The World Health Organization has listed the design of safer intersections as a key intervention to reduce global road trauma. This article presents the first study to systematically analyze the design of all intersections in a large country, based on aerial imagery and deep learning. Approximately 900,000 satellite images were downloaded for all intersections in Australia and customized computer vision techniques emphasized the road infrastructure. A deep autoencoder extracted high‐level features, including the intersection's type, size, shape, lane markings, and complexity, which were used to cluster similar designs. An Australian telematics data set linked infrastructure design to driving behaviors captured during 66 million kilometers of driving. This showed more frequent hard acceleration events (per vehicle) at four‐ than three‐way intersections, relatively low hard deceleration frequencies at T‐intersections, and consistently low average speeds on roundabouts. Overall, domain‐specific feature extraction enabled the identification of infrastructure improvements that could result in safer driving behaviors, potentially reducing road trauma.

中文翻译：

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通过从卫星图像中无监督地提取特征来识别安全的交叉口设计

世界卫生组织已将安全路口的设计列为减少全球道路创伤的关键干预措施。本文提出了一项基于航空影像和深度学习系统分析大国所有交叉口设计的研究。为澳大利亚所有交叉路口下载了大约900,000张卫星图像，并且定制的计算机视觉技术着重于道路基础设施。深度自动编码器提取了高层特征，包括交叉路口的类型，大小，形状，车道标记和复杂性，这些特征被用于对相似的设计进行聚类。澳大利亚的远程信息处理数据集将基础设施设计与在6600万公里的行驶中捕获的驾驶行为联系在一起。这表明四向交叉路口的硬加速事件（每辆车）更为频繁，三路交叉口的硬减速频率相对较低，回旋处的平均速度始终较低。总体而言，特定领域的特征提取使您能够识别基础设施的改进，从而可以改善安全驾驶行为，从而可能减少道路伤害。