5 papers · 1 filter
AutoMT: A Multi-Agent LLM Framework for Automated Metamorphic Testing of Autonomous Driving Systems
Linfeng Liang, Chenkai Tan, Yao Deng +3
Autonomous Driving Systems (ADS) are safety-critical, where failures can be severe. While Metamorphic Testing (MT) is effective for fault detection in ADS, existing methods rely he…
A Step-by-Step Guide to Creating a Robust Autonomous Drone Testing Pipeline
Yupeng Jiang, Yao Deng, Sebastian Schroder +7
Autonomous drones are rapidly reshaping industries ranging from aerial delivery and infrastructure inspection to environmental monitoring and disaster response. Ensuring the safety…
SAFE: Harnessing LLM for Scenario-Driven ADS Testing from Multimodal Crash Data
Siwei Luo, Yang Zhang, Yao Deng +2
Ensuring the safety of Autonomous Driving Systems (ADS) requires realistic and reproducible test scenarios, yet extracting such scenarios from multimodal crash reports remains a ma…
MARL-OT: Multi-Agent Reinforcement Learning Guided Online Fuzzing to Detect Safety Violation in Autonomous Driving Systems
Linfeng Liang, Xi Zheng
Autonomous Driving Systems (ADSs) are safety-critical, as real-world safety violations can result in significant losses. Rigorous testing is essential before deployment, with simul…
GARL: Genetic Algorithm-Augmented Reinforcement Learning to Detect Violations in Marker-Based Autonomous Landing Systems
Linfeng Liang, Yao Deng, Kye Morton +7
Automated Uncrewed Aerial Vehicle (UAV) landing is crucial for autonomous UAV services such as monitoring, surveying, and package delivery. It involves detecting landing targets, p…