To standardize definitions and guide the design, regulation, and policy related to automated
transportation, the Society of Automotive Engineers (SAE) has established a taxonomy …

User trust has been identified as a critical issue that is pivotal to the success of autonomous
vehicle (AV) operations where artificial intelligence (AI) is widely adopted. For such …

Autonomous driving has attracted interest for interpretable action decision models that mimic
human cognition. Existing interpretable autonomous driving models explore static human …

Purpose-Perception has been identified as the main cause underlying most autonomous
vehicle related accidents. As the key technology in perception, deep learning (DL) based …

Image spam threat detection has continually been a popular area of research with the
internet's phenomenal expansion. This research presents an explainable framework for …

In recent years, the advancement of deep-learning technologies has greatly promoted the
research progress of autonomous driving. However, deep neural network is like a black box …

With the development of artificial intelligence and breakthroughs in deep learning, large-
scale foundation models (FMs), such as generative pre-trained transformer (GPT), Sora, etc …

Explaining the decision made by end-to-end autonomous driving is a difficult task. These
approaches take raw sensor data and compute the decision as a black box with large deep …

The exponential growth of electric vehicles (EVs) presents novel challenges in preserving
battery health and in addressing the persistent problem of vehicle range anxiety. To address …

In the transition era towards connected autonomous vehicles (CAVs), the sharing of the
roadway by CAVs and human-driven vehicles (HDVs) in a mixed-traffic stream is expected …