论文标题
使用序列分析和贝叶斯网络的自动化车辆安全评估的交叉点两车崩溃方案规范
Intersection Two-Vehicle Crash Scenario Specification for Automated Vehicle Safety Evaluation Using Sequence Analysis and Bayesian Networks
论文作者
论文摘要
本文使用崩溃序列分析和贝叶斯网络建模制定了测试方案规范程序。从2016年到2018年国家公路交通安全管理崩溃报告采样系统数据库的交叉点两车崩溃数据。撞车涉及的车辆根据其初始位置和轨迹进行了专门重新编织。崩溃序列编码为包括详细的预施加事件和简洁的碰撞事件。基于序列模式，崩溃的特征为55种类型。开发了贝叶斯网络模型来描述崩溃序列类型，崩溃结果，人为因素和环境条件之间的相互关系。通过查询贝叶斯网络条件概率表来指定方案。操作设计域属性的分布（例如驾驶员行为，天气，照明条件，交叉几何，交通控制设备）是根据序列类型的条件指定的。同样，在特定的崩溃结果或操作设计域属性的组合上指定了序列类型的分布。
This paper develops a test scenario specification procedure using crash sequence analysis and Bayesian network modeling. Intersection two-vehicle crash data was obtained from the 2016 to 2018 National Highway Traffic Safety Administration Crash Report Sampling System database. Vehicles involved in the crashes are specifically renumbered based on their initial positions and trajectories. Crash sequences are encoded to include detailed pre-crash events and concise collision events. Based on sequence patterns, the crashes are characterized as 55 types. A Bayesian network model is developed to depict the interrelationships among crash sequence types, crash outcomes, human factors, and environmental conditions. Scenarios are specified by querying the Bayesian network conditional probability tables. Distributions of operational design domain attributes - such as driver behavior, weather, lighting condition, intersection geometry, traffic control device - are specified based on conditions of sequence types. Also, distribution of sequence types is specified on specific crash outcomes or combinations of operational design domain attributes.
