HD Map Verification Without Accurate Localization Prior Using Spatio-Semantic 1D Signals

• Published in: 2020 IEEE Intelligent Vehicles Symposium (IV) pp. 680 - 686
• Main Authors: Pauls, Jan-Hendrik, Strauss, Tobias, Hasberg, Carsten, Lauer, Martin, Stiller, Christoph
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19.10.2020
• Subjects: Feature extraction; Particle separators; Roads; Semantics; Solids; Splines (mathematics); Trajectory
• ISSN: 2642-7214
• DOI: 10.1109/IV47402.2020.9304716

High definition (HD) maps have proven to be a necessary component for safe and comfortable automated driving (AD) [1]. Naïvely verifying HD maps requires an accurate localization prior in order to correctly associate measurements with map data. In periodic environments, such as highways, localization results are often ambiguous - in particular in longitudinal direction. To still be able to verify an HD map, we propose the use of quasi-continuous 1D signals that can be computed without pointwise association. These signals can be chosen to change significantly when the map has changed while they only change rarely or slowly along the road, making them robust against localization errors. A spatio-semantic clustering yields intuitive groups of map features. These groups are then ordered using a robust projection approach, yielding quasi-continuous 1D signals. Such signals can be computed for map and measurement data and their comparison allows detecting road changes. The purposeful design of the signals and their computation only requires lane-level lateral localization and a coarse longitudinal prior, vastly relaxing the requirements on prior localization results compared to the current state of the art. With four example signals, we demonstrate the effectiveness of our approach on a map verification dataset [2], detecting between 49% and 98% of all changed features at false alarm rates usually below 15%. Detecting changes per feature allows to still use unchanged features for AD functions. When omitting this ability and aggregating all features, 98% of all changed road sections can be detected successfully.