41 citations · 57 across the 10 of their papers we have counts for
6 papers · 1 filter
Fine-grained Testing for Autonomous Driving Software: a Study on Autoware with LLM-driven Unit Testing
Wenhan Wang, Xuan Xie, Yuheng Huang +3
Testing autonomous driving systems (ADS) is critical to ensuring their reliability and safety. Existing ADS testing works focuses on designing scenarios to evaluate system-level be…
LeCov: Multi-level Testing Criteria for Large Language Models
Xuan Xie, Jiayang Song, Yuheng Huang +4
Large Language Models (LLMs) are widely used in many different domains, but because of their limited interpretability, there are questions about how trustworthy they are in various…
MORTAR: A Model-based Runtime Action Repair Framework for AI-enabled Cyber-Physical Systems
Renzhi Wang, Zhehua Zhou, Jiayang Song +3
Cyber-Physical Systems (CPSs) are increasingly prevalent across various industrial and daily-life domains, with applications ranging from robotic operations to autonomous driving.…
Online Safety Analysis for LLMs: a Benchmark, an Assessment, and a Path Forward
Xuan Xie, Jiayang Song, Zhehua Zhou +3
While Large Language Models (LLMs) have seen widespread applications across numerous fields, their limited interpretability poses concerns regarding their safe operations from mult…
Towards Building AI-CPS with NVIDIA Isaac Sim: An Industrial Benchmark and Case Study for Robotics Manipulation
Zhehua Zhou, Jiayang Song, Xuan Xie +5
As a representative cyber-physical system (CPS), robotic manipulator has been widely adopted in various academic research and industrial processes, indicating its potential to act…
Mosaic: Model-based Safety Analysis Framework for AI-enabled Cyber-Physical Systems
Xuan Xie, Jiayang Song, Zhehua Zhou +2
Cyber-physical systems (CPSs) are now widely deployed in many industrial domains, e.g., manufacturing systems and autonomous vehicles. To further enhance the capability and applica…