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.

中文翻译：

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支持自动驾驶策略的历史道路事故数据分析

可以预期，由于人为错误而发生的大多数事故将被自动驾驶汽车消除。它们的控制基于从各种传感器获得的实时数据，并通过复杂的算法和执行器的操作进行处理。但是，值得注意的是，该处理流程无法处理意外的情况，例如孩子在车辆前奔跑或路面打滑。对历史事故数据的全面分析可以帮助预测这些情况。例如，可以对公共道路网区域进行本地化，在该区域中，与粗心的行人或恶劣的路面状况有关的事故数量明显高于预期。在实时传感器提供任何相关信息之前，该信息可以帮助自动驾驶汽车的控制为危险情况做好准备。该手稿介绍了一种数据挖掘方法，该方法适用于已经存在的道路事故数据库记录，以查找道路网的黑点。下一步，将使用进一步的统计方法来查找这些区域的重大风险因素，并将其结果纳入自动驾驶汽车的控制策略中，以为这些情况做好准备，以减少潜在的进一步事故发生的可能性。 。本文的评估部分表明，该方法的鲁棒性与现有的黑点搜索算法相似。但是，它提供了有关主要事故模式的其他信息。