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GM-PHD Filter Based Sensor Data Fusion for Automotive Frontal Perception System.
- Source :
-
IEEE Transactions on Vehicular Technology . Jul2022, Vol. 71 Issue 7, p7215-7229. 15p. - Publication Year :
- 2022
-
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 time-varying 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189545
- Volume :
- 71
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Vehicular Technology
- Publication Type :
- Academic Journal
- Accession number :
- 158023192
- Full Text :
- https://doi.org/10.1109/TVT.2022.3171040