Adaptive clustering for contour estimation of vehicles for high-resolution radar

Future automotive radars will be able to achieve much higher range and angular resolution compared to currently used radar sensors. This enables functionalities like vehicle contour estimation to be used in advanced driver assistance systems, thus heavily increasing their performance. In this paper, the application of an adaptive algorithm on basis of k-nearest-neighbours examination for clustering radar data as precursor to estimation of width, length, and position of vehicles is presented and compared to a more basic algorithm. The influence of the parameters of this KNN-DBSCAN algorithm and its performance dependency on the used MIMO radar system is discussed.