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研究生:曾煒婷
研究生(外文):Wei-Ting Tseng
論文名稱(外文):Turn Prediction for Special Intersections and Its Case Study
指導教授:孫敏德
指導教授(外文):Min-Te Sun
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:英文
論文頁數:58
中文關鍵詞:機器學習轉彎預測
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隨著人口成長所帶來的影響使得交通繁忙進而導致交通事故不斷增加,尤其在特別的路口,如交通量大、路口設計不良等等,更容易導致交通事故的發生。在這篇論文當中,我們提出一個轉彎預測演算法去預測車輛在特別的路口(T字型、Y字型或超過四條路口交會)將會行駛於哪一條道路,本系統使用路口雷達來蒐集車輛的動態資訊,並用蒐集到的資料計算出車輛相對於道路的偏轉角度,再以平滑技術來過濾掉偏轉角度中較大起伏的噪音數值。在預測系統中,我們使用袋裝與隨機森林演算法來建構預測模型,來預測車輛在未來相對於道路的偏轉角度,進而判斷該車輛未來將行駛於哪一條道路,並在必要時警示其他車輛減少交通事故的發生。為了評估預測模型的效能,實驗中我們採用雷達蒐集位於高雄凱旋路口及鎮興路口的真實資料集,實驗結果顯示隨機森林演算法在所有資料集中有最高的準確率。
The effect of growing population brings heavy traffic which in turn leads to increased number of traffic accidents. In particular, the majority of traffic accidents happen at special intersections in situations such as heavy traffic, poor intersection design, etc. In this thesis, we propose a turn prediction system to predict which road a vehicle will take at special intersection, e.g., T-junction, Y-junction, or junction where more than 4 roads meet. The proposed system uses the radar installed at the intersection to collect vehicle dynamics. The collected data is processed to calculate deflection angles of vehicles corresponding to the road. The smoothing technique is adopted to filter the noise of calculated deflection angles. The ensemble methods are utilized to construct the model to predict future deflection angles of vehicles corresponding to the road. According to the predicted deflection angle, we can predict which road a vehicle will take at a special intersection and alert other vehicles when necessary. To assess the performance of the model prediction, a real-world experiment is carried out, which utilizes radar to collect the dataset at Kaixuan 4th Rd. and Zhenxing Rd., Qianzhen Dist., Kaohsiung City, Taiwan. The experiment results show that the accuracy of the Random Forest algorithm is the highest among all datasets.
1 Introduction 1
2 RelatedWork 4
2.1 Image-based Path Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Non Image-based Path Prediction . . . . . . . . . . . . . . . . . . . . . . . 5
2.2.1 GPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2.2 Digital Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2.3 IMU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3 Preliminary 9
3.1 Smoothing techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.2 Machine learning techniques . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.2.1 Regression techniques . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2.2 Classication techniques . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2.3 Hybrid techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4 Design 17
4.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.2 Internal Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.3 Data Smoothing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.4 Turn Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.4.1 Data Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.4.2 Model Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
5 Performance 27
iii
5.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
5.2 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
5.2.1 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . 29
5.2.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 32
6 Conclusions 40
Reference 41
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