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Exploratory Driving Performance and Car-Following Modeling for Autonomous Shuttles Based on Field Data. (arXiv:2401.08643v1 [cs.RO])

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Autonomous shuttles (AS) operate in several cities and have shown potential
to improve the public transport network. However, there is no car following
model that is based on field data and allows decision-makers to assess and plan
for AS operations. To fill this gap, this study collected field data from AS,
analyzed their driving performance, and suggested changes in the AS trajectory
model to improve passenger comfort. A sample was collected with more than 4000
seconds of AS following a conventional car. The sample contained GPS positions
from both AS and conventional vehicles. Latitude and longitude positions were
used to calculate the speed, acceleration, and jerk of the leader and follower.
The data analyses indicated that AS have higher jerk values that may impact the
passengers comfort. Several existing models were evaluated, and the researchers
concluded that the calibrated ACC model resulted in lower errors for AS spacing
and speed. The results of the calibration indicate that the AS has lower peak
acceleration and higher deceleration than the parameters that were calibrated
for autonomous vehicle models in other research

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