Detection of Driver’s Cognitive States Based on LightGBM with Multi-Source Fused Data
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- 版元よりダウンロードリンクを連絡
- 形態
- 価格
- 一般価格(税込)：￥6,600 会員価格(税込)：￥5,280
- 文献・情報種別
- SAE Paper
No.2022-01-0066
- 掲載ページ
- 1-12(Total 12 p)
- 発行年月
- 2022年 3月
- 出版社
- SAE International
- 言語
- 英語
- イベント
- WCX SAE World Congress Experience 2022
書誌事項
| 著者（英） | 1) Jingyuan Li, 2) Yahui Liu, 3) Xuewu Ji, 4) Shuxin Tao |
|---|---|
| 勤務先（英） | 1) Tsinghua University, 2) Tsinghua University, 3) Tsinghua University, 4) Tsinghua University |
| 抄録（英） | According to the statistics of National Highway Traffic Safety Administration, driver’s cognitive distraction, which is usually caused by drivers using mobile phones, has become one of the main causes of traffic accidents. To solve this problem and guarantee the safety of man-vehicle-road system, the most critical work is to improve the accuracy of driver’s cognitive state detection. In this paper, a novel driver’s cognitive state detecting method based on LightGBM (Light Gradient Boosting Machine) is proposed. Firstly, cognitive distraction experiments of making calls are carried out on a driving simulator to collect vehicle states, eye tracking and EEG (electron encephalogram) data simultaneously and feature extraction is conducted. Then a classifier considering road and individual characteristics used for detecting cognitive states is trained based on LightGBM algorithm, with 3 predefined cognitive states including concentration, ordinary distraction and extreme distraction. Finally, the proposed algorithm is compared with 8 other algorithms. Comparing results show LightGBM outperforms the 8 methods in accuracy, precision, recall, F1 value and macro-AUC (Area Under Curve) value. Meanwhile, the detecting performance using multi-source fused data is better than using only one or two types of data. 25 most important features are extracted using SHAP (SHapley Additive exPlanations) theory, and the interpretability of the model is improved. This cognitive state detecting method with multi-source fused data provides insights into the evaluation of driving risk, and can be applied to the design of in-vehicle auxiliary distraction warning system to reduce traffic accidents. 翻訳 |
