Identifying driving maneuvers plays an essential role on-board vehicles to
monitor driving and driver states, as well as off-board to train and evaluate
machine learning algorithms for automated driving for example. Maneuvers can be
characterized by vehicle kinematics or data from its surroundings including
other traffic participants. Extracting relevant maneuvers therefore requires
analyzing time-series of (i) structured, multi-dimensional kinematic data, and
(ii) unstructured, large data samples for video, radar, or LiDAR sensors.
However, such data analysis requires scalable and computationally efficient
approaches, especially for non-annotated data. In this paper, we are presenting
a maneuver detection approach based on two variants of space-filling curves
(Z-order and Hilbert) to detect maneuvers when passing roundabouts that do not
use GPS data. We systematically evaluate their respective performance by
including permutations of selections of kinematic signals at varying
frequencies and compare them with two alternative baselines: All manually
identified roundabouts, and roundabouts that are marked by geofences. We find
that encoding just longitudinal and lateral accelerations sampled at 10Hz using
a Hilbert space-filling curve is already successfully identifying roundabout
maneuvers, which allows to avoid the use of potentially sensitive signals such
as GPS locations to comply with data protection and privacy regulations like
GDPR.Comment: 7 pages, 4 figure