Paper_Nitsche_v06_revision.pdf (1.99 MB)
Download fileA novel, modular validation framework for collision avoidance of automated vehicles at road junctions
conference contribution
posted on 2019-03-05, 08:58 authored by Philippe Nitsche, Ruth WelshRuth Welsh, A. Genser, Pete ThomasThis paper presents a new validation method for automated driving systems at road junctions. The method comprises the clustering of critical traffic scenarios at junctions as well as a simulation and evaluation framework to validate those scenarios. The safety performance indicators selected and implemented in the framework can be seen as a new reference for conducting virtual tests at junctions. The applicability of the framework is demonstrated by an experiment based on a selected car-to-car collision scenario. Considering the current progression of automated transport, this work is highly relevant for virtual testing procedures and is an important step towards approval and certification of automated vehicles.
Funding
This work has partly been conducted within the ENABLE-S3 project that has been received funding from the ECSEL Joint Undertaking under grant agreement no. 692455. This joint undertaking receives support from the European Union’s Horizon 2020 Research and Innovation Programme and AT, DK, DE, FI, CZ, IT, ES, PT, PL, IE, BE, FR, NL, UK, SK, NO.
History
Published in
2018 21st International Conference on Intelligent Transportation Systems (ITSC) IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSCVolume
2018-NovemberPages
90 - 97Citation
NITSCHE, P. ... et al, 2018. A novel, modular validation framework for collision avoidance of automated vehicles at road junctions. Presented at the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 4-7 November 2018, pp.90-97.Publisher
© IEEEVersion
- AM (Accepted Manuscript)
Acceptance date
2018-08-15Publication date
2018-12-10Notes
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.ISBN
9781728103235eISSN
2153-0017Publisher version
Language
- en