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" …

In recent years, the field of computer vision has seen significant advancements thanks to the
development of large language models (LLMs). These models have enabled more effective …

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 …

The integration of visual inputs with large language models (LLMs) has led to remarkable
advancements in multi-modal capabilities giving rise to vision large language models …

The quest for fully autonomous vehicles (AVs) capable of navigating complex real-world
scenarios with human-like understanding and responsiveness. In this paper, we introduce …

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 …

The deployment of multimodal large language models (MLLMs) has brought forth a unique
vulnerability: susceptibility to malicious attacks through visual inputs. We delve into the novel …

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 …

Embodied scene understanding serves as the cornerstone for autonomous agents to
perceive, interpret, and respond to open driving scenarios. Such understanding is typically …