Classification of Dynamic Vulnerable Road Users Using a Polarimetric mm-Wave MIMO Radar

In this paper, the classification of dynamic Vulnerable Road Users (VRUs) using polarimetric automotive radar is considered. To this end, a signal processing pipeline for polarimetric automotive MIMO radar is proposed, including a method to enhance angular resolution by combining data from all polarimetric channels. The proposed signal processing pipeline is applied to measurement data of three different types of VRUs and a car, collected with a custom automotive polarimetric radar, developed in collaboration with Huber+Suhner AG. Several polarimetric features are estimated from the range-velocity signatures of the measured targets and are subsequently analysed. A Bayesian classifier and a Convolutional Neural Network (CNN) using these estimated polarimetric features are proposed and their performance is compared against their single-polarised counterparts. It is found that for the Bayesian classifier, a significant increase in classification performance is achieved, compared to the same classifier using single polarised information. For the CNN-based classifier, utilising the distribution of polarimetric features of the target’s range-velocity signatures also increases classification performance, compared to its single-polarised version. This shows that polarimetric information is valuable for classification of VRUs and objects of interest in automotive.