دورية أكاديمية
Risky Traffic Situation Detection and Classification Using Smartphones
| العنوان: | Risky Traffic Situation Detection and Classification Using Smartphones |
|---|---|
| المؤلفون: | Akira Uchiyama, Akihito Hiromori, Ryota Akikawa, Hirozumi Yamaguchi, Teruo Higashino, Masaki Suzuki, Yasuhiko Hiehata, Takeshi Kitahara |
| المصدر: | IEEE Open Journal of Intelligent Transportation Systems, Vol 4, Pp 846-857 (2023) |
| بيانات النشر: | IEEE, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Transportation engineering LCC:Transportation and communications |
| مصطلحات موضوعية: | Risky traffic situation, smartphone, traffic safety, Transportation engineering, TA1001-1280, Transportation and communications, HE1-9990 |
| الوصف: | Behind many traffic accidents, there are more frequent minor incidents (risky traffic situations) that may lead to severe accidents. Analyzing such minor incidents effectively reduces accidents, but the challenge is to design a method to collect and analyze such incident information. In this paper, we propose a novel platform that aggregates behavioral data from pedestrians and drivers using their smartphones and recognizes risky traffic situations from the aggregated data. We design a two-stage approach where the smartphones of pedestrians and vehicles act as local anomaly detectors for triggering the event detector and classifier in the post-stage at the cloud server to suppress the processing and communication overhead. We also introduce an unsupervised learning system to cope with unseen risky situations enabled by joint utilization of the autoencoder-based anomaly detector and the risky situation classifier. The evaluation is conducted through both simulation and real experiments. The simulation result shows the risky situation detector achieves an F-measure of 0.89. We also collected real data at a car driving course to evaluate the risky situation classifier. From the results, we have confirmed that the proposed method succeeded in classifying three risky traffic situations involving pedestrians and/or vehicles with an accuracy of 89.3%. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2687-7813 |
| العلاقة: | https://ieeexplore.ieee.org/document/10318156Test/; https://doaj.org/toc/2687-7813Test |
| DOI: | 10.1109/OJITS.2023.3333263 |
| الوصول الحر: | https://doaj.org/article/3f1122e539d7408883527a9757388cf1Test |
| رقم الانضمام: | edsdoj.3f1122e539d7408883527a9757388cf1 |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 26877813 |
|---|---|
| DOI: | 10.1109/OJITS.2023.3333263 |