TY - GEN
T1 - Poster
T2 - 21st International Conference on Hybrid Systems: Computation and Control, HSCC 2018
AU - Tuncali, Cumhur Erkan
AU - Fainekos, Georgios
AU - Ito, Hisahiro
AU - Kapinski, James
N1 - Publisher Copyright:
© 2018 Copyright held by the owner/author(s).
PY - 2018/4/11
Y1 - 2018/4/11
N2 - One of the main challenges in testing autonomous driving systems is the presence of machine learning components, such as neural networks, for which formal properties are difficult to establish. We present a simulation-based testing framework that supports methods used to evaluate cyberphysical systems, such as test case generation and automatic falsification. We demonstrate how the framework can be used to evaluate closed-loop properties of autonomous driving system models that include machine learning components.
AB - One of the main challenges in testing autonomous driving systems is the presence of machine learning components, such as neural networks, for which formal properties are difficult to establish. We present a simulation-based testing framework that supports methods used to evaluate cyberphysical systems, such as test case generation and automatic falsification. We demonstrate how the framework can be used to evaluate closed-loop properties of autonomous driving system models that include machine learning components.
UR - http://www.scopus.com/inward/record.url?scp=85049444876&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049444876&partnerID=8YFLogxK
U2 - 10.1145/3178126.3187004
DO - 10.1145/3178126.3187004
M3 - Conference contribution
AN - SCOPUS:85049444876
T3 - HSCC 2018 - Proceedings of the 21st International Conference on Hybrid Systems: Computation and Control (part of CPS Week)
SP - 283
EP - 284
BT - HSCC 2018 - Proceedings of the 21st International Conference on Hybrid Systems
PB - Association for Computing Machinery, Inc
Y2 - 11 April 2018 through 13 April 2018
ER -