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A Tool for Benchmarking Large Language Models' Robustness in Assessing the Realism of Driving Scenarios
Jiahui Wu, Chengjie Lu, Aitor Arrieta +1
In recent years, autonomous driving systems have made significant progress, yet ensuring their safety remains a key challenge. To this end, scenario-based testing offers a practica…
Vision Language Model-based Testing of Industrial Autonomous Mobile Robots
Jiahui Wu, Chengjie Lu, Aitor Arrieta +2
PAL Robotics, in Spain, builds a variety of Autonomous Mobile Robots (AMRs), which are deployed in diverse environments (e.g., warehouses, retail spaces, and offices), where they w…
Foundation Models for Software Engineering of Cyber-Physical Systems: the Road Ahead
Chengjie Lu, Pablo Valle, Jiahui Wu +4
Foundation Models (FMs), particularly Large Language Models (LLMs), are increasingly used to support various software engineering activities (e.g., coding and testing). Their adopt…
Reinforcement Learning for Testing Interdependent Requirements in Autonomous Vehicles: An Empirical Study
Jiahui Wu, Chengjie Lu, Aitor Arrieta +1
Autonomous vehicles (AVs) make driving decisions without humans, making dependability assurance critical. Scenario-based testing is widely used to evaluate AVs under diverse condit…