This comprehensive survey explored the evolving landscape of generative Artificial
Intelligence (AI), with a specific focus on the transformative impacts of Mixture of Experts …

In the past year, MultiModal Large Language Models (MM-LLMs) have undergone
substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs …

In this paper, we explore the potential of using a large language model (LLM) to understand
the driving environment in a human-like manner and analyze its ability to reason, interpret …

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

The fusion of human-centric design and artificial intelligence capabilities has opened up
new possibilities for next-generation autonomous vehicles that go beyond traditional …

Large Language Models (LLMs), built upon Transformer-based architectures with massive
pretraining on diverse data, have not only revolutionized natural language processing but …

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 …

Vision-language models are of interest in various domains, including automated driving,
where computer vision techniques can accurately detect road users, but where the vehicle …

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

This letter is the third report from a series of IEEE TIV's decentralized and hybrid workshops
(DHWs) on intelligent vehicles for education (IV4E). Autonomous racing serves as a vital …