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AITest 2021: Oxford, UK
- 2021 IEEE International Conference on Artificial Intelligence Testing, AITest 2021, Oxford, United Kingdom, August 23-26, 2021. IEEE 2021, ISBN 978-1-6654-3481-2
- Paolo Tonella:
Keynote Speaker. xv - Yuta Ojima, Shingo Horiuchi, Fuyuki Ishikawa:
Model-based Data-Complexity Estimator for Deep Learning Systems. 1-8 - Colin Paterson, Radu Calinescu, Chiara Picardi:
Detection and Mitigation of Rare Subclasses in Deep Neural Network Classifiers. 9-16 - Jaganmohan Chandrasekaran, Ankita Ramjibhai Patel, Yu Lei, Raghu Kacker, D. Richard Kuhn:
Evaluation of T-Way Testing of DNNs in Autonomous Driving Systems. 17-18 - Liming Hu, Shunhui Ji, Qiyin Dai, Pengcheng Zhang:
Evolutionary Generation of Test Case for Deep Neural Network Based on Coverage Guidance. 19-20 - Paolo Arcaini
, Andrea Bombarda
Efficient Computation of Robustness of Convolutional Neural Networks. 21-28 - Tinghui Ouyang, Yoshinao Isobe, Vicent Sanz Marco, Jun Ogata, Yoshiki Seo, Yutaka Oiwa:
AI robustness analysis with consideration of corner cases. 29-36 - Mehmet Melih Arici, Alper Sen:
Improving Robustness of Deep Learning Systems with Fast and Customizable Adversarial Data Generation. 37-38 - Shijun Ye, Pengcheng Zhang, Hai Dong
Heuristic-word-selection Genetic Algorithm for Generating Natural Language Adversarial Examples. 39-40 - Noah Metzger, Lars Hoffmann
Towards Trace-Graphs for Data-driven Test Case Mining in the Domain of Automated Driving. 41-48 - Chih-Hong Cheng, Rongjie Yan:
Testing Autonomous Systems with Believed Equivalence Refinement. 49-56 - Chih-Hong Cheng, Alois C. Knoll, Hsuan-Cheng Liao:
Safety Metrics for Semantic Segmentation in Autonomous Driving. 57-64 - Dhasarathy Parthasarathy, Anton Johansson:
SilGAN: Generating driving maneuvers for scenario-based software-in-the-loop testing. 65-72 - Murad Mamedov, Ksenia Vorontsova, Elena Treshcheva
Building a Reusable Defect Resolution Time Prediction Model Based on a Massive Open-Source Dataset: An Industrial Report. 73-80 - Qunying Song, Kaige Tan
An Industrial Workbench for Test Scenario Identification for Autonomous Driving Software. 81-82 - Per Erik Strandberg, Mirgita Frasheri
Ethical AI-Powered Regression Test Selection. 83-84 - Sydur Rahaman, Raina Samuel
Quantifying Nondeterminism and Inconsistency in Self-organizing Map Implementations. 85-92 - Ferhat Özgür Çatak, Tao Yue, Shaukat Ali:
Prediction Surface Uncertainty Quantification in Object Detection Models for Autonomous Driving. 93-100 - John McLeod, Fergus Simpson:
Validating Gaussian Process Models with Simulation-Based Calibration. 101-102 - Hamid Ebadi, Mahshid Helali Moghadam, Markus Borg, Gregory Gay, Afonso Fontes, Kasper Socha:
Efficient and Effective Generation of Test Cases for Pedestrian Detection - Search-based Software Testing of Baidu Apollo in SVL. 103-110 - John Seymour, Dac-Thanh-Chuong Ho, Quang-Hung Luu
An Empirical Testing of Autonomous Vehicle Simulator System for Urban Driving. 111-117 - David Kaufmann, Lorenz Klampfl, Florian Klück, Martin Zimmermann, Jianbo Tao:
Critical and Challenging Scenario Generation based on Automatic Action Behavior Sequence Optimization: 2021 IEEE Autonomous Driving AI Test Challenge Group 108. 118-127 - Vuong Nguyen, Stefan Huber, Alessio Gambi:
SALVO: Automated Generation of Diversified Tests for Self-driving Cars from Existing Maps. 128-135 - Kesav Viswanadha, Francis Indaheng, Justin Wong, Edward Kim, Ellen Kalvan, Yash Pant, Daniel J. Fremont
Addressing the IEEE AV Test Challenge with Scenic and VerifAI. 136-142 - Andrea Piazzoni
ViSTA: a Framework for Virtual Scenario-based Testing of Autonomous Vehicles. 143-150
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