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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. And the problem of precisely and dynamically predicting road safety index 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 provide an opportunity to conduct vehicle safety analysis. In this paper, we first discuss the mobile sensing data collection in VANET and then identify two main challenging issues in vehicle safety analysis in VANET, 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 the limitation discussion. Furthermore, in order to improve the vehicle safety, we propose a new deep learning framework (DeepRSI) to conduct real-time road safety index prediction from the data mining point of view. Specially, 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 VANET.

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