Autonomous driving has achieved significant milestones in research and development over
the last decade. There is increasing interest in the field as the deployment of self-operating …

With the rapid development of autonomous driving technologies, a vast array of autonomous
driving algorithms and platforms have emerged. These algorithms and platforms are usually …

Automated driving desires better performance on tasks like motion planning and interacting
with pedestrians in mixed-traffic environments. Deep learning algorithms can achieve high …

With rapid development in hardware (sensors and processors) and AI algorithms, automated
driving techniques have entered the public's daily life and achieved great success in …

Data science (DS) devotes to extract useful data from noisy one to form actionable insights. It
has broad applications in many domains such as internet search, tourism and social media …

This paper explores the emerging knowledge-driven autonomous driving technologies. Our
investigation highlights the limitations of current autonomous driving systems, in particular …

Given the uncertainty surrounding how existing explainability methods for autonomous
vehicles (AVs) meet the diverse needs of stakeholders, a thorough investigation is …

Source-free domain adaptation (SFDA) assumes that model adaptation only accesses the
well-learned source model and unlabeled target instances for knowledge transfer. However …

Although neural networks excel in video action recognition tasks their" black-box" nature
makes it challenging to understand the rationale behind their decisions. Recent approaches …

The examination of blood samples at a microscopic level plays a fundamental role in clinical
diagnostics. For instance, an in-depth study of White Blood Cells (WBCs), a crucial …