Engineering knowledge-based (or expert) systems require extensive manual effort and
domain knowledge. As Large Language Models (LLMs) are trained using an enormous …

Autonomous Driving Systems (ADS) are safety-critical and require comprehensive testing
before their deployment on public roads. Most existing testing approaches consist in …

Simulation-based testing represents an important step to ensure the reliability of
autonomous driving software. In practice, when companies rely on third-party general …

The optimization of a system's configuration options is crucial for determining its
performance and functionality, particularly in the case of autonomous driving software (ADS) …

Multi-agent systems (MASs) have achieved remarkable success in multi-robot control,
intelligent transportation, and multiplayer games, etc. Thorough testing for MAS is urgently …

As autonomous driving systems (ADS) advance towards higher levels of autonomy,
orchestrating their safety verification becomes increasingly intricate. This paper unveils …

As highlighted in authoritative standards (eg, ISO21448), traffic law compliance is a
fundamental prerequisite for the commercialization of autonomous driving systems (ADS) …

Autonomous vehicles (AV) leverage Artificial Intelligence to reduce accidents and improve
fuel efficiency while sharing the roads with human drivers. Current AV prototypes have not …

Multi-modal foundation models and generative AI have demonstrated promising capabilities
in applications across various domains. Recently, Vision-language-action (VLA) models …

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 …