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Self-Aware LiDAR Sensors in Autonomous Systems using a Convolutional Neural Network

Qu, Junyu; Barton, David; Gönnheimer, Philipp; Pinsker, Florian; Kufer, Dominik; Fleischer, Jürgen

Abstract (englisch):
Autonomous systems, as found in autonomous driving and highly automated production systems, require an increased reliability in order to achieve their high economic potential. Self-aware sensors are a key component in highly reliable autonomous systems. In this paper we highlight a proof of concept (PoC) of a deep learning method that enables a LiDAR (Light detection and ranging) sensor to detect functional impairment. More specifically, a deep convolutional neural network (CNN) is developed and trained with labelled LiDAR data in the form of point clouds to classify the degree of impairment of its functionality. The results are statistically significant and can be regarded as a general classifier for objects within LiDAR data, applied to selected cases of sensor impairment. In detecting impairment and evaluating the correctness of the captured data, the sensor gains a basic form of self-awareness. The presented methods and insights pave the way for improved safety of autonomous systems by the means of more sophisticated “self-aware” neural networks.

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Verlagsausgabe §
DOI: 10.5445/IR/1000128026
Veröffentlicht am 04.01.2021
DOI: 10.1016/j.promfg.2020.11.010
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktionstechnik (WBK)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2351-9789
KITopen-ID: 1000128026
Erschienen in Procedia manufacturing
Verlag Elsevier
Band 52
Seiten 50–55
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
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