| Deep-Learning-Based Risk Prediction with Urban Sensing Data for Consumer Driving Safety | |
|---|---|
| 學年 | 113 |
| 學期 | 1 |
| 發表日期 | 2024-10-29 |
| 作品名稱 | Deep-Learning-Based Risk Prediction with Urban Sensing Data for Consumer Driving Safety |
| 作品名稱（其他語言） | |
| 著者 | Kun-Yu Lin；Pei-Yi Liu；Chih-Lin Hu；Sheng-Zhi Huang；Yung-Hui Chen；Lin Hui |
| 作品所屬單位 | |
| 出版者 | |
| 會議名稱 | 2024 IEEE 13th Global Conference on Consumer Electronics (GCCE) |
| 會議地點 | Kitakyushu, Japan |
| 摘要 | With the provision of IoT-driven and user-provided sensing data sources in smart cities, we take advantage of deep learning techniques to analyze the spatio-temporal traffic data and predict traffic risks for driving safety. Our study continues to collect the traffic data from multiple sensing sources, and meanwhile adopts both CNN and LSTM to interpret the data collection in spatial and temporal dimensions. Thus, a novel traffic risk prediction scheme based on CNN and LSTM, named TRP-CL, is proposed to generate a traffic warning map of risks and hazard situations on a grid-scaled city map. Not only a theoretic formation but also an experimental implementation of the TRP-CL scheme are made to show the practical feasibility. |
| 關鍵字 | |
| 語言 | en_US |
| 收錄於 | |
| 會議性質 | 國際 |
| 校內研討會地點 | 無 |
| 研討會時間 | 20241029~20241101 |
| 通訊作者 | |
| 國別 | USA |
| 公開徵稿 | |
| 出版型式 | |
| 出處 | |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/127935 ) |
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