Bóka, Jenő and Lindenmaier, László and Aradi, Szilárd and Bécsi, Tamás (2026) A Novel Sensor Model-Based Object Existence Probability Fusion for Automotive Track-to-Track Fusion. IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY, 7. pp. 1429-1443. ISSN 2644-1330 10.1109/OJVT.2026.3692885

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Abstract

Environment perception—and, in particular, multi-sensor fusion—plays a crucial role in ensuring the reliability of advanced driver assistance systems. Track-to-track fusion principles are widely adopted in automotive applications to combine object tracks generated by smart sensors. Within these systems, track management is a key component, as it strongly influences performance through track initiation and false-track suppression. State-of-the-art approaches address this problem by estimating and fusing the existence probabilities of tracks. Consequently, robust fusion of sensor-level existence estimates is essential for reliable track-to-track methods. In this paper, we extend our previous two-step hybrid existence probability fusion framework with probabilistic tracking and birth models. The proposed tracking model incorporates target velocity in addition to the sensor field of view, while the birth model captures the spatial context in which new objects are likely to emerge, enabling faster confirmation and improved false-track suppression. Experimental results obtained with a conventional radar–camera frontal perception setup demonstrate that integrating the proposed sensor models yields more than a 4% overall performance improvement, even within a standard one-step Dempster–Shafer belief fusion framework. © 2020 IEEE.

Item Type:	Article
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Systems and Control Lab
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	09 Sep 2026 09:06
Last Modified:	09 Sep 2026 09:06
URI:	https://eprints.sztaki.hu/id/eprint/11134

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