129 research outputs found
Simulator-based explanation and debugging of hazard-triggering events in DNN-based safety-critical systems
When Deep Neural Networks (DNNs) are used in safety-critical systems,
engineers should determine the safety risks associated with DNN errors observed
during testing. For DNNs processing images, engineers visually inspect all
error-inducing images to determine common characteristics among them. Such
characteristics correspond to hazard-triggering events (e.g., low illumination)
that are essential inputs for safety analysis. Though informative, such
activity is expensive and error-prone.
To support such safety analysis practices, we propose SEDE, a technique that
generates readable descriptions for commonalities in error-inducing, real-world
images and improves the DNN through effective retraining. SEDE leverages the
availability of simulators, which are commonly used for cyber-physical systems.
SEDE relies on genetic algorithms to drive simulators towards the generation of
images that are similar to error-inducing, real-world images in the test set;
it then leverages rule learning algorithms to derive expressions that capture
commonalities in terms of simulator parameter values. The derived expressions
are then used to generate additional images to retrain and improve the DNN.
With DNNs performing in-car sensing tasks, SEDE successfully characterized
hazard-triggering events leading to a DNN accuracy drop. Also, SEDE enabled
retraining to achieve significant improvements in DNN accuracy, up to 18
percentage points.Comment: 40 pages, 15 figures, 17 table
A multiple story urban office building
Thesis (M. Arch)--Massachusetts Institute of Technology, Dept. of Architecture, 1960.Accompanying drawings held by MIT Museum.Includes bibliographical references (leaves 39-40).Charles Thomas Stifter.M.Arc
Autoencoder Attractors for Uncertainty Estimation
The reliability assessment of a machine learning model's prediction is an
important quantity for the deployment in safety critical applications. Not only
can it be used to detect novel sceneries, either as out-of-distribution or
anomaly sample, but it also helps to determine deficiencies in the training
data distribution. A lot of promising research directions have either proposed
traditional methods like Gaussian processes or extended deep learning based
approaches, for example, by interpreting them from a Bayesian point of view. In
this work we propose a novel approach for uncertainty estimation based on
autoencoder models: The recursive application of a previously trained
autoencoder model can be interpreted as a dynamical system storing training
examples as attractors. While input images close to known samples will converge
to the same or similar attractor, input samples containing unknown features are
unstable and converge to different training samples by potentially removing or
changing characteristic features. The use of dropout during training and
inference leads to a family of similar dynamical systems, each one being robust
on samples close to the training distribution but unstable on new features.
Either the model reliably removes these features or the resulting instability
can be exploited to detect problematic input samples. We evaluate our approach
on several dataset combinations as well as on an industrial application for
occupant classification in the vehicle interior for which we additionally
release a new synthetic dataset.Comment: This paper is accepted at IEEE International Conference on Pattern
Recognition (ICPR), 202
Automated Repair of Feature Interaction Failures in Automated Driving Systems
In the past years, several automated repair strategies have been
proposed to fix bugs in individual software programs without any
human intervention. There has been, however, little work on how
automated repair techniques can resolve failures that arise at the
system-level and are caused by undesired interactions among different
system components or functions. Feature interaction failures
are common in complex systems such as autonomous cars that are
typically built as a composition of independent features (i.e., units
of functionality). In this paper, we propose a repair technique to
automatically resolve undesired feature interaction failures in automated
driving systems (ADS) that lead to the violation of system
safety requirements. Our repair strategy achieves its goal by (1) localizing
faults spanning several lines of code, (2) simultaneously
resolving multiple interaction failures caused by independent faults,
(3) scaling repair strategies from the unit-level to the system-level,
and (4) resolving failures based on their order of severity. We have
evaluated our approach using two industrial ADS containing four
features. Our results show that our repair strategy resolves the
undesired interaction failures in these two systems in less than 16h
and outperforms existing automated repair techniques
Simulator-based explanation and debugging of hazard-triggering events in DNN-based safety-critical systems
When Deep Neural Networks (DNNs) are used in safety-critical systems, engineers should determine the safety risks associated with failures (i.e., erroneous outputs) observed during testing. For DNNs processing images, engineers visually inspect all failure-inducing images to determine common characteristics among them. Such characteristics correspond to hazard-triggering events (e.g., low illumination) that are essential inputs for safety analysis. Though informative, such activity is expensive and error-prone.
To support such safety analysis practices, we propose SEDE, a technique that generates readable descriptions for commonalities in failure-inducing, real-world images and improves the DNN through effective retraining. SEDE leverages the availability of simulators, which are commonly used for cyber-physical systems. It relies on genetic algorithms to drive simulators towards the generation of images that are similar to failure-inducing, real-world images in the test set; it then employs rule learning algorithms to derive expressions that capture commonalities in terms of simulator parameter values. The derived expressions are then used to generate additional images to retrain and improve the DNN.
With DNNs performing in-car sensing tasks, SEDE successfully characterized hazard-triggering events leading to a DNN accuracy drop. Also, SEDE enabled retraining leading to significant improvements in DNN accuracy, up to 18 percentage points
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