基於生物特徵的異常行為識別系統在真實車輛的可應用性研究

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姓名

侯家豪(Hou, Chia-Hao) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於生物特徵的異常行為識別系統在真實車輛的可應用性研究
(The Applicability of Biometric-Based Driver Abnormal Behavior Detection System in Real Vehicle)

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摘要(中)

交通事故已為10大死因的其中之一，而異常駕駛行為相當容易引起交通事故，為了識別異常行為，需要建構駕駛行為模型，過去有許多關於建構駕駛行為模型的研究，但是許多需要額外的儀器，實用上造成額外的經濟負擔或不便。隨著穿戴式裝置的普及與目前新款汽車逐漸可以對應可攜裝置界面，同時市面上的穿戴式裝置搭載的感測器如加速度計、陀螺儀與磁力計，令使用穿戴式裝置建構駕駛者行為模型有新的可能性。在本研究中，針對實驗室過去基於模擬環境駕駛資料提出的方法，為了得知該系統方法可否移植到真實車輛駕駛中，系統性的分析其方法在真實駕駛環境與模擬駕駛環境的差異是否會對識別效能造成顯著影響，並證實持續性小震動對智慧手錶之駕駛者行為模型建構方法會造成顯著負面影響，需要用中值濾波器濾除；不會因突發性大震動對識別效能造成顯著影響，同時我們提出了一套藉由車內環境感測器濾除所有震動的演算法，但經實驗證實此方法無效，經研究分析發現在讓兩個感測器讀數投影到同一座標系這件事會導致辨識率下將，才導致濾除演算法無效。

摘要(英)

Abnormal driving behaviors can easily cause traffic accidents. To identify abnormal driving behaviors of a people, driving behavior modeling is crucial. Smartwatch becomes more and more common and we can use it to analyze driving behavior. In this research, we analysis whether the difference between the real road driving and the driving simulator will have a significant effect on the method our laboratory proposed that modeling the distribution of the hand-movement feature of the driver obtained from the smartwatch by Gaussian mixture models (GMMs).We prove that our method would have significant negative impact to continuously vibration and we need to use median filter to do data pre-processing. Also, we prove that method won’t have significant impact to suddenly huge vibration. Although in this work, we proposed a filter to filtering all vibration by another car environment sensor reading, but it isn’t work when experiment. We found that align driver behavior sensor and car environment sensor let the accuracy become worse.

關鍵字(中)

★ 異常行為偵測
★ 高斯混合模型
★ 支持向量機
★ 穿戴式裝置

關鍵字(英)

論文目次

中文摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vii
一、緒論 1
1-1 研究背景 1
1-2研究動機 2
1-3研究目的 2
1-4論文架構 2
二、相關研究 3
2-1 駕駛者行為模型相關研究 3
2-2 支持向量機 3
2-3 生物特徵驗證效能評估指標 5
2-4 引導影像濾波器 5
三、Android 感測器 7
3-1感測器 8
3-2投影感測器讀數至世界座標系 8
四、系統架構 9
4.1 資料特徵擷取 10
4.2 特徵轉換 10
4.3 駕駛者模型建構 13
4.4 異常行為偵測 14
五、研究方法與設計 15
5-1 真實環境中可能會影響辨識率的變因 16
5-2 持續性小震動 18
5-3 突發性劇烈震動 19
5-4 即時雜訊濾除方法 21
六、實驗環境 23
6-1模擬駕駛環境 23
6-2 真實駕駛環境 24
6-3 轉彎切割 28
七、實驗與討論 32
7-1實驗一：真實環境與模擬環境識別效能之比較 32
7-1-1 結果分析 34
7-2 實驗二:持續性小震動對辨識率影響 34
7-2-1結果分析 37
7-3 實驗三:突發性大震動對辨識率影響 37
7-3-1結果分析 40
7-4 實驗四:新提出之濾波演算法對辨識率影響 41
7-4-1結果分析 43
八、結論 49
參考文獻 50

參考文獻

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指導教授

梁德容

審核日期

2017-8-22

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