Centralized Multi-Sensor Poisson Multi-Bernoulli Mixture Tracker for Autonomous Driving

Abstract

With recent advances in Advanced Driver Assistance Systems (ADAS), autonomous driving has increased the need for reliable perception techniques. To achieve reliability, automotive sensors are being applied to autonomous driving vehicles, such as cameras, LiDAR, and radars. Various methods for fusing sensors have been studied to increase performance. In this study, we propose a centralized multi-sensor tracker, which is a first attempt to take advantage of fusing heterogeneous onboard sensors while accounting for data uncertainties. The proposed approach uses a Random Finite Set based Poisson Multi-Bernoulli Mixture filter. Experimental results from an actual vehicle dataset show that the proposed method tracks accurately even when objects are occluded or overlapped. It demonstrates the capability of tracking objects for autonomous driving in an urban environment.