Multi-vehicle tracking with microscopic traffic flow model-based particle filtering. (July 2019)

• Record Type: Journal Article
• Authors: Song, Dan; Tharmarasa, Ratnasingham; Florea, Mihai C.; Duclos-Hindie, Nicolas; Fernando, Xavier N.; Kirubarajan, Thiagalingam
• Is Part Of: Automatica. Volume 105(2019)
• Page Start: 28
• Page End: 35
• Publication Date: 2019-07
• Subjects: Microscopic traffic flow model -- Particle filter -- Posterior Cramer–Rao lower bound -- Road traffic -- Multi-vehicle tracking; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2019.03.016
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: This paper addresses the problem of tracking multiple vehicles on multi-lane roads with consideration for interactions among vehicles. Due to limited lane resources and traffic heterogeneity, vehicles have to interact with neighboring vehicles while moving along roads to improve their navigability and safety, resulting in highly dependent motions. However, multitarget tracking algorithms generally assume that targets move independently of one another. To address this limitation, using the microscopic traffic flow (MTF) model for modeling vehicle dynamics in the presence of interactions with surrounding traffic, an MTF-based tracking algorithm is proposed under the particle filter (PF) framework. The recursive maximum likelihood (RML) method is integrated into the PF to estimate unknown parameters in the MTF model. The posterior Cramer–Rao lower bound (PCRLB) is also derived for this problem. The performance of the proposed MTF-PF algorithm is compared with those of existing algorithms for multi-vehicle tracking on multi-lane roads. Numerical results show that the proposed algorithm requires less prior information while yielding more accurate and consistent tracks.