Recent research has found that navigation systems usually assume that all roads are equally safe, directing drivers to dangerous routes, which led to catastrophic consequences. To address this problem, this paper aims to begin the process of adding road safety awareness to navigation systems. To do so, we first created a definition for road safety that navigation systems can easily understand by adapting well-established safety standards from transportation studies. Based on this road safety definition, we then developed a machine learning-based road safety classifier that predicts the safety level for road segments using a diverse feature set constructed only from large-scale publicly available geographic data. Evaluations in four different countries show that our road safety classifier achieves satisfactory performance. Finally, we discuss the factors to consider when extending our road safety classifier to other regions and potential new safety designs enabled by our road safety predictions.
翻译：最近的研究发现,导航系统通常假定所有道路都同样安全,将驾驶员引向危险路线,从而导致灾难性后果。为解决这一问题,本文件旨在开始提高导航系统道路安全意识的进程。为此,我们首先制定了一个道路安全定义,通过修改运输研究中既定的安全标准,导航系统可以很容易地理解这一定义。根据这一道路安全定义,我们随后开发了一个基于机器的道路安全分类系统,该分类系统利用仅根据大规模公开提供的地理数据建立的不同特征,预测路段的安全水平。对四个不同国家的评估表明,我们的道路安全分类系统取得了令人满意的业绩。最后,我们讨论了在将道路安全分类系统扩大到其他地区时应考虑的因素,以及我们道路安全预测所促成的潜在新安全设计。
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