Müller, Fabian ; Eggert, Julian (2021)
Behaviour investigation of a risk-aware driving model for trajectory prediction.
5th International Symposium on Future Active Safety Technology toward Zero Accidents (FAST-zero ’19). Blacksburg, VA, USA (09.09.2019-11.09.2019)
doi: 10.26083/tuprints-00020255
Konferenzveröffentlichung, Zweitveröffentlichung, Verlagsversion
 | Es ist eine neuere Version dieses Eintrags verfügbar. |
Kurzbeschreibung (Abstract)
The prevention of risky situations is one of the main tasks in autonomous driving (AD) and intelligent driving assistant systems (ADAS). Uncertainty in the traffic participants’ behavior and the sensor measurements leads to critical situations, which have to be anticipated by appropriate risk prediction approaches. The risk prediction itself requires dedicated driver models which are interaction sensitive and computationally cheap, to efficiently simulate how a scene might evolve. In this paper, we present a new driver model which is aware of the usual risks encountered in normal driving scenarios. It can cope with longitudinal as well as lateral collision risks, and adjusts its behavior by minimizing the expected integral risk. We show how our model is suited for coping with parallel lane scenarios like overtaking, following and in-between positioning by analyzing its behavior and stability.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Müller, Fabian ; Eggert, Julian |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Behaviour investigation of a risk-aware driving model for trajectory prediction |
| Sprache: | Englisch |
| Publikationsjahr: | 2021 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2021 |
| Buchtitel: | Proceedings of the 5th International Symposium on Future Active Safety Technology toward Zero Accidents |
| Kollation: | 8 Seiten |
| Veranstaltungstitel: | 5th International Symposium on Future Active Safety Technology toward Zero Accidents (FAST-zero ’19) |
| Veranstaltungsort: | Blacksburg, VA, USA |
| Veranstaltungsdatum: | 09.09.2019-11.09.2019 |
| DOI: | 10.26083/tuprints-00020255 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/20255 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (Abstract): | The prevention of risky situations is one of the main tasks in autonomous driving (AD) and intelligent driving assistant systems (ADAS). Uncertainty in the traffic participants’ behavior and the sensor measurements leads to critical situations, which have to be anticipated by appropriate risk prediction approaches. The risk prediction itself requires dedicated driver models which are interaction sensitive and computationally cheap, to efficiently simulate how a scene might evolve. In this paper, we present a new driver model which is aware of the usual risks encountered in normal driving scenarios. It can cope with longitudinal as well as lateral collision risks, and adjusts its behavior by minimizing the expected integral risk. We show how our model is suited for coping with parallel lane scenarios like overtaking, following and in-between positioning by analyzing its behavior and stability. |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-202551 |
| Zusätzliche Informationen: | Keywords: Risk Assessment, Safety, Trajectory Prediction, Trajectory Planning |
| 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: | 21 Dez 2021 13:08 |
| Letzte Änderung: | 22 Dez 2021 11:06 |
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| Suche nach Titel in: | TUfind oder in Google |
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