GM-PHD Filter Based Sensor Data Fusion for Automotive Frontal Perception System

Lindenmaier, László and Aradi, Szilárd and Bécsi, Tamás and Törő, Olivér and Gáspár, Péter (2022) GM-PHD Filter Based Sensor Data Fusion for Automotive Frontal Perception System. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 71 (7). pp. 7215-7229. ISSN 0018-9545 10.1109/TVT.2022.3171040


Download (3MB) | Preview


Advanced driver assistance systems and highly automated driving functions require an enhanced frontal perception system. The requirements of a frontal environment perception system cannot be satisfied by either of the existing automotive sensors. A commonly used sensor cluster for these functions consists of a mono-vision smart camera and automotive radar. The sensor fusion is intended to combine the data of these sensors to perform a robust environment perception. Multi-object tracking algorithms have a suitable software architecture for sensor data fusion. Several multi-object tracking algorithms, such as JPDAF or MHT, have good tracking performance; however, the computational requirements of these algorithms are significant according to their combinatorial complexity. The GM-PHD filter is a straightforward algorithm with favorable runtime characteristics that can track an unknown and timevarying number of objects. However, the conventional GM-PHD filter has a poor performance in object cardinality estimation. This paper proposes a method that extends the GM-PHD filter with an object birth model that relies on the sensor detections and a robust object extraction module, including Bayesian estimation of objects’ existence probability to compensate for drawbacks of the conventional algorithm.

Item Type: Article
Uncontrolled Keywords: Object Detection; Gaussian Mixture Model; Sensor fusion; Multi-object tracking; PHD filter; radar detection; Advanced driver assistance; Smart cameras;
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: 27 Jan 2023 07:51
Last Modified: 11 Sep 2023 15:00

Update Item Update Item