A Runtime-Efficient Multi-Object Tracking Approach for Automotive Perception Systems

There is an increasing demand for fully autonomous driving with the spread of advanced driver assistance systems. However, a higher level of automation requires an enhanced environment perception system. The automotive smart sensors detecting the surrounding objects are usually subject to various un...

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Published in:SACI (International Symposium on Applied Computational Intelligence and Informatics. Online) pp. 000785 - 000792
Main Authors: Lindenmaier, Laszlo, Czibere, Balazs, Aradi, Szilard, Becsi, Tamas
Format: Conference Proceeding
Language:English
Published: IEEE 23.05.2023
Subjects:
Automation
Automotive applications
Computational complexity
Environment perception
Informatics
Multi-object tracking
Nearest neighbor methods
Noise measurement
Object detection
Real-time systems
Uncertainty
ISSN:2765-818X
Online Access:Get full text
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Abstract There is an increasing demand for fully autonomous driving with the spread of advanced driver assistance systems. However, a higher level of automation requires an enhanced environment perception system. The automotive smart sensors detecting the surrounding objects are usually subject to various uncertainties. Objects are sometimes misdetected, false detections may also occur, and the sensor measurements are noisy. Multi-object tracking algorithms aim to compensate for these uncertainties, estimating precisely and continuously the state of surrounding objects. The real-time execution of these algorithms is crucial in automotive applications. This paper presents an improved runtime-efficient local nearest neighbor-based approach. The performance metrics of the proposed method get close to the state-of-the-art algorithms, besides having significantly lower computational complexity.
AbstractList There is an increasing demand for fully autonomous driving with the spread of advanced driver assistance systems. However, a higher level of automation requires an enhanced environment perception system. The automotive smart sensors detecting the surrounding objects are usually subject to various uncertainties. Objects are sometimes misdetected, false detections may also occur, and the sensor measurements are noisy. Multi-object tracking algorithms aim to compensate for these uncertainties, estimating precisely and continuously the state of surrounding objects. The real-time execution of these algorithms is crucial in automotive applications. This paper presents an improved runtime-efficient local nearest neighbor-based approach. The performance metrics of the proposed method get close to the state-of-the-art algorithms, besides having significantly lower computational complexity.
Author Czibere, Balazs
Becsi, Tamas
Lindenmaier, Laszlo
Aradi, Szilard
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  fullname: Becsi, Tamas
  email: becsi.tamas@kjk.bme.hu
  organization: Budapest University of Technology and Economics,Dept. of Control for Transportation and Vehicle Systems,Budapest,Hungary
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Snippet There is an increasing demand for fully autonomous driving with the spread of advanced driver assistance systems. However, a higher level of automation...
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SubjectTerms Automation
Automotive applications
Computational complexity
Environment perception
Informatics
Multi-object tracking
Nearest neighbor methods
Noise measurement
Object detection
Real-time systems
Uncertainty
Title A Runtime-Efficient Multi-Object Tracking Approach for Automotive Perception Systems
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