Gaussian mixture probability hypothesis density filter with dynamic probabilities: Application to road traffic surveillance. (January 2023)

• Record Type: Journal Article
• Authors: Sun, Yi-Chieh; Hwang, Inseok
• Is Part Of: European journal of control. Volume 69(2023)
• Publication Date: 2023-01
• Subjects: Multiple-target tracking -- State-dependent -- GM-PHD filter -- Semantic information -- Occlusion; Control theory -- Periodicals; Automatic control -- Periodicals; Automatic control -- Mathematics -- Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.ejcon.2022.100761
• Languages: English
• ISSNs: 0947-3580

Abstract

Abstract: With the development of autonomous driving system, accurate tracking of the number of vehicles and their states is critical to ensure traffic safety. Since the numbers and states of the vehicles are unknown and time-varying, the road traffic surveillance can be considered as a multiple-target tracking (MTT) problem. The Gaussian mixture probability hypothesis density (GM-PHD) filter is a popular approach in solving MTT problem and has been successfully applied to a range of applications including ground vehicle tracking. However, due to the road constraint, the maneuverability of a ground vehicle is limited to the road lane. Thus, the target detection probability, survival probability, and birth rate, which are assumed to be constant in the original GM-PHD filter, vary according to the surveillance area as well as its own state. To address this, we propose an algorithm that dynamically updates the probability of detection and survival, and birth rate simultaneously in a mathematically rigorous way. This is done by integrating the semantic information inferred from the environment with the state-dependent GM-PHD filter. The performance and effectiveness of the proposed algorithm are demonstrated via illustrative road traffic scenarios.