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…
Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses
Yao Deng, Tiehua Zhang, Guannan Lou +3
The rapid development of artificial intelligence, especially deep learning technology, has advanced autonomous driving systems (ADSs) by providing precise control decisions to coun…
SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems
Chenhao Xu, Jiaqi Ge, Yong Li +5
Federated learning (FL) enables collaborative training of a shared model on edge devices while maintaining data privacy. FL is effective when dealing with independent and identical…
A Declarative Metamorphic Testing Framework for Autonomous Driving
Yao Deng, Xi Zheng, Tianyi Zhang +4
Autonomous driving has gained much attention from both industry and academia. Currently, Deep Neural Networks (DNNs) are widely used for perception and control in autonomous drivin…
An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models
Yao Deng, Xi Zheng, Tianyi Zhang +3
Nowadays, autonomous driving has attracted much attention from both industry and academia. Convolutional neural network (CNN) is a key component in autonomous driving, which is als…
An Overview of Attacks and Defences on Intelligent Connected Vehicles
Mahdi Dibaei, Xi Zheng, Kun Jiang +9
Cyber security is one of the most significant challenges in connected vehicular systems and connected vehicles are prone to different cybersecurity attacks that endanger passengers…