道路环境美学是否影响自动驾驶汽车的危险驾驶行为? 通过可解释的机器学习和随机参数的多项逻辑与异质性的道路准备情况的评估
Sizhe Yao1, Bo Yu1, Yuren Chen1
1Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, 4800 Cao'an Highway, Shanghai, 201804, China.
Accident; analysis and prevention
|December 10, 2024
概括
道路美学影响自动驾驶汽车 (AV) 的驾驶行为. 本研究引入了一种新的模型,用于使用道路环境美学来评估AV道路准备性 (RRAV),提高风险驾驶行为的预测准确性 (RDBAV).
科学领域:
- 道路环境设计 道路环境设计
- 自动驾驶汽车 (AV) 的感知
- 机器学习用于智能交通系统.
背景情况:
- 道路美学增强了人类的驾驶体验和行为,但在自动驾驶汽车 (AV) 道路准备性评估 (RRAV) 中被忽视.
- 当前的RRAV模型没有考虑道路环境美学对AV的潜在影响.
- 了解道路美学和AV行为之间的关系对于开发先进的AV系统至关重要.
研究的目的:
- 调查道路环境美学对自动驾驶汽车 (RDBAV) 危险驾驶行为的影响.
- 提出一种新的RRAV评估模型,将道路环境美学纳入其中.
- 提高自动驾驶汽车的安全性,并为人类驾驶员和自动驾驶汽车优化道路设计.
主要方法:
- 收集了真实自动驾驶数据,包括1491个纵向和225个横向RDBAV事件以及相应的道路环境图像.
- 开发了一种道路环境审美学的定量模型,从自然,生动,多样和统一中提取了38个特征.
- 使用可解释的机器学习 (XGBoost与SHAP) 来构建RRAV模型,根据美学特征预测RDBAV.
主要成果:
- 基于XGBoost的RRAV模型实现了高预测准确度:96.9%的纵向RDBAV和91.8%的横向RDBAV.
- SHAP分析量化了特定美学特征对RDBAV的影响,提供了整体和个别解释.
- 分析显示,在纵向RDBAV的左视线曲线长度和主导颜色等因素中存在显著的异质性.
结论:
- 道路环境美学显著影响AV危险驾驶行为 (RDBAV).
- 拟议的整合美学的RRAV模型与现有方法相比,提供了更高的预测准确性.
- 这些发现支持人类类型的AV感知系统的开发和以美学为导向的道路设计优化.
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