Traditional approaches to safety event analysis in autonomous systems have relied on
complex machine and deep learning models and extensive datasets for high accuracy and …

Advancements in autonomous driving have increasingly focused on end-to-end (E2E)
systems that manage the full spectrum of driving tasks, from environmental perception to …

In this paper, we introduce GRID, a novel paradigm that reframes a broad range of visual
generation tasks as the problem of arranging grids, akin to film strips. At its core, GRID …

Motivated by the emergent reasoning capabilities of Vision Language Models (VLMs) and
their potential to improve the comprehensibility of autonomous driving systems, this paper …

Autonomous driving is a challenging task that requires perceiving and understanding the
surrounding environment for safe trajectory planning. While existing vision-based end-to …

Language-image pre-training faces significant challenges due to limited data in specific
formats and the constrained capacities of text encoders. While prevailing methods attempt to …

The Multi-modal Large Language Models (MLLMs) with extensive world knowledge have
revitalized autonomous driving, particularly in reasoning tasks within perceivable regions …