Reference : Testing Advanced Driver Assistance Systems using Multi-objective Search and Neural Ne...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/28071
Testing Advanced Driver Assistance Systems using Multi-objective Search and Neural Networks
English
Ben Abdessalem (helali), Raja mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Nejati, Shiva mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Briand, Lionel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Stifter, Thomas mailto [International Electronics & Engineering (IEE), Contern, Luxembourg]
2016
International Conference on Automated Software Engineering (ASE 2016)
ACM
63-74
Yes
No
International
31st IEEE/ACM International Conference on Automated Software Engineering (ASE 2016)
from 03-09-2016 to 07-09-2016
Singapore
Singapore
[en] Advanced Driver Assistance Systems ; Multi-Objective Search Optimization ; Simulation ; Surrogate Modeling ; Neural Networks
[en] Recent years have seen a proliferation of complex Advanced Driver Assistance Systems (ADAS), in particular, for use in autonomous cars. These systems consist of sensors and cameras as well as image processing and decision support software components. They are meant to help drivers by providing proper warnings or by preventing dangerous situations. In this paper, we focus on the problem of design time testing of ADAS in a simulated environment. We provide a testing approach for ADAS by combining multi- objective search with surrogate models developed based on neural networks. We use multi-objective search to guide testing towards the most critical behaviors of ADAS. Surrogate modeling enables our testing approach to explore a larger part of the input search space within limited computational resources. We characterize the condition under which the multi-objective search algorithm behaves the same with and without surrogate modeling, thus showing the accuracy of our approach. We evaluate our approach by applying it to an industrial ADAS system. Our experiment shows that our approach automatically identifies test cases indicating critical ADAS behaviors. Further, we show that combining our search algorithm with surrogate modeling improves the quality of the generated test cases, especially under tight and realistic computational resources.
Interdisciplinary Centre for Security, Reliability and Trust
Fonds National de la Recherche, Luxembourg (FNR/P10/03)
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/28071
10.1145/2970276.2970311
http://doi.acm.org/10.1145/2970276.2970311

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