AI RESEARCH
From Segments to Scenes: Temporal Understanding in Autonomous Driving via Vision-Language Model
arXiv CS.AI
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ArXi:2512.05277v3 Announce Type: replace-cross Vision-Language Models (VLMs) are increasingly deployed as the perception and reasoning backbone of autonomous agents acting in the wild, with autonomous driving (AD) being one of the most safety-critical instances. Reliable temporal understanding is essential for such agents to anticipate events, attribute causes, and act safely in dynamic environments, yet this remains a significant challenge even for state-of-the-art (SoTA) VLMs.