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Accident Scenario Generation with Recurrent Neural Networks

Conference paper
Authors Ian Rhys Jenkins
Ludvig Oliver Gee
Alessia Knauss
Hang Yin
Jan Schröder
Published in IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Publication year 2018
Published at Department of Computer Science and Engineering (GU)
Language en
Subject categories


© 2018 IEEE. There is a lack of approaches to derive accident scenarios automatically for the testing of Autonomous Drive systems. Current approaches that generate test scenarios do not scale due to the manual work required. Machine learning provides the possibility to automate such tasks. In this paper, an automated approach based on Recurrent Neural Networks to generate accident scenarios is presented. Based on a prototype, our approach is evaluated on temporal data from simulated in-vehicle and V2X data to automatically generate new accident scenarios. The results confirm that generated scenarios resemble the accidents that took place in an exclusive test set.

Page Manager: Webmaster|Last update: 9/11/2012

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