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http://dx.doi.org/10.12815/kits.2022.21.5.218

Doppler Velocity-based Dynamic Object Tracking and Rejection for Increasing Reliability of Radar Ego-Motion Estimation  

Park, Yeong Sang (Autonomous Driving Intelligence Research Section, ETRI)
Min, Kyoung-Wook (Autonomous Driving Intelligence Research Section, ETRI)
Choi, Jeong Dan (Intelligent Robotics Research Division, ETRI)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.21, no.5, 2022 , pp. 218-232 More about this Journal
Abstract
Researches are underway to use a radar sensor, a sensor used for object recognition in vehicles, for position estimation. In particular, a method of classifying dynamic and static objects using the Doppler velocity, the output from the radar sensor, and calculating ego-motion using only static objects has been researched recently. Also, for the existing dynamic object classification, several methods using RANSAC or robust filtering has been proposed. Still, a classification method with higher performance is needed due to the nature of the position estimation, in which even a single failure causes large effects. Hence, in this paper, we propose a method to improve the classification performance compared to existing methods through tracking and filtering of dynamic objects. Additionally, the method used a GMPHD filter to maximize tracking performance. In effect, the method showed higher performance in terms of classification accuracy compared to existing methods, and especially shows that the failure of the RANSAC could be prevented.
Keywords
Autonomous driving; Radar sensor; Dynamic object tracking; PHD filter;
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