Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Daniel J. Fremont"'
Autor:
Anwesha Chattoraj, Eric Vin, Yusuke Tanaka, Jillian Naldrien Pantig, Daniel J. Fremont, Ankur Mehta
Publikováno v:
Proceedings of Cyber-Physical Systems and Internet of Things Week 2023.
Publikováno v:
2022 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW).
Publikováno v:
Computer Aided Verification ISBN: 9783031131875
In many synthesis problems, it can be essential to generate implementations which not only satisfy functional constraints but are also randomized to improve variety, robustness, or unpredictability. The recently-proposed framework of control improvis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::52d1a67404a7a8d67633c37fe63d2f06
https://doi.org/10.1007/978-3-031-13188-2_26
https://doi.org/10.1007/978-3-031-13188-2_26
Autor:
Edward Kim, Jay Shenoy, Sebastian Junges, Daniel J. Fremont, Alberto Sangiovanni-Vincentelli, Sanjit A. Seshia
Publikováno v:
Mitra, S. (ed.), ICCPS 2022: 13th ACM/IEEE International Conference, pp. 34-45
Simulation-based testing of autonomous vehicles (AVs) has become an essential complement to road testing to ensure safety. Consequently, substantial research has focused on searching for failure scenarios in simulation. However, a fundamental questio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bfdc41fde86fd18878ed0864caa831a5
https://repository.ubn.ru.nl/handle/2066/253560
https://repository.ubn.ru.nl/handle/2066/253560
Publikováno v:
DAC
Persistent challenges in making autonomous vehicles safe and reliable have hampered their widespread deployment. We believe that formal methods will play an essential role in the enterprise of ensuring AV safety by providing tools for the modeling, v
Autor:
Mykel J. Kochenderfer, Ransalu Senanayake, Daniel J. Fremont, Alessio Lomuscio, Dragos D. Margineantu, Cheng Soon Ong
Publikováno v:
Machine Learning.
Publikováno v:
Robotics: Science and Systems
High level declarative constraints provide a powerful (and popular) way to define and construct control policies; however, most synthesis algorithms do not support specifying the degree of randomness (unpredictability) of the resulting controller. In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::da4d0b42e4f01c7c3d6827f037341a70
http://arxiv.org/abs/2103.05672
http://arxiv.org/abs/2103.05672
Publikováno v:
Runtime Verification ISBN: 9783030884932
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::17b651249ff6e80de122fc3091e4d608
https://doi.org/10.1007/978-3-030-88494-9_15
https://doi.org/10.1007/978-3-030-88494-9_15
Publikováno v:
Runtime Verification ISBN: 9783030884932
RV
RV
Autonomous systems are increasingly deployed in safety-critical applications and rely more on high-performance components based on artificial intelligence (AI) and machine learning (ML). Runtime monitors play an important role in raising the level of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::607f05ede2121f7e97e93985835bb08b
https://doi.org/10.1007/978-3-030-88494-9_19
https://doi.org/10.1007/978-3-030-88494-9_19
Autor:
Ellen Kalvan, Yash Vardhan Pant, Daniel J. Fremont, Kesav Viswanadha, Francis Indaheng, Sanjit A. Seshia, Justin C. Wong, Edward Kim
Publikováno v:
AITest
This paper summarizes our formal approach to testing autonomous vehicles (AVs) in simulation for the IEEE AV Test Challenge. We demonstrate a systematic testing framework leveraging our previous work on formally-driven simulation for intelligent cybe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::99f19f7db02700333e43f5a702bdf931