With the emergence of Large Language Models (LLMs) and Vision Foundation Models
(VFMs), multimodal AI systems benefiting from large models have the potential to equally …

Autonomous driving (AD) has made significant strides in recent years. However existing
frameworks struggle to interpret and execute spontaneous user instructions such as" …

The future of autonomous vehicles lies in the convergence of human-centric design and
advanced AI capabilities. Autonomous vehicles of the future will not only transport …

Real-time learning concerns the ability of learning systems to acquire knowledge over time,
enabling their adaptation and generalization to novel tasks. It is a critical ability for …

Autonomous driving technology, a catalyst for revolutionizing transportation and urban
mobility, has the tend to transition from rule-based systems to data-driven strategies …

As the Large Language Model (LLM) becomes increasingly important in various domains.
However, the following challenges still remain unsolved in accelerating LLM inference:(1) …

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

The evolution of autonomous driving has made remarkable advancements in recent years,
evolving into a tangible reality. However, a human-centric large-scale adoption hinges on …

While behavior cloning has recently emerged as a highly successful paradigm for
autonomous driving humans rarely learn to perform complex tasks such as driving via …

To ensure safe driving in dynamic environments, autonomous vehicles should possess the
capability to accurately predict the lane change intentions of surrounding vehicles in …