Fetching the paper…
Reading the bibliography…
Autonomous Driving Systems (ADSs) are complex Cyber-Physical Systems (CPSs) that must ensure safety even in uncertain conditions.
Neural networks and physical systems with emergent collective computational abilities
J J Hopfield. 1982 · 1982
Earlier work this paper cites.
Learning representations by back-propagating errors
David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. 1986 · 1986
Earlier work this paper cites.
Metamorphic Testing: A New Approach for Generating Next Test Cases
T. Y. Chen, S. C. Cheung, and S. M. Yiu. 1998 · 1998
Earlier work this paper cites.
Exploring methods for evaluating group differences on the NSSE and other surveys: Are the t-test and Cohen’sd indices the most appropriate choices. In annual meeting of the Southern Association for Institutional Research . 1–51
Jeanine Romano, Jeffrey. D. Kromrey, Jesse Coraggio, Jeff Skowronek, and Linda Devine. 2006 · 2006
Earlier work this paper cites.
Metamorphic runtime checking of non-testable programs
Christian Murphy and Gail E Kaiser. 2009 · 2009
Earlier work this paper cites.
Automatic system testing of programs without test oracles. In Proceedings of the eighteenth international symposium on Software testing and analysis . 189–200
Christian Murphy, Kuang Shen, and Gail Kaiser. 2009 · 2009
Earlier work this paper cites.
Metamorphic runtime checking of applications without test oracles
Jonathan Bell, Christian Murphy, and Gail Kaiser. 2015 · 2015
Earlier work this paper cites.
Testing advanced driver assistance systems using multi-objective search and neural networks. In Proceedings of the 31st IEEE/ACM international conference on automated software engineering . 63–74
Raja Ben Abdessalem, Shiva Nejati, Lionel C Briand, and Thomas Stifter. 2016 · 2016
Earlier work this paper cites.
End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning . PMLR, 1050–1059
Yarin Gal and Zoubin Ghahramani. 2016 · 2016
Earlier work this paper cites.
A survey on metamorphic testing
Sergio Segura, Gordon Fraser, Ana B Sanchez, and Antonio Ruiz-Cortés. 2016 · 2016
Earlier work this paper cites.
Understanding uncertainty in cyber-physical systems: a conceptual model. In Modelling Foundations and Applications: 12th European Conference, ECMFA 2016, Held as Part of STAF 2016, Vienna, Austria, July 6-7, 2016, Proceedings 12 . Springer, 247–264
Man Zhang, Bran Selic, Shaukat Ali, Tao Yue, Oscar Okariz, and Roland Norgren. 2016 · 2016
Earlier work this paper cites.
Robot Operating System (ROS). Vol. 1
Anis Koubâa et al · 2017
Earlier work this paper cites.
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles. In Advances in Neural Information Processing Systems , I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. 2017 · 2017
Earlier work this paper cites.
Metamorphic model-based testing of autonomous systems. In 2017 IEEE/ACM 2nd International Workshop on Metamorphic Testing (MET) . IEEE, 35–41
Mikael Lindvall, Adam Porter, Gudjon Magnusson, and Christoph Schulze. 2017 · 2017
Earlier work this paper cites.
Deepxplore: Automated whitebox testing of deep learning systems. In proceedings of the 26th Symposium on Operating Systems Principles . 1–18
Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana. 2017 · 2017
Earlier work this paper cites.
Adaptive generation of challenging scenarios for testing and evaluation of autonomous vehicles
Galen E Mullins, Paul G Stankiewicz, R Chad Hawthorne, and Satyandra K Gupta. 2018 · 2018
Earlier work this paper cites.
Deeptest: Automated testing of deep-neural-network-driven autonomous cars. In Proceedings of the 40th international conference on software engineering . 303–314
Yuchi Tian, Kexin Pei, Suman Jana, and Baishakhi Ray. 2018 · 2018
Earlier work this paper cites.
DeepRoad: GAN-based metamorphic testing and input validation framework for autonomous driving systems. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering . 132–142
Mengshi Zhang, Yuqun Zhang, Lingming Zhang, Cong Liu, and Sarfraz Khurshid. 2018 · 2018
Earlier work this paper cites.
Generating effective test cases for self-driving cars from police reports. In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 257–267
Alessio Gambi, Tri Huynh, and Gordon Fraser. 2019a · 2019
Cited alongside, same era.
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Dan Hendrycks and Thomas Dietterich. 2019 · 2019
Cited alongside, same era.
Towards structured evaluation of deep neural network supervisors. In 2019 IEEE International Conference On Artificial Intelligence Testing (AITest) . IEEE, 27–34
Jens Henriksson, Christian Berger, Markus Borg, Lars Tornberg, Cristofer Englund, Sankar Raman Sathyamoorthy, and Stig Ursing. 2019 · 2019
Cited alongside, same era.
Guiding deep learning system testing using surprise adequacy. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . IEEE, 1039–1049
Jinhan Kim, Robert Feldt, and Shin Yoo. 2019 · 2019
Cited alongside, same era.
Testing the plasticity of reinforcement learning-based systems
Matteo Biagiola and Paolo Tonella. 2022 · 2022
Later among the works it cites.
