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Evaluation of Cluster Algorithms for Radar-Based Object Recognition in Autonomous and Assisted Driving.

Daniel Carvalho de Ramos¹, Lucas Reksua Ferreira¹, Max Mauro Dias Santos¹

¹Department of Electronic, Federal Technological University of Paraná, Ponta Grossa 84017-220, PR, Brazil.

Sensors (Basel, Switzerland)

|November 27, 2024

Summary

This summary is machine-generated.

This study evaluates clustering algorithms for automotive radar object recognition. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) demonstrated superior performance for reliable vehicle perception systems.

Keywords:

DBSCAN K-Means Mean-Shift automotive applications autonomous vehicles cluster algorithms driving assistance object identification perception systems point clouds radar sensor

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Area of Science:

Automotive Engineering
Computer Vision
Sensor Fusion

Background:

Perception systems are crucial for assisted driving and autonomous vehicles.
Sensors like RADAR, cameras, and LIDAR provide environmental data for navigation.
Radar technology offers robust object detection, especially in adverse weather conditions, using point cloud data.

Purpose of the Study:

To evaluate the suitability of various clustering algorithms for automotive radar systems.
To identify which algorithms are most effective for object identification, investigation, and tracking.
To compare the performance of different clustering methods in the context of radar-based perception.

Main Methods:

A comprehensive review of current clustering algorithms was conducted.
The mathematical underpinnings of each algorithm were analyzed.
Performance indicators were used to assess suitability and efficiency for radar applications.

Main Results:

K-Means, Mean Shift, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) were found suitable for automotive radar.
DBSCAN exhibited superior performance compared to other evaluated algorithms.
The type of radar sensor significantly influences the effectiveness of object recognition methods.

Conclusions:

DBSCAN is a highly effective algorithm for object recognition in automotive radar systems.
The selection of appropriate clustering algorithms is vital for robust autonomous driving perception.
Future research should consider the interplay between radar hardware and software algorithms.