每位駕駛者都有屬於他們獨特的駕駛習慣，且通常在不同駕駛情境下，同一位駕駛者握住或操作方向盤的方式也不同。在本研究中，我們提出一種新的基於高斯混合模型塑模方法，此塑模方法可以改善傳統高斯混合模型在駕駛行為塑模上的問題。此外，我們提出的塑模方法可以應用在建構較佳的智慧型手錶感測器(如加速度計、方位感測器)之駕駛者認證系統或駕駛者識別系統上。為了驗證我們提出的方法之可行性，我們建構兩個實驗系統，分別為駕駛者認證系統與駕駛者識別系統。對於駕駛者認證系統的實驗結果顯示，在模擬環境中相等錯誤率(Equal Error Rate)可達4.46%；在真實駕駛環境中相等錯誤率達可11.35%。對於駕駛者識別系統而言，實驗結果顯示，在模擬環境中識別率可達87.16%；在真實環境中識別率可達73.07%。上述實驗結果皆比傳統高斯混合模型方法佳，因此，可以證實我們提出新的基於高斯混合模型塑模方法具有可用性。;All drivers have their own distinct driving habits, and usually hold and operate the steering wheel differently in different driving scenarios. In this study, we proposed a novel Gaussian mixture model (GMM)-based method that can improve the traditional GMM in modeling driving behavior. This new method can be applied to build a better driver authentication or identification system based on the accelerometer and orientation sensor of a smartwatch. To demonstrate the feasibility of the proposed method, we created two experimental systems that analyzes driving behavior using the built-in sensors of a smartwatch. The experimental results for driver authentication—an equal error rate (EER) of 4.46% in the simulated environment and an EER of 11.35% in the real-driving environment—confirm the feasibility of this approach. For driver identification, the experimental results indicated that the proposed approach had identification rates of 87.16% in a simulated environment and 73.07% in a real-driving environment.
