWLEDGE IS POWER