From Segments to Scenes: Temporal Understanding in Autonomous Driving via Vision-Language Model 事件

From Segments to Scenes: Temporal Understanding in Autonomous Driving via Vision-Language Model arXiv:2512.05277v3 Announce Type: replace Abstract: 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 environm