在許多關於車輛安全的應用中，都需要車輛的軌跡資料，例如左轉輔助及前方碰撞警告。可惜的是，透過全球定位系統 (GPS) 收集到的車輛軌跡資料，受到 GPS 的限制或較低的取樣率影響，常常不夠準確。因此，如何清理並校正收集到的軌跡資料是一個重要的問題。而相較於其他的軌跡清理的研究，基於 HMM 的軌跡清理方式似乎較有希望達到效果。在此研究中，我們提出了使用三種不同的量測值來評估基於 HMM 的軌跡清理效能，分別是：Hausdorff 距離，Fréchet 距離以及 Average Fréchet 距離。在我們的實驗中，使用基於 HMM 的軌跡清理，並利用前面提出的三個量測值來找出對於這個基於 HMM 的軌跡清理最好的參數設定。;The vehicle trajectories are required in many vehicle safety applications, such as Left Turn Assist (LTA) and Front Collision Warning (FCW). Unfortunately, the vehicle trajectories collected by Global Positioning System are often inaccurate due to the limitation of GPS or low sampling rate. How to cleanse and calibrate the collected trajectory is therefore an important issue. The HMM based trajectory data cleansing seems to be promising among different research works of trajectory cleansing, but it does not provide adquate quantitative analysis to evaluate its performance. In this research, we propose to use three different distance measurements: Hausdorff distance, Fréchet distance, and Average Fréchet distance, to evaluate the performance of HMM based trajectory cleansing. In our HMM-based trajectory cleansing experiments, the three propose measurements are used to identify the best parameter settings for the HMM-based trajectory cleansing.
