系統識別號 U0026-0208202023514600
論文名稱(中文) 基於結合CNN與LSTM神經網路之車輛碰撞風險預測
論文名稱(英文) Risk Prediction of Vehicle Collision Based on A Combined Neural Network of CNN and LSTM
校院名稱 成功大學
系所名稱(中) 交通管理科學系
系所名稱(英) Department of Transportation & Communication Management Science
學年度 108
學期 2
出版年 109
研究生(中文) 謝辰陽
研究生(英文) Chen-Yang Hsieh
學號 R56071088
學位類別 碩士
語文別 英文
論文頁數 87頁
口試委員 指導教授-胡大瀛
中文關鍵字 車輛碰撞  自駕車  長短期記憶網路  卷積神經網路  圖像序列 
英文關鍵字 Vehicle Collision  Autonomous Vehicles  LSTM  CNN  Image Sequence 
中文摘要 根據內政部警政署的統計,中華民國臺灣在2018年發生了超過三十二萬起交通事故,造成將近一千五百人死亡。隨著自動駕駛汽車技術的發展,車輛可以藉由分析車輛上安裝的各種傳感器,如光學雷達、雷達和相機所收集的資料,評估道路安全風險並在適當的時機採取必要的預防措施。近年來有越來越多的人在自己的汽車上安裝行車紀錄器,這些行車記錄器不僅可以在交通事故發生後釐清相關的肇事責任,還可以在行駛過程中隨時監控周圍環境的變化,進而達到行車安全的目的。
英文摘要 According to the statistics from the National Police Agency, Ministry of the Interior, in 2018, there were 320,315 traffic accidents, including 1,493 deaths in Taiwan. With the development of autonomous vehicles (AV), vehicles can analyze the data captured by sensors equipped on them like LiDARs, radars, and cameras to assess the risk of road safety and take the necessary precautions. Currently, there are more and more people install a dashboard camera (dashcam) in their cars. The dashcam cannot only clarify the responsibility of a traffic accident but also can monitor the surrounding conditions at any time while driving, which can achieve the goal of road safety.
This study collected the video data of vehicle collision provided by the Tainan City Traffic Accident Investigation Committee, including the video recorded by dashcam or closed-circuit television (CCTV) to simulate the sensor of autonomous vehicles and train the vehicle collision risk prediction models. ResNet-50 network which is a kind of pre-trained convolutional neural network (CNN) is used to capture the image features of each frame in videos. Long short-term memory (LSTM) network is good at processing time-series data is used to capture the temporal features of videos. In this study, five models based on CNN and LSTM with different structures and input data are built. F1-score is used to evaluate the performance of models. The results show that the Model 5 using both vehicle dynamic feature data and video clips data gets a 0.94 F1-score has the best performance, and the collision risk can be detected to exceed the 0.5 threshold at 2.5 to 3.0 seconds before the collision occurred. For the models only use the video data, the performance of the Model 3 gets a 0.83 F1-score, and the collision risk can be detected to exceed the 0.5 threshold at 3.0 seconds before collision.
摘要 II
誌謝 III
1.1 Research Background and Motivation 1
1.2 Research Objectives 2
1.3 Research Flow Chart 3
2.1 Autonomous Vehicles 6
2.1.1 The Development of Autonomous Vehicles 7
2.1.2 Advanced Driver Assistance System (ADAS) 9
2.2 The Applications of Deep Learning in Traffic Accident Prevention 10
2.3 Deep Learning Approaches for Image Sequence Prediction 14
2.3.1 Long Short-Term Memory (LSTM) 14
2.3.2 CNN Long Short-Term Memory (CNN-LSTM) 15
2.4 Definition of the Vehicle Dynamic Features 16
2.5 Summary 18
3.1 Research Framework 19
3.2 Long Short-Term Memory (LSTM) 22
3.3 Convolutional Neural Network (CNN) 25
3.4 Selection of the Vehicle Dynamic Features 28
3.5 The Architecture of the Prediction Model 29
3.6 Evaluation Criteria 35
3.7 Software and Package 37
4.1 Data Collection 38
4.1.1 Vehicle Dynamic Features Data 40
4.1.2 Video Clips Data and Preprocessing 42
4.2 Model Building 46
4.2.1 The Hyperparameters of Models 47
4.2.2 Model 1 50
4.2.3 Model 2 52
4.2.4 Model 3 54
4.2.5 Model 4 56
4.2.6 Model 5 58
5.1 Classification Results and Analysis 60
5.2 Collision Risk Prediction 67
5.2.1 Model 3 69
5.2.2 Model 4 73
5.2.3 Model 5 77
5.3 Summary and Future Applications 80
6.1 Conclusions 81
6.2 Suggestions 82
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