34 citations · 88 across the 11 of their papers we have counts for
11 papers
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…
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…
CUEING: a lightweight model to Capture hUman attEntion In driviNG
Linfeng Liang, Yao Deng, Yang Zhang +4
Discrepancies in decision-making between Autonomous Driving Systems (ADS) and human drivers underscore the need for intuitive human gaze predictors to bridge this gap, thereby impr…
TARGET: Automated Scenario Generation from Traffic Rules for Testing Autonomous Vehicles via Validated LLM-Guided Knowledge Extraction
Yao Deng, Jiaohong Yao, Zhi Tu +3
Recent incidents with autonomous vehicles highlight the need for rigorous testing to ensure safety and robustness. Constructing test scenarios for autonomous driving systems (ADSs)…
Scenario-Based Test Reduction and Prioritization for Multi-Module Autonomous Driving Systems
Yao Deng, Xi Zheng, Mengshi Zhang +2
When developing autonomous driving systems (ADS), developers often need to replay previously collected driving recordings to check the correctness of newly introduced changes to th…
Testing of Autonomous Driving Systems: Where Are We and Where Should We Go?
Guannan Lou, Yao Deng, Xi Zheng +2
Autonomous driving has shown great potential to reform modern transportation. Yet its reliability and safety have drawn a lot of attention and concerns. Compared with traditional s…