整合视觉大语言模型和推理链,用于驾驶员行为分析和风险评估
Kunpeng Zhang1, Shipu Wang2, Ning Jia3
1College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China; Department of Automation, Tsinghua University, Beijing 100084, China.
本研究引入了一种新的分心驾驶分类 (DDC) 方法,使用视觉大语言模型 (LLM) 分析驾驶员姿势以提高安全性. 分心驾驶语言模型 (DDLM) 提供了更好的分心检测和推理能力.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 司机的行为显著影响道路安全.
- 对于分类驾驶员分心的复杂方法是必不可少的.
- 现有的模型可能缺乏解释性和推理能力.
研究的目的:
- 使用视觉大语言模型 (LLM) 开发一种新的分心驾驶分类 (DDC) 方法.
- 为了提高驾驶员分心的检测和分类准确度.
- 改善分心评估背后的解释性和推理.
主要方法:
- 使用了一个视觉的大型语言模型 (LLM),命名为分心驾驶语言模型 (DDLM).
- 整体内置的人体姿势估计分析关键姿势特征 (头部,手).
- 集成了一个推理链框架,以提供对分类的解释.
主要成果:
- DDLM在分类驾驶员行为和相关风险水平方面表现出更好的表现.
- 在100驱动器数据集上的零射击和少数射击学习场景中取得了卓越的结果.
- 提供了对驾驶员分心的详细,上下文意识的评估.
结论:
- DDLM是用于准确检测和分析驾驶分心的先进工具.
- 该方法显示了改善整体驾驶安全的巨大潜力.
- 姿势估计和推理的整合增强了这项任务的LLM能力.
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