Core Insights - The article discusses the advancements in Visual Language Models (VLMs) and their potential application in autonomous driving, particularly focusing on the reliability and interpretability of driving decisions generated by VLMs [3][5]. Group 1: DriveBench Overview - DriveBench is introduced as a benchmark dataset designed to evaluate the reliability of VLMs in 17 different settings, comprising 19,200 frames and 20,498 question-answer pairs [3]. - The framework covers four core tasks in autonomous driving: perception, prediction, planning, and behavior, and incorporates 15 types of Out-of-Distribution (OoD) scenarios to systematically test VLMs in complex driving environments [7][9]. Group 2: Presentation Details - The article highlights a live presentation by Shaoyuan Xie, a PhD student at the University of California, Irvine, who will discuss the empirical study on VLMs and their readiness for autonomous driving [9]. - The presentation will cover an overview of VLMs in autonomous driving, the reliability assessment of DriveBench, and future prospects for VLM applications in the industry [9].