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
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Abstract
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 |
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| 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 |
| URI: | https://eprints.sztaki.hu/id/eprint/10490 |
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