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A Foresighted Driver Model derived from Integral Expected Risk

Eggert, Julian ; Müller, Fabian (2021):
A Foresighted Driver Model derived from Integral Expected Risk. (Postprint)
In: ITSC 2019 Conference Proceedings,
Darmstadt, IEEE, 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 27.-30.10.2019, ISBN 978-1-5386-7024-8,
DOI: 10.26083/tuprints-00019147,
[Conference or Workshop Item]

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Item Type: Conference or Workshop Item
Origin: Secondary publication service
Status: Postprint
Title: A Foresighted Driver Model derived from Integral Expected Risk
Language: English

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.

Book Title: ITSC 2019 Conference Proceedings
Place of Publication: Darmstadt
Publisher: IEEE
Collation: 8 ungezählte Seiten
Classification DDC: 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Divisions: 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control Methods and Robotics
Event Title: 2019 IEEE Intelligent Transportation Systems Conference (ITSC)
Event Location: Auckland, New Zealand
Event Dates: 27.-30.10.2019
Date Deposited: 14 Jul 2021 12:11
Last Modified: 14 Jul 2021 12:11
DOI: 10.26083/tuprints-00019147
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-191477
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/19147
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