For the realization of autonomous driving, early detection and classification of vulnerable road users such as pedestrians is indispensable. High-resolution radars in the short range enable the detection of pedestrian-identifying micro-Doppler distributions. Especially the limb motion can provide fundamental features, which can support the design of automotive safety systems. This paper presents a detailed analysis of high-resolution human micro-Doppler signatures in terms of their composition by characeristic limb movements using synchronized measurements of a motion capture system. The use of state-of-the-art radar sensor technology as well as signal processing including clustering and OS-CFAR filtering leads to comparisons of simultaneously captured movements with the associated micro-Doppler signatures and provides new insights in the detailed radar-based analysis of the human gait. The potential feasibility to detect characteristic features for predictive algorithms is discussed and analyzed on the basis of realistic automotive scenarios such as road crossing movements.
Radar-Based Analysis of Pedestrian Micro-Doppler Signatures Using Motion Capture Sensors
2018-06-01
4302715 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
TIBKAT | 2022
|