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Found 8 papers, 0 papers with code
Tuning the feedback controller gains is a simple way to improve autonomous driving performance
Often, the feedback control gains are simply passed from paper to paper with little re-tuning taking place, even though the changes to the neural networks can alter the vehicle's closed loop dynamics.
Mutation-based Consistency Testing for Evaluating the Code Understanding Capability of LLMs
We apply different types of code mutations, such as operator replacement and statement deletion, to generate inconsistent code-description pairs.
Identifying the Hazard Boundary of ML-enabled Autonomous Systems Using Cooperative Co-Evolutionary Search
In Machine Learning (ML)-enabled autonomous systems (MLASs), it is essential to identify the hazard boundary of ML Components (MLCs) in the MLAS under analysis.
Many-Objective Reinforcement Learning for Online Testing of DNN-Enabled Systems
However, the environmental variables (e. g., lighting conditions) that might change during the systems' operation in the real world, causing the DES to violate requirements (safety, functional), are often kept constant during the execution of an online test scenario due to the two major challenges: (1) the space of all possible scenarios to explore would become even larger if they changed and (2) there are typically many requirements to test simultaneously.
Can Offline Testing of Deep Neural Networks Replace Their Online Testing?
Further, we cannot exploit offline testing results to reduce the cost of online testing in practice since we are not able to identify specific situations where offline testing could be as accurate as online testing in identifying safety requirement violations.
Digital Twins Are Not Monozygotic -- Cross-Replicating ADAS Testing in Two Industry-Grade Automotive Simulators
Based on a minimalistic scene, we compare critical test scenarios generated using our SBST solution in these two simulators.
Automatic Test Suite Generation for Key-Points Detection DNNs using Many-Objective Search (Experience Paper)
In this paper, we present an approach to automatically generate test data for KP-DNNs using many-objective search.
Comparing Offline and Online Testing of Deep Neural Networks: An Autonomous Car Case Study
Further, offline testing is more optimistic than online testing as many safety violations identified by online testing could not be identified by offline testing, while large prediction errors generated by offline testing always led to severe safety violations detectable by online testing.
