1 code implementation • 30 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.
1 code implementation • 8 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.
1 code implementation • 27 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.
no code implementations • 8 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.
1 code implementation • 6 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.
1 code implementation • 11 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.