21 May 2015 Study of the microdoppler signature of a bicyclist for different directions of approach
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The successful implementation of autonomous driving in an urban setting depends on the ability of the environment perception system to correctly classify vulnerable road users such as pedestrians and bicyclists in dense, complex scenarios. Self-driving vehicles include sensor systems such as cameras, lidars, and radars to enable decision making. Among these systems, radars are particularly relevant due to their operational robustness under adverse weather and night light conditions. Classification of pedestrian and car in urban settings using automotive radar has been widely investigated, suggesting that micro-Doppler signatures are useful for target discrimination. Our objective is to analyze and study the micro-Doppler signature of bicyclists approaching a vehicle from different directions in order to establish the basis of a classification criterion to distinguish bicycles from other targets including clutter. The micro-Doppler signature is obtained by grouping individual reflecting points using a clustering algorithm and observing the evolution of all the points belonging to an object in the Doppler domain over time. A comparison is then made with simulated data that uses a kinematic model of bicyclists’ movement. The suitability of the micro-Doppler bicyclist signature as a classification feature is determined by comparing it to those belonging to cars and pedestrians approaching the automotive radar system.
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Berta Rodriguez-Hervas, Berta Rodriguez-Hervas, Michael Maile, Michael Maile, Benjamin C. Flores, Benjamin C. Flores, "Study of the microdoppler signature of a bicyclist for different directions of approach", Proc. SPIE 9461, Radar Sensor Technology XIX; and Active and Passive Signatures VI, 94611E (21 May 2015); doi: 10.1117/12.2087619; https://doi.org/10.1117/12.2087619


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