Chih-Hong Cheng 鄭志弘
Chih-Hong Cheng is currently researcher at Fraunhofer IKS, acting as the department head - Safety Assurance for AI. His research interests include software engineering, formal methods, and AI/ML for trustworthy autonomy. He received his doctoral degree in CS from the Technical University of Munich.
(2019-2021) Technical manager at DENSO, responsible for the research activities in autonomous driving safety within Europe.
(2011-2013, 2015-2019) Permanent researcher at fortiss - Research Institute of the Free State of Bavaria; developed the research topic of dependable AI for autonomous systems.
(2013-2015) Scientist in ABB Corporate Research Germany; worked on projects related to intelligent production systems (Industry 4.0), cloud-related technologies for industrial automation (PaaS, IaaS), and the analysis of complex industrial software systems.
(copyrights belong to publishers)
Mixed-neighborhood, multi-speed cellular automata for safety-aware pedestrian prediction [SEFM'21]
Federated learning for driver status monitoring [ITSC'21]
Monitoring object detection abnormalities via data-label and post-algorithm abstractions [IROS'21 (pdf)]
Safety metrics for semantic segmentation in autonomous driving [AI Test'21 (pdf)]
Testing autonomous systems with believed equivalence refinement [AI Test'21 (pdf)]
Continuous safety verification of neural networks [DATE'21 (pdf)]
Provably robust monitoring of neuron activation patterns [DATE'21 (pdf)]
Safety-aware hardening of 3D object detection neural network systems [SAFECOMP'20 (pdf)]
Towards robust direct perception networks for automated driving [IV'20 (pdf)]
Towards safety verification of direct perception neural networks [DATE'20 (pdf)]
nn-dependability-kit: Engineering neural networks for safety-critical autonomous driving systems [ICCAD'19 (pdf)]
Runtime monitoring neuron activation patterns [DATE'19 (pdf)]
Towards dependability metrics for neural networks [MEMOCODE'18 (pdf)]
Quantitative projection coverage for testing ML-enabled autonomous systems [ATVA'18 (pdf)]
Verification of binarized neural networks via inter-neuron factoring [VSTTE'18 (pdf)]
Neural networks for safety-critical applications - challenges, experiments and perspectives [DATE'18 (pdf)]
autoCode4: Structural controller synthesis [TACAS'17 (pdf)]
Compositional parameter synthesis [FM'16]
Semantic degrees for Industrie 4.0 engineering [ESEC/FSE'15 (pdf)]
JBernstein: A validity checker for generalized polynomial constraints [CAV'13 (pdf)]
Algorithms for synthesizing priorities in component based systems [ATVA'11 (pdf)]
Talks (slides available for download):
Monitoring and testing towards AD safety standard compliance, a talk given at the MT-CPS'21 workshop in CPS-IoT week
Challenges and opportunities towards safe autonomous driving, a research talk given at the FoMLAS'20 workshop associated with CAV
Introduction to safe machine learning, a talk given at the SOTIF US conference at Austin
When neural networks meet dependability, a talk given at various circumstances