  Motion planning is a fundamental component in automated vehicles. It
influences the comfort and time efficiency of the ride. Despite a vast
collection of studies working towards improving motion comfort in self-driving
cars, little attention has been paid to the performance of human drivers as a
baseline. In this paper, we present an experimental study conducted on a public
road using an instrumented vehicle to investigate how human drivers balance
comfort and time efficiency. The human driving data is compared with two
optimization-based motion planners that we developed in the past. In situations
when there is no difference in travel times, human drivers incurred an average
of 23.5% more energy in the longitudinal and lateral acceleration signals than
the motion planner that minimizes accelerations. In terms of frequency-weighted
acceleration energy, an indicator correlated with the incidence of motion
sickness, the average performance deficiency rises to 70.2%. Frequency-domain
analysis reveals that human drivers exhibit more longitudinal oscillations in
the frequency range of 0.2-1 Hz and more lateral oscillations in the frequency
range of up to 0.2 Hz. This is reflected in time-domain data features such as
less smooth speed profiles and higher velocities for long turns. The
performance difference also partly results from several practical matters and
additional factors considered by human drivers when planning and controlling
vehicle motion. The driving data collected in this study provides a performance
baseline for motion planning algorithms to compare with and can be further
exploited to deepen the understanding of human drivers.
