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08/11/2023
Safeguarding Learning-based Control for Smart Energy Systems with Sampling Specifications
We study challenges using reinforcement learning in controlling energy s...
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07/24/2023
Safety Performance of Neural Networks in the Presence of Covariate Shift
Covariate shift may impact the operational safety performance of neural ...
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06/14/2023
Towards Rigorous Design of OoD Detectors
Out-of-distribution (OoD) detection techniques are instrumental for safe...
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05/28/2023
Potential-based Credit Assignment for Cooperative RL-based Testing of Autonomous Vehicles
While autonomous vehicles (AVs) may perform remarkably well in generic r...
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03/06/2023
EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learning
Uncertainty estimation is crucial in safety-critical settings such as au...
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11/14/2022
Butterfly Effect Attack: Tiny and Seemingly Unrelated Perturbations for Object Detection
This work aims to explore and identify tiny and seemingly unrelated pert...
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09/23/2022
Facilitating Change Implementation for Continuous ML-Safety Assurance
We propose a method for deploying a safety-critical machine-learning com...
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09/21/2022
Safety Metrics and Losses for Object Detection in Autonomous Driving
State-of-the-art object detectors have been shown effective in many appl...
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05/16/2022
Prioritizing Corners in OoD Detectors via Symbolic String Manipulation
For safety assurance of deep neural networks (DNNs), out-of-distribution...
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02/10/2022
Unaligned but Safe – Formally Compensating Performance Limitations for Imprecise 2D Object Detection
In this paper, we consider the imperfection within machine learning-base...
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11/04/2021
Logically Sound Arguments for the Effectiveness of ML Safety Measures
We investigate the issues of achieving sufficient rigor in the arguments...
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10/02/2021
ComOpT: Combination and Optimization for Testing Autonomous Driving Systems
ComOpT is an open-source research tool for coverage-driven testing of au...
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05/21/2021
Safety Metrics for Semantic Segmentation in Autonomous Driving
Within the context of autonomous driving, safety-related metrics for dee...
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03/29/2021
Monitoring Object Detection Abnormalities via Data-Label and Post-Algorithm Abstractions
While object detection modules are essential functionalities for any aut...
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03/08/2021
Testing Autonomous Systems with Believed Equivalence Refinement
Continuous engineering of autonomous driving functions commonly requires...
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11/24/2020
Provably-Robust Runtime Monitoring of Neuron Activation Patterns
For deep neural networks (DNNs) to be used in safety-critical autonomous...
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10/12/2020
Continuous Safety Verification of Neural Networks
Deploying deep neural networks (DNNs) as core functions in autonomous dr...
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03/25/2020
Safety-Aware Hardening of 3D Object Detection Neural Network Systems
We study how state-of-the-art neural networks for 3D object detection us...
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09/30/2019
Towards Robust Direct Perception Networks for Automated Driving
We consider the problem of engineering robust direct perception neural n...
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04/09/2019
Towards Safety Verification of Direct Perception Neural Networks
We study the problem of safety verification of direct perception neural ...
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02/27/2019
Architecting Dependable Learning-enabled Autonomous Systems: A Survey
We provide a summary over architectural approaches that can be used to c...
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11/16/2018
nn-dependability-kit: Engineering Neural Networks for Safety-Critical Systems
nn-dependability-kit is an open-source toolbox to support safety enginee...
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09/18/2018
Runtime Monitoring Neuron Activation Patterns
For using neural networks in safety critical domains, it is important to...
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09/18/2018
Runtime Monitoring Neural Activation Patterns
For using neural networks in safety critical domains, it is important to...
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06/06/2018
Towards Dependability Metrics for Neural Networks
Neural networks and other data engineered models are instrumental in dev...
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05/11/2018
Quantitative Projection Coverage for Testing ML-enabled Autonomous Systems
Systematically testing models learned from neural networks remains a cru...
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10/09/2017
Verification of Binarized Neural Networks via Inter-Neuron Factoring
We study the problem of formal verification of Binarized Neural Networks...
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10/09/2017
Verification of Binarized Neural Networks
We study the problem of formal verification of Binarized Neural Networks...
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09/04/2017
Neural Networks for Safety-Critical Applications - Challenges, Experiments and Perspectives
We propose a methodology for designing dependable Artificial Neural Netw...
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04/28/2017