Smartphone-based Risky Traffic Situation Detection and Classification
Smartphone-based Risky Traffic Situation Detection and Classification
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DOI：
10.1109/percomworkshops48775.2020.9156157
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
Ryota Akikawa;Akira Uchiyama;Akihito Hiromori;Hirozumi Yamaguchi;T. Higashino;Masaki Suzuki;Yasuhiko Hiehata;T. Kitahara
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文献类型：
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作者：
Ryota Akikawa;Akira Uchiyama;Akihito Hiromori;Hirozumi Yamaguchi;T. Higashino;Masaki Suzuki;Yasuhiko Hiehata;T. Kitahara
虽然日本发生的交通事故数量在减少，但每年仍有大约40万起交通事故。在此类事故的背后，频发着可能导致如此严重事故的小事故(险情事件)。分析此类次要事件对减少事故是有效的，但挑战是设计和部署一种收集和分析此类事件信息的方法。行车记录器可能对此很有用，但它们不能从没有记录器的车辆上收集信息。在这项研究中，我们提出了一种平台的设计和开发，该平台聚合了行人和车辆司机使用智能手机的行为数据，并从聚合的数据中自动估计危险的交通状况。我们给出了在受控环境下对这些事件进行检测和分类的初步结果，四类分类的F值为0.89。
Although the number of traffic accidents occurring in Japan is decreasing, there still happen approximately 400,000 traffic accidents annually. Behind such accidents, there are frequent minor incidents (near-miss incidents) that may lead to such serious accidents. Analyzing such minor incidents is effective to reduce accidents, but the challenge is to design and deploy a method to collect and analyze such incident information. Drive recorders may be useful for such a purpose, but they cannot collect information from those vehicles without recorders. In this study, we propose the design and development of a platform that aggregates behavioral data from pedestrians and vehicle drivers using their smartphones, and automatically estimates risky traffic situations from the aggregated data. We present our preliminary result of detecting and classifying those events in a controlled environment and have achieved F-value 0.89 for four categories classification.