Automated and autonomous vehicles are often required to operate in complex road
environments with potential hazards that may lead to hazardous events causing injury or …

A machine learning model can often produce biased outputs for a familiar group or similar
sets of classes during inference over an unknown dataset. The generalization of neural …

Deep Neural Networks (DNNs) are used in various domains and industry fields with great
success due to their ability to learn complex tasks from high-dimensional data. However, the …

Testing autonomous driving systems for safety and reliability is essential, yet complex. A
primary challenge is identifying relevant test scenarios, especially the critical ones that may …

Simulation testing has been widely adopted by leading companies to ensure the safety of
autonomous driving systems (ADSs). Anumber of scenario-based testing approaches have …

Testing of autonomous driving systems (ADS) is a crucial, yet complex task that requires
different approaches to ensure the safety and reliability of the system in various driving …

As autonomous driving technology rapidly advances, effective testing tools and methods
become crucial. This paper comprehensively assesses the capabilities and limitations of …

Autonomous driving is a widely discussed topic nowadays. There are several papers in the
literature that overview the myriad possibilities and problems that drive the impact in this …