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http://dx.doi.org/10.25673/95740
Titel: | Prediction based activation of vehicle safety systems : a contribution to improve occupant safety by validation of pre-crash information and crash severity plus restraint strategy prediction |
Autor(en): | Sequeira, Gerald Joy Alphonso![]() |
Gutachter: | Jumar, Ulrich![]() |
Körperschaft: | Otto-von-Guericke-Universität Magdeburg, Fakultät für Elektrotechnik und Informationstechnik |
Erscheinungsdatum: | 2022 |
Umfang: | X, 156 Seiten |
Typ: | Hochschulschrift![]() |
Art: | Dissertation |
Tag der Verteidigung: | 2022 |
Sprache: | Englisch |
Herausgeber: | VDI Verlag, Düsseldorf |
Serie/Report Nr.: | Fortschriftt-Berichte VDI![]() |
URN: | urn:nbn:de:gbv:ma9:1-1981185920-976972 |
Schlagwörter: | Predictive Safety System Contour estimation Crash Severity prediction Restraint Strategy prediction Crash Validation |
Zusammenfassung: | The world of transportation is rapidly changing with the introduction of partial autonomy in vehicles and the race between the manufacturers to produce a fully automated passenger vehicle. In addition, to enhance driving comfort and reduce the driving workload, these automated vehicles are also visualized as an approach to reduce the majority of accidents that are caused by human errors. However, accidents do happen and there are also some likelihoods that these automated vehicles might fail. Especially in the initial introductory years, which highlights the need for passive safety systems in safeguarding the occupants. These vehicles typically employ forward-looking sensors for the perception of the surrounding environment, which presents an opportunity to use the information from these sensors to predict an upcoming inevitable crash and further estimate the passive safety action required for the predicted crash in the pre-crash phase. This work presents an approach to activate the vehicle safety systems based on the precrash prediction. Contents 1 Introduction 1 1.1 |
URI: | https://opendata.uni-halle.de//handle/1981185920/97697 http://dx.doi.org/10.25673/95740 |
Open-Access: | ![]() |
Nutzungslizenz: | ![]() |
Enthalten in den Sammlungen: | Fakultät für Elektrotechnik und Informationstechnik |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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Sequeira_Gerald_Disseration_2022.pdf | Dissertation | 46.9 MB | Adobe PDF | ![]() Öffnen/Anzeigen |