Multi-Object Tracking Strategy of Autonomous Vehicle Using Modiﬁed Unscented Kalman Filter and Reference Point Switching

doi:10.1007/s12204-021-2350-0

Abstract

Abstract: In this study, a multi-object tracking (MOT) scheme based on a light detection and ranging sensor was proposed to overcome imprecise velocity observations in object occlusion scenarios. By applying real-time velocity estimation, a modified unscented Kalman filter (UKF) was proposed for the state estimation of a target object. The proposed method can reduce the calculation cost by obviating unscented transformations. Additionally, combined with the advantages of a two-reference-point selection scheme based on a center point and a corner point, a reference point switching approach was introduced to improve tracking accuracy and consistency. The state estimation capability of the proposed UKF was verified by comparing it with the standard UKF in single-target tracking simulations. Moreover, the performance of the proposed MOT system was evaluated using real traffic datasets.

Key words: multi-object tracking (MOT), light detection and ranging (LiDAR) sensor, unscented Kalman ?lter (UKF), object occlusion

CLC Number:

U 461

WANG Muyuan∗ (王木塬), WU Xiaodong (吴晓东). Multi-Object Tracking Strategy of Autonomous Vehicle Using Modiﬁed Unscented Kalman Filter and Reference Point Switching[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(5): 607-614.

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