通过大型视觉语言模型改善自动驾驶环境中的行人交叉场景的智能感知和决策优化
Xiao Teng1,2, Lin Huang3, Zhenjiang Shen4,5
1Faculty of Transdisciplinary Sciences, Institute of Philosophy in Interdisciplinary Sciences, Kanazawa University, Kanazawa, 920-1192, Japan.
Scientific reports
|August 25, 2025
概括
这项研究使用视觉语言模型 (VLM) 创建自动驾驶的智能行人交叉系统. 该系统通过处理多模式数据来提高安全性,以准确地感知和预测风险.
科学领域:
- 人工智能
- 计算机视觉
- 智能交通系统
背景情况:
- 自动驾驶系统需要对行人交叉场景进行强有力的感知和决策.
- 传统的方法难以应对动态环境的复杂性和不同行人的行为.
- 整合先进的人工智能模型对于提高智能交通的安全性和可靠性至关重要.
研究的目的:
- 使用视觉语言模型 (VLM) 开发自动驾驶的智能行人交叉场景系统.
- 在复杂的交通环境中提高感知精度,风险预测和决策.
- 为自动驾驶汽车与行人互动建立标准化,数据驱动的框架.
主要方法:
- 基于国际自动驾驶标准的行人交叉场景的分类.
- 为JSON格式生成结构化场景检查列表的标准化提示程序的开发.
- 多模式数据处理和行为建模用于碰撞风险预测和安全建议.
主要成果:
- 获得了93.05%的感知精度.
- 风险预测 (85.91%) 和决策规则 (87.72%) 的一致率很高.
- 成功地将视觉感知与推理结合在一起,从而形成结构化和可解释的决策.
结论:
- 基于VLM的系统为提高自动驾驶的感知,预测和决策提供了一个新的技术框架.
- 这种方法克服了传统方法在处理复杂的行人行为和动态环境方面的局限性.
- 该系统为行人繁多的区域提供了更安全,更可靠的自主导航的基础.
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