Novel Vehicle Bounding Box Tracking Using a Low-End 3D Laser Scanner

Abstract

Vehicle bounding box tracking (VBBT) is a new problem that is becoming increasingly important in autonomous driving. It is defined as a problem in which not only the position but also the size of a target vehicle is estimated using a sensor. In this paper, novel VBBT using a low-end three-dimensional (3D) laser scanner is proposed. Compared to previous methods, the proposed VBBT has three distinctions: (1) the center of a rectangular vehicle is defined as its position, and the motion model that uses the center of the vehicle as the state is developed; (2) a new measurement model is proposed that models the measured size of the target vehicle as a sample from a uniform distribution; and (3) a Bayesian filter for the proposed motion and measurement model is developed and it is named as the Pareto Kalman filter (PKF). Finally, the proposed method is applied to six scenarios, and its validity is demonstrated through experimentation.