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Automated vehicle system architecture with performance assessment

 
: Tas, Ömer Sahin; Hörmann, Stefan; Schäufele, Bernd; Kuhnt, Florian

:

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017 : 16-19 October 2017, Yokohama, Japan
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-5386-1526-3
ISBN: 978-1-5386-1525-6
ISBN: 978-1-5386-1527-0
8 S.
International Conference on Intelligent Transportation Systems (ITSC) <20, 2017, Yokohama>
European Commission EC
H2020; 661933; RobustSENSE
Robust and Reliable Environment Sensing and Situation Prediction for Advanced Driver Assistance Systems and Automated Driving
Englisch
Konferenzbeitrag
Fraunhofer FOKUS ()

Abstract
This paper proposes a reference architecture to increase reliability and robustness of an automated vehicle. The architecture exploits the benefits arising from the in-terdependencies of the system and provides self awareness. Performance Assessment units attached to subsystems quantify the reliability of their operation and return performance values. The Environment Condition Assessment, which is another important novelty of the architecture, informs augmented sensors on current sensing conditions. Utilizing environment conditions and performance values for subsequent centralized integrity checks allow algorithms to adapt to current driving conditions and thereby to increase their robustness. We demonstrate the benefit of the approach with the example of false positive object detection and tracking, where the detection of a ghost object is resolved in centralized performance assessment using a Bayesian network.

: http://publica.fraunhofer.de/dokumente/N-487649.html