We provide a sober look at the application of Multimodal Large Language Models (MLLMs)
within the domain of autonomous driving and challenge/verify some common assumptions …

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 …

The emergence of Multimodal Large Language Models ((M) LLMs) has ushered in new
avenues in artificial intelligence, particularly for autonomous driving by offering enhanced …

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

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 …

Typically, autonomous driving adopts a modular design, which divides the full stack into
perception, prediction, planning and control parts. Though interpretable, such modular …

The advances in multimodal large language models (MLLMs) have led to growing interests
in LLM-based autonomous driving agents to leverage their strong reasoning capabilities …

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

The pursuit of autonomous driving technology hinges on the sophisticated integration of
perception, decision-making, and control systems. Traditional approaches, both data-driven …