Müller, Fabian ; Eggert, Julian (2021)
Time-Course Sensitive Collision Probability Model for Risk Estimation.
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). Rhodes, Greece (virtual conference) (20.-23.09.2020)
doi: 10.26083/tuprints-00019146
Konferenzveröffentlichung, Zweitveröffentlichung, Postprint
Kurzbeschreibung (Abstract)
Avoiding critical situations is a prerequisite for Advanced Driver Assistant Systems and Autonomous Driving to decrease the number of total hazards and fatal collisions. As a guide for safe motion behavior and for avoiding critical situations in complex scenarios with several interacting traffic participants, an appropriate risk measurement is necessary. It should incorporate system-inherent uncertainties like present in environment recognition, behavior predictions and physical model assumptions. In this paper, we introduce a time-course-aware incremental risk model for motion planning which predicts state distributions along forecasted trajectories and regards their magnitude evolution by the Survival Theory and their shape adaptation by removing collided distribution parts while preserving statistical moments. Our approach is able to reproduce motion risk probability costs as found by particle-based Monte-Carlo (MC) simulations in a range of scenarios, at much lower computational costs.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Müller, Fabian ; Eggert, Julian |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Time-Course Sensitive Collision Probability Model for Risk Estimation |
| Sprache: | Englisch |
| Publikationsjahr: | 2021 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2020 |
| Verlag: | IEEE |
| Kollation: | 8 ungezählte Seiten |
| Veranstaltungstitel: | 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) |
| Veranstaltungsort: | Rhodes, Greece (virtual conference) |
| Veranstaltungsdatum: | 20.-23.09.2020 |
| DOI: | 10.26083/tuprints-00019146 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/19146 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (Abstract): | Avoiding critical situations is a prerequisite for Advanced Driver Assistant Systems and Autonomous Driving to decrease the number of total hazards and fatal collisions. As a guide for safe motion behavior and for avoiding critical situations in complex scenarios with several interacting traffic participants, an appropriate risk measurement is necessary. It should incorporate system-inherent uncertainties like present in environment recognition, behavior predictions and physical model assumptions. In this paper, we introduce a time-course-aware incremental risk model for motion planning which predicts state distributions along forecasted trajectories and regards their magnitude evolution by the Survival Theory and their shape adaptation by removing collided distribution parts while preserving statistical moments. Our approach is able to reproduce motion risk probability costs as found by particle-based Monte-Carlo (MC) simulations in a range of scenarios, at much lower computational costs. |
| Status: | Postprint |
| URN: | urn:nbn:de:tuda-tuprints-191465 |
| 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 12:07 |
| Letzte Änderung: | 20 Jul 2021 07:37 |
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