Eggert, Julian ; Müller, Fabian (2021)
A Foresighted Driver Model derived from Integral Expected Risk.
2019 IEEE Intelligent Transportation Systems Conference (ITSC). Auckland, New Zealand (27.-30.10.2019)
doi: 10.26083/tuprints-00019147
Konferenzveröffentlichung, Zweitveröffentlichung, Postprint
Kurzbeschreibung (Abstract)
Current efforts in Advanced Driver Assistant Systems and Autonomous Driving research target at making the vehicles more intelligent, in terms of understanding what is going on and selecting the most appropriate behaviors. A crucial element of this research is the prediction of the evolution of the current driving situation with microscopic driver models. In this paper we present a microscopic driver model with a gradient-like, simple behavior generation that is fully and concisely derived from mathematical risk theory. Following this model, drivers act by estimating the expected, integral future risks and benefits and by seeking the best instantaneous tradeoff between these quantities, choosing the immediate action that reduces the hypothetical risks in the most efficient way. We show how this model is able to incorporate different risk types and situation parameters, allowing an extension and generalization to variable scenarios.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Eggert, Julian ; Müller, Fabian |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | A Foresighted Driver Model derived from Integral Expected Risk |
| Sprache: | Englisch |
| Publikationsjahr: | 2021 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2019 |
| Verlag: | IEEE |
| Buchtitel: | ITSC 2019 Conference Proceedings |
| Kollation: | 8 ungezählte Seiten |
| Veranstaltungstitel: | 2019 IEEE Intelligent Transportation Systems Conference (ITSC) |
| Veranstaltungsort: | Auckland, New Zealand |
| Veranstaltungsdatum: | 27.-30.10.2019 |
| DOI: | 10.26083/tuprints-00019147 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/19147 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (Abstract): | Current efforts in Advanced Driver Assistant Systems and Autonomous Driving research target at making the vehicles more intelligent, in terms of understanding what is going on and selecting the most appropriate behaviors. A crucial element of this research is the prediction of the evolution of the current driving situation with microscopic driver models. In this paper we present a microscopic driver model with a gradient-like, simple behavior generation that is fully and concisely derived from mathematical risk theory. Following this model, drivers act by estimating the expected, integral future risks and benefits and by seeking the best instantaneous tradeoff between these quantities, choosing the immediate action that reduces the hypothetical risks in the most efficient way. We show how this model is able to incorporate different risk types and situation parameters, allowing an extension and generalization to variable scenarios. |
| Status: | Postprint |
| URN: | urn:nbn:de:tuda-tuprints-191477 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik > Regelungsmethoden und Robotik (ab 01.08.2022 umbenannt in Regelungsmethoden und Intelligente Systeme) |
| Hinterlegungsdatum: | 14 Jul 2021 12:11 |
| Letzte Änderung: | 20 Jul 2021 07:32 |
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