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Constraint-Based Hierarchical Cluster Selection in Automotive Radar Data.

Claudia Malzer1,2, Marcus Baum1

  • 1Data Fusion Group, Institute of Computer Science, University of Göttingen, 37077 Göttingen, Germany.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary

Hierarchical DBSCAN (HDBSCAN) with cluster-level constraints improves automotive radar object detection. This method enhances clustering of radar measurements for autonomous driving, outperforming unsupervised approaches.

Keywords:
HDBSCANautomotive radarconstraint-based clusteringhierarchical clusteringsemi-supervised clustering

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Area of Science:

  • Computer Vision
  • Sensor Fusion
  • Autonomous Driving Systems

Background:

  • High-resolution automotive radar sensors are crucial for detecting and tracking objects in traffic.
  • Clustering radar detection points is challenging due to varying data density and number across scans.
  • Density-based spatial clustering of applications with noise (DBSCAN) variants are common, but hierarchical methods are less explored.

Purpose of the Study:

  • To evaluate the suitability of HDBSCAN for clustering automotive radar measurements.
  • To enhance unsupervised HDBSCAN performance using cluster-level constraints and distance thresholds.
  • To compare constraint-based HDBSCAN with semi-supervised and unsupervised methods on real-world driving data.

Main Methods:

  • Applied HDBSCAN, a hierarchical clustering algorithm, to radar measurements from the nuScenes dataset.
  • Introduced cluster-level constraints using aggregated background information from cluster candidates.
  • Implemented a distance threshold to mitigate the selection of small, low-level clusters.
  • Evaluated performance against unsupervised HDBSCAN and label-based semi-supervised HDBSCAN.

Main Results:

  • Constraint-based HDBSCAN significantly improved clustering results compared to the unsupervised version.
  • The proposed constraints effectively adapted HDBSCAN to the specific context of automotive radar data.
  • Carefully selected constraints are vital for optimal performance in dynamic traffic environments.

Conclusions:

  • HDBSCAN, enhanced with cluster-level constraints, is a promising approach for automotive radar data clustering.
  • Constraint-based methods offer superior performance over unsupervised clustering for object detection in autonomous driving.
  • The effectiveness of constraints is environment-dependent and requires careful tuning for robust application.