Search Results for author: Shaukat Ali

Found 6 papers, 5 papers with code

EpiTESTER: Testing Autonomous Vehicles with Epigenetic Algorithm and Attention Mechanism

1 code implementation30 Nov 2023 Chengjie Lu, Shaukat Ali, Tao Yue

Testing autonomous vehicles (AVs) under various environmental scenarios that lead the vehicles to unsafe situations is known to be challenging.

Autonomous Driving

DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather Data

1 code implementation8 Oct 2023 Chengjie Lu, Tao Yue, Man Zhang, Shaukat Ali

In addition, existing ADS testing techniques have limited effectiveness in ensuring the realism of test scenarios, especially the realism of weather conditions and their changes over time.

Autonomous Driving Q-Learning +1

Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical Systems

1 code implementation27 Sep 2023 Qinghua Xu, Shaukat Ali, Tao Yue

LATTICE also, on average, reduces the training time of ATTAIN by 4. 2% on the five datasets and is on par with the baselines in terms of detection delay time.

Anomaly Detection

Knowledge Distillation-Empowered Digital Twin for Anomaly Detection

no code implementations8 Sep 2023 Qinghua Xu, Shaukat Ali, Tao Yue, Zaimovic Nedim, Inderjeet Singh

However, constructing a DT for anomaly detection in TCMS necessitates sufficient training data and extracting both chronological and context features with high quality.

Anomaly Detection Knowledge Distillation +2

EvoCLINICAL: Evolving Cyber-Cyber Digital Twin with Active Transfer Learning for Automated Cancer Registry System

1 code implementation6 Sep 2023 Chengjie Lu, Qinghua Xu, Tao Yue, Shaukat Ali, Thomas Schwitalla, Jan F. Nygård

To tackle this challenge, we propose EvoCLINICAL, which considers the CCDT developed for the previous version of GURI as the pretrained model and fine-tunes it with the dataset labelled by querying a new GURI version.

Active Learning Transfer Learning

Prediction Surface Uncertainty Quantification in Object Detection Models for Autonomous Driving

1 code implementation11 Jul 2021 Ferhat Ozgur Catak, Tao Yue, Shaukat Ali

Object detection in autonomous cars is commonly based on camera images and Lidar inputs, which are often used to train prediction models such as deep artificial neural networks for decision making for object recognition, adjusting speed, etc.

Autonomous Driving Decision Making +6

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