6 citations · 8 across the 9 of their papers we have counts for
9 papers
FuzzRisk: Online Collision Risk Estimation for Autonomous Vehicles based on Depth-Aware Object Detection via Fuzzy Inference
Brian Hsuan-Cheng Liao, Yingjie Xu, Chih-Hong Cheng +2
This paper presents a novel monitoring framework that infers the level of collision risk for autonomous vehicles (AVs) based on their object detection performance. The framework ta…
Estimating the Robustness Radius for Randomized Smoothing with 100 Sample Efficiency
Emmanouil Seferis, Stefanos Kollias, Chih-Hong Cheng
Randomized smoothing (RS) has successfully been used to improve the robustness of predictions for deep neural networks (DNNs) by adding random noise to create multiple variations o…
BAM: Box Abstraction Monitors for Real-time OoD Detection in Object Detection
Changshun Wu, Weicheng He, Chih-Hong Cheng +2
Out-of-distribution (OoD) detection techniques for deep neural networks (DNNs) become crucial thanks to their filtering of abnormal inputs, especially when DNNs are used in safety-…
Runtime Monitoring DNN-Based Perception
Chih-Hong Cheng, Michael Luttenberger, Rongjie Yan
Deep neural networks (DNNs) are instrumental in realizing complex perception systems. As many of these applications are safety-critical by design, engineering rigor is required to…
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…