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基于Vision Transformer和SHAP的自动驾驶汽车的可解释的安全评估场景的整合方法

Minhee Kang¹, Keeyeon Hwang¹, Young Yoon²

¹School of Electrical Engineering, Korea Advanced Institute of Science and Technology(KAIST), Daejeon, 34141, Republic of Korea.

Accident; analysis and prevention

|January 14, 2025

概括

一个新的框架使用可解释的AI (XAI) 和现实世界驾驶数据生成可靠的自动驾驶汽车 (AV) 安全评估场景. 这种方法通过为高速公路和城市道路创建逻辑,具体的场景来增强AV安全验证.

科学领域:

自主驾驶系统自主驾驶系统
交通运输中的人工智能
道路安全工程道路安全工程

背景情况:

自动驾驶汽车 (AV) 接近商业化,需要强大的安全验证方法.
目前基于场景的AV安全评估方法面临数据,AI模型和标准化的局限性.
人驾驶汽车 (HV) 与自动驾驶汽车共存,创造了复杂的现实世界驾驶动态,必须加以解决.

研究的目的:

提出一个整体框架,用于生成功能,逻辑和具体的自动驾驶汽车安全评估场景.
为了解决现有的数据驱动场景生成方法的缺陷.
提高AV安全评估的可靠性和可解释性.

主要方法:

开发了一个整合可解释AI (XAI) 和实时驱动LiDAR数据的框架,以创建可解释的场景 (X-场景).
利用LiDAR点云数据和动态特征提取的声音化.
使用视觉XAI和视觉变压器 (ViT) 进行危急情况分类和注意力地图生成.
应用了SHapley添加式解释 (SHAP) 来确定特征重要性和相关性分析,以选择相关的场景标准.

主要成果:

为高速公路和城市道路环境创建了X场景,其中包含了自我车辆和周围对象参数.
该框架提供了一种系统方法,用于创建高度可信的AV安全评估场景.
解释了场景选择过程,提高了透明度和可靠性.

结论:

拟议的框架提供了一个综合解决方案,用于生成可靠的AV安全评估场景.
可解释AI (XAI) 和SHAP分析对于选择相关特征和确保场景有效性至关重要.
这种方法为自动驾驶汽车的商业化提供了更可靠的安全验证.

关键词:

3D-liDAR是一个3D-liDAR.自动化车辆自动化车辆这就是 SHAP SHAP 的意思.安全的安全的安全的安全的安全.一个场景,一个场景.视觉变压器视觉变压器

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