Robust Object Tracking Against Sensor Failures With Centralized IMM Filter

Abstract

Highly automated driving requires the use of multiple sensors for reliable tracking functionality. In response to the requirement, the proposed method modifies the conventional Interacting Multiple Model (IMM) filter to fuse multi-sensor data by utilizing the independence of observations. In addition, the proposed IMM is integrated with a Centralized Kalman Filter (CKF) that ensures track continuity against sensor failures, providing optimal state estimates. When tracking objects in a moving reference frame, such as in autonomous vehicles, onboard sensor measurements represent relative values, making it challenging to estimate the actual motion of objects. While transforming states to a global coordinates is a solution, the solution can arise another problem where the tracking results depends on the status and performance of the localization. Therefore, to tackle the problem, a track compensation algorithm utilizing a hybrid coordinate system is proposed. The actual motions of objects are estimated based on errors between the track state and the associated measurement. The performance of the proposed algorithms is demonstrated using experimental scenario data conducted with an actual vehicle.