Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving
and transportation systems to digitize and synergize connected automated vehicles …

Verification and validation (V&V) hold a significant position in the research and development
of automated vehicles (AVs). Current literature indicates that different V&V techniques have …

The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …

Foundation models are used to train a broad system of general data to build adaptations to
new bottlenecks. Typically, they contain hundreds of billions of hyperparameters that have …

The recent advancements in machine learning and artificial intelligence (particularly
foundation models such as BERT, GPT-3, T5, ResNet, etc.) have demonstrated remarkable …

This survey reviews explainability methods for vision-based self-driving systems trained with
behavior cloning. The concept of explainability has several facets and the need for …

Briefing: The rapid advancement of fundamental theories and computing capacity has
brought artificial intelligence, internet of things, extended reality, and many other new …

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

Motion control algorithms in the presence of pedestrians are critical for the development of
safe and reliable Autonomous Vehicles (AVs). Traditional motion control algorithms rely on …

Connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs) will
inevitably coexist on roads in the future, creating mixed-flow traffic. The heterogeneous car …