Safety-enhanced autonomous driving using interpretable sensor fusion transformer
Large-scale deployment of autonomous vehicles has been continually delayed due to safety
concerns. On the one hand, comprehensive scene understanding is indispensable, a lack of …
concerns. On the one hand, comprehensive scene understanding is indispensable, a lack of …
A survey on automated driving system testing: Landscapes and trends
Automated Driving Systems (ADS) have made great achievements in recent years thanks to
the efforts from both academia and industry. A typical ADS is composed of multiple modules …
the efforts from both academia and industry. A typical ADS is composed of multiple modules …
Black-box safety analysis and retraining of DNNs based on feature extraction and clustering
Deep neural networks (DNNs) have demonstrated superior performance over classical
machine learning to support many features in safety-critical systems. Although DNNs are …
machine learning to support many features in safety-critical systems. Although DNNs are …
LiRTest: augmenting LiDAR point clouds for automated testing of autonomous driving systems
With the tremendous advancement of Deep Neural Networks (DNNs), autonomous driving
systems (ADS) have achieved significant development and been applied to assist in many …
systems (ADS) have achieved significant development and been applied to assist in many …
Swordfish: A Framework for Evaluating Deep Neural Network-based Basecalling using Computation-In-Memory with Non-Ideal Memristors
Basecalling, an essential step in many genome analysis studies, relies on large Deep
Neural Network s (DNN s) to achieve high accuracy. Unfortunately, these DNN s are …
Neural Network s (DNN s) to achieve high accuracy. Unfortunately, these DNN s are …
Aries: Efficient testing of deep neural networks via labeling-free accuracy estimation
Deep learning (DL) plays a more and more important role in our daily life due to its
competitive performance in industrial application domains. As the core of DL-enabled …
competitive performance in industrial application domains. As the core of DL-enabled …
Edge Intelligence for Internet of Vehicles: A Survey
The Internet of Vehicles (IoV) has become a fundamental platform for advancing Intelligent
Transportation Systems (ITSs) and Intelligent Connected Vehicles (ICVs). However, the …
Transportation Systems (ITSs) and Intelligent Connected Vehicles (ICVs). However, the …
Generative model-based testing on decision-making policies
The reliability of decision-making policies is urgently important today as they have
established the fundamentals of many critical applications, such as autonomous driving and …
established the fundamentals of many critical applications, such as autonomous driving and …
Mixed and constrained input mutation for effective fuzzing of deep learning systems
Adversarial examples cause misclassifications of deep learning (DL) systems. It isn't easy to
debug misclassifications due to the intrinsic complexity of DL architecture. Thus, applying …
debug misclassifications due to the intrinsic complexity of DL architecture. Thus, applying …
LaF: Labeling-free model selection for automated deep neural network reusing
Applying deep learning (DL) to science is a new trend in recent years, which leads DL
engineering to become an important problem. Although training data preparation, model …
engineering to become an important problem. Although training data preparation, model …