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 …

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 …

How should representations from complementary sensors be integrated for autonomous
driving? Geometry-based sensor fusion has shown great promise for perception tasks such …

Multimodal large language models (MLLMs) have shown remarkable capabilities across a
broad range of tasks but their knowledge and abilities in the geographic and geospatial …

As autonomous driving technology matures, end-to-end methodologies have emerged as a
leading strategy, promising seamless integration from perception to control via deep …

By harnessing the capabilities of large language models (LLMs), recent large multimodal
models (LMMs) have shown remarkable versatility in open-world multimodal understanding …

While recently Multimodal Large Language Models (MM-LLMs) have made exciting strides,
they mostly fall prey to the limitation of only input-side multimodal understanding, without the …

The demand for high-quality annotated data has surged in recent years for applications
driven by real-world artificial intelligence, such as autonomous driving and embodied …

The burgeoning interest in Multimodal Large Language Models (MLLMs), such as OpenAI's
GPT-4V (ision), has significantly impacted both academic and industrial realms. These …

In the past year, Multimodal Large Language Models (MLLMs) have demonstrated
remarkable performance in tasks such as visual question answering, visual understanding …