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Time-Course Sensitive Collision Probability Model for Risk Estimation

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
Conference or Workshop Item, Secondary publication, Postprint

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Item Type: Conference or Workshop Item
Type of entry: Secondary publication
Title: Time-Course Sensitive Collision Probability Model for Risk Estimation
Language: English
Date: 2021
Place of Publication: Darmstadt
Year of primary publication: 2020
Publisher: IEEE
Collation: 8 ungezählte Seiten
Event Title: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
Event Location: Rhodes, Greece (virtual conference)
Event Dates: 20.-23.09.2020
DOI: 10.26083/tuprints-00019146
Corresponding Links:
Origin: Secondary publication service
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
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 Robotics (from 01.08.2022 renamed Control Methods and Intelligent Systems)
Date Deposited: 14 Jul 2021 12:07
Last Modified: 25 Nov 2022 14:33
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/19146
PPN: 483252867
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