一个贝叶斯极值理论建模框架,用自动驾驶汽车传感器数据来评估通道范围的行人安全
Sunny Singh1, Yasir Ali2, Md Mazharul Haque1
1Queensland University of Technology, School of Civil and Environmental Engineering, Brisbane, Australia.
Accident; analysis and prevention
|December 6, 2023
概括
这项研究使用自动驾驶车辆数据和极端价值理论来模拟整个道路走廊的行人撞车风险. 区块最大模型在识别高风险行人区方面更准确.
科学领域:
- 运输工程 运输工程
- 道路安全分析 道路安全分析
- 数据科学数据科学数据科学
背景情况:
- 行人安全至关重要,但碰撞事件分散,使走廊级风险评估具有挑战性.
- 现有的研究往往侧重于十字路口,缺乏网络范围的行人互动模型.
- 自动驾驶汽车数据为全面的道路安全分析提供了丰富的,尚未开发的资源.
研究的目的:
- 开发和应用一个极端价值理论 (EVT) 框架来估计走廊范围内的行人撞车风险.
- 使用自动驾驶汽车 (AV) 传感器数据进行网络级安全分析.
- 为了比较不同EVT模型在预测行人撞车频率方面的性能.
主要方法:
- 开发了贝叶斯极值理论模型:对于区块最大值的通用极值 (GEV) 和峰值超过值的通用帕雷托值 (GPD).
- 将框架应用于来自美国迈阿密的Argoverse数据集的AV传感器数据 (LiDAR,摄像头).
- 提取了车辆和行人轨迹,使用入后时间 (PET) 确定了冲突,并将交通/行人数量和速度作为共变量.
主要成果:
- 无论是GEV还是GPD模型都提供了对历史行人撞车频率的合理估计.
- 区块最大值 (GEV) 模型的准确性高于峰值超过值 (GPD) 模型.
- 通过包括交通和行人流动变量,有效地捕捉了非静止性.
结论:
- 自动驾驶汽车传感器数据是网络级行人安全分析的宝贵资源.
- 拟议的EVT框架允许在交通网络中有效识别行人碰撞风险区域.
- 这种方法有助于理解和减轻隔离交叉路口以外的行人风险.
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