Szénási Sándor
Faculty of Economics and Informatics, J. Selye University, Komárno, Slovakia.
John von Neumann Faculty of Informatics, Óbuda University, Budapest, Hungary.
PeerJ Comput Sci. 2021 Feb 23;7:e399. doi: 10.7717/peerj-cs.399. eCollection 2021.
It is expected that most accidents occurring due to human mistakes will be eliminated by autonomous vehicles. Their control is based on real-time data obtained from the various sensors, processed by sophisticated algorithms and the operation of actuators. However, it is worth noting that this process flow cannot handle unexpected accident situations like a child running out in front of the vehicle or an unexpectedly slippery road surface. A comprehensive analysis of historical accident data can help to forecast these situations. For example, it is possible to localize areas of the public road network, where the number of accidents related to careless pedestrians or bad road surface conditions is significantly higher than expected. This information can help the control of the autonomous vehicle to prepare for dangerous situations long before the real-time sensors provide any related information. This manuscript presents a data-mining method working on the already existing road accident database records to find the black spots of the road network. As a next step, a further statistical approach is used to find the significant risk factors of these zones, which result can be built into the controlling strategy of self-driven cars to prepare them for these situations to decrease the probability of the potential further incidents. The evaluation part of this paper shows that the robustness of the proposed method is similar to the already existing black spot searching algorithms. However, it provides additional information about the main accident patterns.
预计大多数因人为失误而发生的事故将由自动驾驶车辆消除。它们的控制基于从各种传感器获得的实时数据，这些数据由复杂的算法处理并通过执行器进行操作。然而，值得注意的是，这个流程无法处理意外的事故情况，比如有儿童突然跑到车辆前方或者路面意外湿滑。对历史事故数据进行全面分析有助于预测这些情况。例如，可以定位公共道路网络中与粗心行人或不良路面状况相关的事故数量明显高于预期的区域。这些信息可以帮助自动驾驶车辆的控制系统在实时传感器提供任何相关信息之前很久就为危险情况做好准备。本文提出了一种数据挖掘方法，用于处理已有的道路事故数据库记录，以找出道路网络中的黑点。下一步，使用进一步的统计方法来找出这些区域的显著风险因素，其结果可以纳入自动驾驶汽车的控制策略中，使它们为这些情况做好准备，以降低潜在后续事故的概率。本文的评估部分表明，所提出方法的稳健性与现有的黑点搜索算法相似。然而，它提供了有关主要事故模式的额外信息。