A declarative metamorphic testing framework for autonomous driving
Yao Deng, Xi Zheng, Tianyi Zhang, Huai Liu, Guannan Lou, Miryung Kim, and Tsong Yueh Chen. 2022 · 2022
Later among the works it cites.
Evaluation of runtime monitoring for UAV emergency landing. In 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 9703–9709
Joris Guerin, Kevin Delmas, and Jérémie Guiochet. 2022 · 2022
Later among the works it cites.
Efficient online testing for DNN-enabled systems using surrogate-assisted and many-objective optimization. In Proceedings of the 44th international conference on software engineering . 811–822
Fitash Ul Haq, Donghwan Shin, and Lionel Briand. 2022 · 2022
Later among the works it cites.
DeepGuard: a framework for safeguarding autonomous driving systems from inconsistent behaviour
Manzoor Hussain, Nazakat Ali, and Jang-Eui Hong. 2022 · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Uncertainty-wise cyber-physical system test modeling
Man Zhang, Shaukat Ali, Tao Yue, Roland Norgren, and Oscar Okariz. 2019 · 2019
Cited alongside, same era.
Metamorphic testing of driverless cars
Zhi Quan Zhou and Liqun Sun. 2019 · 2019
Cited alongside, same era.
Generating avoidable collision scenarios for testing autonomous driving systems. In 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST) . IEEE, 375–386
Alessandro Calò, Paolo Arcaini, Shaukat Ali, Florian Hauer, and Fuyuki Ishikawa. 2020 · 2020
Cited alongside, same era.
Taxonomy of real faults in deep learning systems. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering . 1110–1121
Nargiz Humbatova, Gunel Jahangirova, Gabriele Bavota, Vincenzo Riccio, Andrea Stocco, and Paolo Tonella. 2020 · 2020
Cited alongside, same era.
Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control. In 2020 IEEE International Conference on Robotics and Automation (ICRA) . 7344–7350
Rhiannon Michelmore, Matthew Wicker, Luca Laurenti, Luca Cardelli, Yarin Gal, and Marta Kwiatkowska. 2020 · 2020
Cited alongside, same era.
Adaptive metamorphic testing with contextual bandits
Helge Spieker and Arnaud Gotlieb. 2020 · 2020
Cited alongside, same era.
Misbehaviour prediction for autonomous driving systems. In Proceedings of the ACM/IEEE 42nd international conference on software engineering . 359–371
Andrea Stocco, Michael Weiss, Marco Calzana, and Paolo Tonella. 2020 · 2020
Cited alongside, same era.
Dissector: Input validation for deep learning applications by crossing-layer dissection. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering . 727–738
Huiyan Wang, Jingwei Xu, Chang Xu, Xiaoxing Ma, and Jian Lu. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
Learning configurations of operating environment of autonomous vehicles to maximize their collisions
Chengjie Lu, Yize Shi, Huihui Zhang, Man Zhang, Tiexin Wang, Tao Yue, and Shaukat Ali. 2022 · 2022
Later among the works it cites.
Mind the gap! a study on the transferability of virtual vs physical-world testing of autonomous driving systems
Andrea Stocco, Brian Pulfer, and Paolo Tonella. 2022b · 2022
Later among the works it cites.
Confidence-driven weighted retraining for predicting safety-critical failures in autonomous driving systems
Andrea Stocco and Paolo Tonella. 2022 · 2022
Later among the works it cites.
LawBreaker: An approach for specifying traffic laws and fuzzing autonomous vehicles. In Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering . 1–12
Yang Sun, Christopher M Poskitt, Jun Sun, Yuqi Chen, and Zijiang Yang. 2022 · 2022
Later among the works it cites.
Neural network guided evolutionary fuzzing for finding traffic violations of autonomous vehicles
Ziyuan Zhong, Gail Kaiser, and Baishakhi Ray. 2022 · 2022
Later among the works it cites.
Dataset for "MarMot: Metamorphic Runtime Monitoring of Autonomous Driving Systems"
Jon Ayerdi, Pablo Valle, Asier Iriarte, Ibai Roman, Miren Illarramendi, and Aitor Arrieta. 2023 · 2023
Closest in time.
Many-objective reinforcement learning for online testing of dnn-enabled systems. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 1814–1826
Fitash Ul Haq, Donghwan Shin, and Lionel C Briand. 2023 · 2023
Closest in time.
LeoRover Dataset
LeoRover. 2022 · 2023
Closest in time.
LeoRover
LeoRover. 2023 · 2023
Closest in time.
When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 1161–1173
Vincenzo Riccio and Paolo Tonella. 2023 · 2023
Closest in time.
STRETCH: Generating Challenging Scenarios for Testing Collision Avoidance Systems. In 2023 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 1–6
Franz Scheuer, Alessio Gambi, and Paolo Arcaini. 2023 · 2023
Closest in time.
Uncertainty quantification for deep neural networks: An empirical comparison and usage guidelines
Michael Weiss and Paolo Tonella. 2023 · 2023
Closest in time.
Specification-based Autonomous Driving System Testing
Yuan Zhou, Yang Sun, Yun Tang, Yuqi Chen, Jun Sun, Christopher M Poskitt, Yang Liu, and Zijiang Yang. 2023 · 2023
Closest in time.