Information about vehicle safety, such as the driving safety status and the road safety index, is of great importance to protect humans and support safe driving route planning. Despite some research on driving safety analysis, the accuracy and granularity of driving safety assessment are both very limited. Also, the problem of precisely and dynamically predicting road safety throughout a city has not been sufficiently studied and remains open. With the proliferation of sensor-equipped vehicles and smart devices, a huge amount of mobile sensing data provides an opportunity to conduct vehicle safety analysis. In this article, we first discuss mobile sensing data collection in VANETs and then identify two main challenges in vehicle safety analysis in VANETs, i.e., driving safety analysis and road safety analysis. In each issue, we review and classify the state-of-the-art vehicle safety analysis techniques into different categories. For each category, a short description is given followed by a discussion of limitations. In order to improve vehicle safety, we propose a new deep learning framework (DeepRSI) to conduct real-time road safety prediction from the data mining perspective. Specifically, the proposed framework considers the spatio-temporal relationship of vehicle GPS trajectories and external environment factors. The evaluation results demonstrate the advantages of our proposed scheme over other methods by utilizing mobile sensing data collected in VANETs.

中文翻译：

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通过深度学习和移动传感提高车辆安全


行车安全状态、道路安全指数等车辆安全信息对于保护人类、支持安全行车路线规划具有重要意义。尽管对驾驶安全分析进行了一些研究，但驾驶安全评估的准确性和粒度都非常有限。此外，精确、动态地预测整个城市的道路安全问题尚未得到充分研究并且仍然悬而未决。随着配备传感器的车辆和智能设备的激增，大量的移动传感数据为进行车辆安全分析提供了机会。在本文中，我们首先讨论 VANET 中的移动传感数据收集，然后确定 VANET 中车辆安全分析的两个主要挑战，即驾驶安全分析和道路安全分析。在每一期中，我们都会回顾最先进的车辆安全分析技术并将其分为不同的类别。对于每个类别，都会给出简短的描述，然后讨论局限性。为了提高车辆安全性，我们提出了一种新的深度学习框架（DeepRSI），从数据挖掘的角度进行实时道路安全预测。具体来说，所提出的框架考虑了车辆 GPS 轨迹和外部环境因素的时空关系。评估结果通过利用 VANET 中收集的移动传感数据证明了我们提出的方案相对于其他方法的优势。