Related papers: Machine Learning-based Test Selection for Simulati…
In recent years, curial incidents and accidents have been reported due to un-intended control caused by misjudgment of statistical machine learning (SML), which include deep learning. The international functional safety standards for…
In-field test of processor-based devices is a must when considering safety-critical systems (e.g., in robotics, aerospace, and automotive applications). During in-field testing, different solutions can be adopted, depending on the specific…
Ensuring safety in the sense of constraint satisfaction for learning-based control is a critical challenge, especially in the model-free case. While safety filters address this challenge in the model-based setting by modifying unsafe…
In a critical software system, the testers have to spend an enormous amount of time and effort to maintain the software due to the continuous occurrence of defects. Among such defects, some severe defects may adversely affect the software.…
The boom of DL technology leads to massive DL models built and shared, which facilitates the acquisition and reuse of DL models. For a given task, we encounter multiple DL models available with the same functionality, which are considered…
During the development of autonomous systems such as driverless cars, it is important to characterize the scenarios that are most likely to result in failure. Adaptive Stress Testing (AST) provides a way to search for the most-likely…
In order to find the most likely failure scenarios which may occur under certain given operation domain, critical-scenario-based test is supposed as an effective and widely used method, which gives suggestions for designers to improve the…
With the implementation of the new EU regulation 2022/1426 regarding the type-approval of the automated driving system (ADS) of fully automated vehicles, scenario-based testing has gained significant importance in evaluating the performance…
The safety and reliability of Automated Driving Systems (ADSs) must be validated prior to large-scale deployment. Among existing validation approaches, scenario-based testing has been regarded as a promising method to improve testing…
The process of testing any software system is an enormous task which is time consuming and costly. The time and required effort to do sufficient testing grow, as the size and complexity of the software grows, which may cause overrun of the…
While it has been repeatedly shown that learning-based controllers can provide superior performance, they often lack of safety guarantees. This paper aims at addressing this problem by introducing a model predictive safety certification…
Simulation is essential to validate autonomous driving systems. However, a simple simulation, even for an extremely high number of simulated miles or hours, is not sufficient. We need well-founded criteria showing that simulation does…
This research explores Cost-Sensitive Learning (CSL) in the fraud detection domain to decrease the fraud class's incorrect predictions and increase its accuracy. Notably, we concentrate on shill bidding fraud that is challenging to detect…
System-level testing of avionics software systems requires compliance with different international safety standards such as DO-178C. An important consideration of the avionics industry is automated test data generation according to the…
Precise handling of chemical instruments and materials within a self-driving laboratory environment using robotic systems demands advanced and reliable control strategies. Sliding Mode Control (SMC) has emerged as a robust approach for…
Application developers often place executable assertions -- equipped with program-specific predicates -- in their system, targeting programming errors. However, these detectors can detect data errors resulting from transient hardware faults…
The MUSICC project has created a proof-of-concept scenario database to be used as part of a type approval process for the verification of automated driving systems (ADS). This process must include a highly automated means of evaluating test…
Automated vehicles require efficient and safe planning to maneuver in uncertain environments. Largely this uncertainty is caused by other traffic participants, e.g., surrounding vehicles. Future motion of surrounding vehicles is often…
Common cross-validation (CV) methods like k-fold cross-validation or Monte-Carlo cross-validation estimate the predictive performance of a learner by repeatedly training it on a large portion of the given data and testing on the remaining…
An open question in autonomous driving is how best to use simulation to validate the safety of autonomous vehicles. Existing techniques rely on simulated rollouts, which can be inefficient for finding rare failure events, while other…