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http://dx.doi.org/10.22680/kasa2018.10.2.029

Quantitative Analysis of Automotive Radar-based Perception Algorithm for Autonomous Driving  

Lee, Hojoon (서울대학교 기계공학과)
Chae, HeungSeok (서울대학교 기계공학과)
Seo, Hotae (서울대학교 기계공학과)
Yi, Kyongsu (서울대학교 기계공학과)
Publication Information
Journal of Auto-vehicle Safety Association / v.10, no.2, 2018 , pp. 29-35 More about this Journal
Abstract
This paper presents a quantitative evaluation method and result of moving vehicle perception using automotive radar. It is also important to analyze the accuracy of the perception algorithm quantitatively as well as to accurately percept nearby moving vehicles for safe and efficient autonomous driving. In this study, accuracy of the automotive radar-based perception algorithm which is developed based on interacting multiple model (IMM) has been verified via vehicle tests on real roads. In order to obtain experimental data for quantitative evaluation, Long Range Radar (LRR) has been mounted on the front of the ego vehicle and Short Range Radar (SRR) has been mounted on the rear side of both sides. RT-range has been installed on the ego vehicle and the target vehicle to simultaneously collect reference data on the states of the two vehicles. The experimental data is acquired in various relative positions and velocity, and the accuracy of the algorithm has been analyzed according to relative position and velocity. Quantitative analysis is conducted on relative position, relative heading angle, absolute velocity, and yaw rate of each vehicle.
Keywords
Autonomous Driving; Moving Vehicle Perception; Point Cloud Processing; Quantitative Analysis; Automotive Radar Data Processing;
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