Recent years have witnessed numerous technical breakthroughs in connected and
autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly …

The rise of large foundation models, trained on extensive datasets, is revolutionizing the
field of AI. Models such as SAM, DALL-E2, and GPT-4 showcase their adaptability by …

Autonomous Driving (AD) faces crucial hurdles for commercial launch, notably in the form of
diminished public trust and safety concerns from long-tail unforeseen driving scenarios. This …

Traffic accidents, being a significant contributor to both human casualties and property
damage, have long been a focal point of research for many scholars in the field of traffic …

It is still a challenging topic to make reactive driving behaviors in complex urban
environments as road users' intentions are unknown. Model-based reinforcement learning …

Autonomous driving has advanced significantly due to sensors, machine learning, and
artificial intelligence improvements. However, prevailing methods struggle with intricate …

Learning a human-like driving policy from large-scale driving demonstrations is promising,
but the uncertainty and non-deterministic nature of planning make it challenging. In this …

Scenario engineering plays a vital role in various Industry 5.0 applications. In the field of
autonomous driving systems, driving scenario data are important for the training and testing …

Vision-Large-Language-models (VLMs) have great application prospects in autonomous
driving. Despite the ability of VLMs to comprehend and make decisions in complex …

Large Language Models (LLMs) are demonstrating outstanding potential for tasks such as
text generation, summarization, and classification. Given that such models are trained on a …