The Online Vehicle Type Classifier Design for Road-Side Radar Detectors

Abstract

This study presents an online vehicle type classifier for road-side radar detectors in multi-lane environments. An automatic learning framework which composes a parametric statistic model and algorithms is introduced. The parameters of an online vehicle type classifier are trained with vehicles passing in front of detectors. The online vehicle type classifier tries to identify the vehicle type in real time. The road-side radar detector is developed based on frequency-modulation continuous-wave (FMCW) radar with the carrier frequency at X-band. Vehicles are classified into two major categories, large and small. The classification based on (i) average energy maximum and (ii) average energy variance, that are extracted from the frequency-domain signatures caused by passed vehicles. A two-dimension Gaussian Mixed Model (denoted as GMM) is employed to develop the learning model. Expectation maximization (denoted EM) algorithm is implemented to obtain the parameters of GMM. Numerical examples are demonstrated with real-world experiments. In the field tests, the automatic framework delivers an accuracy of minimum 88%, even with extremes scenarios (including (i) small samples and (ii) large sample size difference of different vehicle types). The examples show satisfying results of the proposed online vehicle type classifier.