Autonomous driving systems generally employ separate models for different tasks resulting
in intricate designs. For the first time, we leverage singular multimodal large language …

Large language models (LLMs) have opened up new possibilities for intelligent agents,
endowing them with human-like thinking and cognitive abilities. In this work, we delve into …

The rise of multimodal large language models (MLLMs) has spurred interest in language-
based driving tasks. However existing research typically focuses on limited tasks and often …

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 …

In the past decade, autonomous driving has experienced rapid development in both
academia and industry. However, its limited interpretability remains a significant unsolved …

Existing learning-based autonomous driving (AD) systems face challenges in
comprehending high-level information, generalizing to rare events, and providing …

Despite significant recent progress in the field of autonomous driving modern methods still
struggle and can incur serious accidents when encountering long-tail unforeseen events …

Large Language Models (LLMs) have shown promise in the autonomous driving sector,
particularly in generalization and interpretability. We introduce a unique object-level …

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 …

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