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Found 9 papers, 1 papers with code
Bayesian Learning for the Robust Verification of Autonomous Robots
We develop a novel Bayesian learning framework that enables the runtime verification of autonomous robots performing critical missions in uncertain environments.
Closed-loop Analysis of Vision-based Autonomous Systems: A Case Study
We address the above challenges by replacing the camera and the network with a compact probabilistic abstraction built from the confusion matrices computed for the DNN on a representative image data set.
Discrete-Event Controller Synthesis for Autonomous Systems with Deep-Learning Perception Components
We use the method in simulation to synthesise controllers for mobile-robot collision avoidance, and for maintaining driver attentiveness in shared-control autonomous driving.
DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers
We introduce DeepCert, a tool-supported method for verifying the robustness of deep neural network (DNN) image classifiers to contextually relevant perturbations such as blur, haze, and changes in image contrast.
Maintaining driver attentiveness in shared-control autonomous driving
We present a work-in-progress approach to improving driver attentiveness in cars provided with automated driving systems.
Autonomous Driving
Robotics
Systems and Control
Systems and Control
Fast Parametric Model Checking through Model Fragmentation
Parametric model checking (PMC) computes algebraic formulae that express key non-functional properties of a system (reliability, performance, etc.)
Software Engineering Formal Languages and Automata Theory Robotics
Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)
Machine Learning (ML) is now used in a range of systems with results that are reported to exceed, under certain conditions, human performance.
Detection and Mitigation of Rare Subclasses in Deep Neural Network Classifiers
Regions of high-dimensional input spaces that are underrepresented in training datasets reduce machine-learnt classifier performance, and may lead to corner cases and unwanted bias for classifiers used in decision making systems.
Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges
Our paper provides a comprehensive survey of the state-of-the-art in the assurance of ML, i. e. in the generation of evidence that ML is sufficiently safe for its intended use.
