The concept of world models has garnered significant attention due to advancements in
multimodal large language models such as GPT-4 and video generation models such as …

Autonomous driving, particularly navigating complex and unanticipated scenarios, demands
sophisticated reasoning and planning capabilities. While Multi-modal Large Language …

The rapid advancement of deep learning has intensified the need for comprehensive data
for use by autonomous driving algorithms. High-quality datasets are crucial for the …

Vision-based occupancy prediction, also known as 3D Semantic Scene Completion (SSC),
presents a significant challenge in computer vision. Previous methods, confined to onboard …

Vision-based occupancy prediction, also known as 3D Semantic Scene Completion (SSC),
presents a significant challenge in computer vision. Previous methods, confined to onboard …

Closed-loop simulation environments play a crucial role in the validation and enhancement
of autonomous driving systems (ADS). However, certain challenges warrant significant …

The rapid development of the autonomous driving industry has led to a significant
accumulation of autonomous driving data. Consequently, there comes a growing demand …

Abstract Graph Machine Learning (GML) has gained considerable attention in modeling
complex graph-structured data, but many of them focus on collecting high-quality data (ie …

With the evolution of data-driven autonomous driving technology, transferring driving
responsibility from humans to machines is now feasible. Addressing the long-tail distribution …

Large language models (LLMs) have shown great promise in task execution, but traditional
closed-loop systems limit their planning efficiency. Addressing this challenge, we introduce …