24 citations · 96 across the 43 of their papers we have counts for
44 papers
Safeguarding Learning-based Control for Smart Energy Systems with Sampling Specifications
Chih-Hong Cheng, Venkatesh Prasad Venkataramanan, Pragya Kirti Gupta +2
We study challenges using reinforcement learning in controlling energy systems, where apart from performance requirements, one has additional safety requirements such as avoiding b…
Safety Performance of Neural Networks in the Presence of Covariate Shift
Chih-Hong Cheng, Harald Ruess, Konstantinos Theodorou
Covariate shift may impact the operational safety performance of neural networks. A re-evaluation of the safety performance, however, requires collecting new operational data and c…
What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety Critical Systems
Saddek Bensalem, Chih-Hong Cheng, Wei Huang +3
Machine learning has made remarkable advancements, but confidently utilising learning-enabled components in safety-critical domains still poses challenges. Among the challenges, it…
Towards Rigorous Design of OoD Detectors
Chih-Hong Cheng, Changshun Wu, Harald Ruess +1
Out-of-distribution (OoD) detection techniques are instrumental for safety-related neural networks. We are arguing, however, that current performance-oriented OoD detection techniq…
Potential-based Credit Assignment for Cooperative RL-based Testing of Autonomous Vehicles
Utku Ayvaz, Chih-Hong Cheng, Hao Shen
While autonomous vehicles (AVs) may perform remarkably well in generic real-life cases, their irrational action in some unforeseen cases leads to critical safety concerns. This pap…
EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learning
Monish R. Nallapareddy, Kshitij Sirohi, Paulo L. J. Drews-Jr +3
Uncertainty estimation is crucial in safety-critical settings such as automated driving as it provides valuable information for several downstream tasks including high-level decisi…
Butterfly Effect Attack: Tiny and Seemingly Unrelated Perturbations for Object Detection
Nguyen Anh Vu Doan, Arda Yüksel, Chih-Hong Cheng
This work aims to explore and identify tiny and seemingly unrelated perturbations of images in object detection that will lead to performance degradation. While tininess can natura…
Facilitating Change Implementation for Continuous ML-Safety Assurance
Chih-Hong Cheng, Nguyen Anh Vu Doan, Balahari Balu +9
We propose a method for deploying a safety-critical machine-learning component into continuously evolving environments where an increased degree of automation in the engineering pr…
USC: Uncompromising Spatial Constraints for Safety-Oriented 3D Object Detectors in Autonomous Driving
Brian Hsuan-Cheng Liao, Chih-Hong Cheng, Hasan Esen +1
In this work, we consider the safety-oriented performance of 3D object detectors in autonomous driving contexts. Specifically, despite impressive results shown by the mass literatu…
Prioritizing Corners in OoD Detectors via Symbolic String Manipulation
Chih-Hong Cheng, Changshun Wu, Emmanouil Seferis +1
For safety assurance of deep neural networks (DNNs), out-of-distribution (OoD) monitoring techniques are essential as they filter spurious input that is distant from the training d…
Unaligned but Safe -- Formally Compensating Performance Limitations for Imprecise 2D Object Detection
Tobias Schuster, Emmanouil Seferis, Simon Burton +1
In this paper, we consider the imperfection within machine learning-based 2D object detection and its impact on safety. We address a special sub-type of performance limitations: th…
Are Transformers More Robust? Towards Exact Robustness Verification for Transformers
Brian Hsuan-Cheng Liao, Chih-Hong Cheng, Hasan Esen +1
As an emerging type of Neural Networks (NNs), Transformers are used in many domains ranging from Natural Language Processing to Autonomous Driving. In this paper, we study the robu…
Logically Sound Arguments for the Effectiveness of ML Safety Measures
Chih-Hong Cheng, Tobias Schuster, Simon Burton
We investigate the issues of achieving sufficient rigor in the arguments for the safety of machine learning functions. By considering the known weaknesses of DNN-based 2D bounding…
ComOpT: Combination and Optimization for Testing Autonomous Driving Systems
Changwen Li, Chih-Hong Cheng, Tiantian Sun +2
ComOpT is an open-source research tool for coverage-driven testing of autonomous driving systems, focusing on planning and control. Starting with (i) a meta-model characterizing di…
Safety Metrics for Semantic Segmentation in Autonomous Driving
Chih-Hong Cheng, Alois Knoll, Hsuan-Cheng Liao
Within the context of autonomous driving, safety-related metrics for deep neural networks have been widely studied for image classification and object detection. In this paper, we…
Monitoring Object Detection Abnormalities via Data-Label and Post-Algorithm Abstractions
Yuhang Chen, Chih-Hong Cheng, Jun Yan +1
While object detection modules are essential functionalities for any autonomous vehicle, the performance of such modules that are implemented using deep neural networks can be, in…
Testing Autonomous Systems with Believed Equivalence Refinement
Chih-Hong Cheng, Rongjie Yan
Continuous engineering of autonomous driving functions commonly requires deploying vehicles in road testing to obtain inputs that cause problematic decisions. Although the discover…
Provably-Robust Runtime Monitoring of Neuron Activation Patterns
Chih-Hong Cheng
For deep neural networks (DNNs) to be used in safety-critical autonomous driving tasks, it is desirable to monitor in operation time if the input for the DNN is similar to the data…
Continuous Safety Verification of Neural Networks
Chih-Hong Cheng, Rongjie Yan
Deploying deep neural networks (DNNs) as core functions in autonomous driving creates unique verification and validation challenges. In particular, the continuous engineering parad…
Safety-Aware Hardening of 3D Object Detection Neural Network Systems
Chih-Hong Cheng
We study how state-of-the-art neural networks for 3D object detection using a single-stage pipeline can be made safety aware. We start with the safety specification (reflecting the…
Towards Robust Direct Perception Networks for Automated Driving
Chih-Hong Cheng
We consider the problem of engineering robust direct perception neural networks with output being regression. Such networks take high dimensional input image data, and they produce…
Towards Safety Verification of Direct Perception Neural Networks
Chih-Hong Cheng, Chung-Hao Huang, Thomas Brunner +1
We study the problem of safety verification of direct perception neural networks, where camera images are used as inputs to produce high-level features for autonomous vehicles to m…
Architecting Dependable Learning-enabled Autonomous Systems: A Survey
Chih-Hong Cheng, Dhiraj Gulati, Rongjie Yan
We provide a summary over architectural approaches that can be used to construct dependable learning-enabled autonomous systems, with a focus on automated driving. We consider thre…
nn-dependability-kit: Engineering Neural Networks for Safety-Critical Autonomous Driving Systems
Chih-Hong Cheng, Chung-Hao Huang, Georg Nührenberg
Can engineering neural networks be approached in a disciplined way similar to how engineers build software for civil aircraft? We present nn-dependability-kit, an open-source toolb…