Eggert, Julian ; Puphal, Tim (2022)
Continuous Risk Measures for Driving Support.
In: International Journal of Automotive Engineering, 2018, 9 (3)
doi: 10.26083/tuprints-00022385
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
In this paper, we compare three different model-based risk measures by evaluating their stengths and weaknesses qualitatively and testing them quantitatively on a set of real longitudinal and intersection scenarios. We start with the traditional heuristic Time-To-Collision (TTC), which we extend towards 2D operation and non-crash cases to retrieve the Time-To-Closest-Encounter (TTCE). The second risk measure models position uncertainty with a Gaussian distribution and uses spatial occupancy probabilities for collision risks. We then derive a novel risk measure based on the statistics of sparse critical events and so-called “survival” conditions. The resulting survival analysis shows to have an earlier detection time of crashes and less false positive detections in near-crash and non-crash cases supported by its solid theoretical grounding. It can be seen as a generalization of TTCE and the Gaussian method which is suitable for the validation of ADAS and AD.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Eggert, Julian ; Puphal, Tim |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Continuous Risk Measures for Driving Support |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2018 |
| Verlag: | Society of Automotive Engineers of Japan |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | International Journal of Automotive Engineering |
| Jahrgang/Volume einer Zeitschrift: | 9 |
| (Heft-)Nummer: | 3 |
| DOI: | 10.26083/tuprints-00022385 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/22385 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (Abstract): | In this paper, we compare three different model-based risk measures by evaluating their stengths and weaknesses qualitatively and testing them quantitatively on a set of real longitudinal and intersection scenarios. We start with the traditional heuristic Time-To-Collision (TTC), which we extend towards 2D operation and non-crash cases to retrieve the Time-To-Closest-Encounter (TTCE). The second risk measure models position uncertainty with a Gaussian distribution and uses spatial occupancy probabilities for collision risks. We then derive a novel risk measure based on the statistics of sparse critical events and so-called “survival” conditions. The resulting survival analysis shows to have an earlier detection time of crashes and less false positive detections in near-crash and non-crash cases supported by its solid theoretical grounding. It can be seen as a generalization of TTCE and the Gaussian method which is suitable for the validation of ADAS and AD. |
| Freie Schlagworte: | Safety, Risk Indicators, 2D Risk Measures, Risk Measures, Predictive Risk, Prediction Uncertainty, TTX, Time-To-Collision, TTC, Gaussian Collision Probability, Statistics of Sparse Events, Inhomogenous Poisson Processes, Survival Function, VI-DAS |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-223854 |
| 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 Intelligente Systeme |
| Hinterlegungsdatum: | 16 Sep 2022 12:26 |
| Letzte Änderung: | 19 Sep 2022 13:06 |
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| Suche nach Titel in: | TUfind oder in Google |
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