High-resolution Radar Based Object Classification for Automotive Applications

  • This paper presents a method for classifying traffic participants based on high-resolution automotive radar sensors for autonomous driving applications. The major classes of traffic participants addressed in this work are pedestrians, bicyclists and passenger cars. The preprocessed radar detections are first segmented into distinct clusters using density-based spatial clustering of applications with noise (DBSCAN) algorithm. Each cluster of detections would typically have different properties based on the respective characteristics of the object that they originated from. Therefore, sixteen distinct features based on radar detections, that are suitable for separating pedestrians, bicyclists and passenger car categories are selected and extracted for each of the cluster. A support vector machine (SVM) classifier is constructed, trained and parametrised for distinguishing the road users based on the extracted features. Experiments are conducted to analyse the classification performance of the proposed method on real data.
Author:Ganesh Nageswaran
URN (permanent link):urn:nbn:de:hbz:386-kluedo-62798
Document Type:Working Paper
Language of publication:English
Publication Date:2021/03/01
Year of Publication:2016
Publishing Institute:Technische Universität Kaiserslautern
Date of the Publication (Server):2021/03/02
Tag:autonomous driving; clustering; machine learning; object classification; radar
Number of page:10
Faculties / Organisational entities:Fachbereich Maschinenbau und Verfahrenstechnik
DDC-Cassification:6 Technik, Medizin, angewandte Wissenschaften / 620 Ingenieurwissenschaften und Maschinenbau
Licence (German):Creative Commons 4.0 - Namensnennung (CC BY 4.0)