A data mining approach for traffic accidents, pattern extraction and test scenario generation for autonomous vehicles
A data mining approach for traffic accidents, pattern extraction and test scenario generation for autonomous vehicles
复制标题
DOI：
10.1016/j.ijtst.2022.10.002
复制
发表时间：
2023-12-01
影响因子：
--
通讯作者：
Jennings, Paul
中科院分区：
文献类型：
--
作者：
Esenturk, Emre;Turley, Daniel;Jennings, Paul
为了有效地打击交通事故，分析和了解与事故相关的条件是非常重要的。这种分析可以作为(I)通过找出事故前状况之间的联系来制定应对措施的基础；(Ii)制定积极主动的战略，通过使车辆更安全来防止事故发生。本文对这两种方法的发展做出了贡献。对于(I)，人们需要识别事故的模式。对于(Ii)，引入联网和自动化车辆(CAV)是一个很有前途的解决方案。然而，骑兵在部署在公共道路上之前，需要在各种交通场景下进行测试，以证明它们的安全性。这就需要对骑士队的高质量测试场景有很大的需求。本文实现了两个目标。首先，它分析了过去的交通事故(英国的STATS19数据库)，以确定不同事故数据中的趋势，并揭示事故前条件之间的关系。这是使用集群算法(ROCK)来完成的。结果出现了七个截然不同的大星系团。然后，使用频率分析和几何分析进一步分析这些集群中的每一个的含义。其次，通过利用每个集群中的信息系统地为CAV开发测试用例场景来支持主动路线(II)，这些场景反映了各个集群的风险易发状况。这是使用数据挖掘方法(市场篮子算法)和对集群的进一步几何解释来完成的。这样，就形成了带有集群特征的明确情景。(C)2022年同济大学和同济大学出版社。这是一篇在CC By-NC-ND许可证(http://creativecommons.org/许可证/By-NC-ND/4.0/)下开放获取的文章。
To effectively fight against traffic accidents, it is of great importance to analyse and understand the conditions that are linked with accidents. Such an analysis can serve as the basis to (i) develop reactive measures by finding the links between the pre-accident conditions (ii) devise proactive strategies that will prevent the occurrence of accidents by making the vehicles safer. This paper contributes to advancement of both approaches. For (i), one needs to identify the patterns in accidents. For (ii), introduction of Connected and Automated Vehicles (CAVs) is a promising solution. However CAVs need to be tested under numerous traffic scenarios to prove their safety before their deployment on public roads. This necessitates a great demand for high quality test scenarios for CAVs. This paper achieves two goals. First, it analyses the past traffic accidents (UK's STATS19 database) to identify trends in the heterogeneous accident data and unravel the relationships between pre-accident conditions. This is done using a clustering algorithm (ROCK). Seven distinct large clusters emerge as a result. Each of these clusters are then further analysed for their meaning using the frequency analysis and geometric analysis. Secondly the paper underpins the proactive route (ii) by systematically developing, using the information in each cluster, test-case scenarios for CAVs which reflect the risk-prone conditions of the respective clusters. This is done using a data mining method (Market Basket algorithm) and further geometric interpretation of clusters. This way explicit scenarios are developed carrying the characteristics of the clusters that they come from.(c) 2022 Tongji University and Tongji University Press. Publishing Services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).