在混合交通环境中对自动驾驶汽车和传统汽车碰撞伤害严重性的联合分析:应用随机参数双变量探针模型
Jian Xiang1, Zhengwu Wang2, Yibo Chen2
1Key Laboratory of Highway Engineering of Ministry of Education, Changsha University of Science & Technology, Changsha 410114 Hunan, China; School of Transportation, Changsha University of Science & Technology, Changsha 410114 Hunan, China.
Accident; analysis and prevention
|March 31, 2025
概括
混合交通安全至关重要. 自动驾驶汽车 (AV) 与传统车辆 (CV) 驾驶员行为和复杂的道路条件面临更高的风险,与CV不同. 为了更安全的混合交通环境,需要有针对性的策略.
科学领域:
- 运输工程 运输工程
- 交通安全研究 交通安全研究
- 交通运输中的人工智能
背景情况:
- 自动驾驶汽车 (AV) 和常规汽车 (CV) 的混合交通环境带来了独特的安全挑战.
- 了解道路和建筑环境因素对这些混合环境中的碰撞伤害严重性的影响,对于未来的道路安全至关重要.
- 现有的模型可能无法完全捕捉混合交通事故数据中的复杂相互作用和未观察到的异质性.
研究的目的:
- 提出和验证一个共同的模型来分析混合交通中AV和CV的碰撞伤害严重程度.
- 识别和比较影响AVS和CVS受伤严重程度的不同因素.
- 为在混合交通环境中制定有针对性的安全策略提供数据驱动的见解.
主要方法:
- 开发一个随机参数双变量探针模型 (RBP) 以共同分析AV和CV的碰撞伤害严重程度.
- 考虑伤害结果与影响因素中未观察到的异质性之间的相关性.
- 模型验证使用加利福尼亚州699起偶尔碰撞的数据集,并与单变量和标准双变量试验模型进行比较.
主要成果:
- 与单变量和双变量试验模型相比,RBP模型显示出更高的解释能力和适合性.
- 自动驾驶车辆的碰撞受到自动驾驶车辆驾驶员对自动驾驶车辆行为的反应,不利的自然条件 (例如,黑暗) 和复杂的道路特征 (例如,Y交叉点,合并车道) 的显著影响.
- 与易受伤害的道路使用者和公共交通基础设施相关的建筑环境因素显示,与心脏病事故不同,对心脏病事故伤害严重性产生异质影响.
结论:
- 该研究强调了在混合交通中对AV和CV的不同安全风险和影响因素.
- 调查结果强调了需要在低可见性和AV和CV之间有效的通信系统中提高AV性能.
- 建议包括加强弱势道路使用者的道路基础设施,以减轻混合交通场景中的风险.
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