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Continuous Risk Measures for Driving Support

Eggert, Julian ; Puphal, Tim (2022)
Continuous Risk Measures for Driving Support.
In: International Journal of Automotive Engineering, 9 (3)
doi: 10.26083/tuprints-00022385
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Continuous Risk Measures for Driving Support
Language: English
Date: 2022
Place of Publication: Darmstadt
Publisher: Society of Automotive Engineers of Japan
Journal or Publication Title: International Journal of Automotive Engineering
Volume of the journal: 9
Issue Number: 3
DOI: 10.26083/tuprints-00022385
Corresponding Links:
Origin: Secondary publication service

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.

Uncontrolled Keywords: 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: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-223854
Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control Methods and Intelligent Systems
Date Deposited: 16 Sep 2022 12:26
Last Modified: 21 Apr 2023 12:46
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/22385
PPN: 507154150
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