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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/96115完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李綱 | zh_TW |
| dc.contributor.advisor | Kang Li | en |
| dc.contributor.author | 鄭閔 | zh_TW |
| dc.contributor.author | Min Cheng | en |
| dc.date.accessioned | 2024-10-31T16:05:40Z | - |
| dc.date.available | 2024-11-01 | - |
| dc.date.copyright | 2024-10-31 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-06 | - |
| dc.identifier.citation | Surface vehicle recommended practice, SAE J3016, Sep 2016.
N. Kalra and S. M. Paddock, “Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?,” Transportation Research Part A: Policy and Practice, Vol. 94, pp. 182-193, Dec. 2016. J. Sun, H. Zhang, H. Zhou, R. Yu, and Y. Tian, “Scenario-based test automation for highly automated vehicles: A review and paving the way for systematic safety assurance,” IEEE Transactions on Intelligent Transportation Systems, Vol. 23, No. 9, pp. 14088-14103, Sep. 2022. C. Amersbach and H. Winner, “Defining required and feasible test coverage for scenario-based validation of highly automated vehicles,” in IEEE Intelligent Transportation Systems Conference (ITSC), 2019, pp. 425-430. W. H. Ding, C. J. Xu, M. Arief, H. H. Lin, B. Li, and D. Zhao, “A survey on safety-critical driving scenario generation - A methodological perspective,” IEEE Transactions on Intelligent Transportation Systems, Vol. 24, No. 7, pp. 6971-6988, Jul. 2023. C. Wang, C. Popp, and H. Winner, “Acceleration-based collision criticality metric for holistic online safety assessment in automated driving,”IEEE Access, Vol. 10, pp. 70662-70674, Jun. 2022. T. Menzel, G. Bagschik, and M. Maurer, “Scenarios for development, test and validation of automated vehicles,” in IEEE Intelligent Vehicles Symposium (IV), 2018, pp. 1821-1827. G. Bagschik, T. Menzel, and M. Maurer, "Ontology based Scene Creation for the Development of Automated Vehicles," in IEEE Intelligent Vehicles Symposium (IV), 2018, pp. 1813-1820. Z. Zhong, Y. Tang, Y. Zhou, V. d. O. Neves, Y. Liu, and B. Ray, “A survey on scenario-based testing for automated driving systems in high-fidelity simulation,” arXiv preprint, arXiv:2112.00964, Dec. 2021. S. Kuutti, S. Fallah, and R. Bowden, "Training Adversarial Agents to Exploit Weaknesses in Deep Control Policies," in IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 108-114. S. Feng, X. Yan, H. Sun, Y. Feng, and H. X. Liu, "Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment," Nature Communications, Vol. 12, No. 1, p. 748, Feb. 2021. J. M. Scanlon, K. D. Kusano, T. Daniel, C. Alderson, A. Ogle, and T. Victor, “Waymo simulated driving behavior in reconstructed fatal crashes within an autonomous vehicle operating domain,” Accident Analysis & Prevention, Vol. 163, p. 106454, Dec. 2021. I. R. Jenkins, L. O. Gee, A. Knauss, H. Yin, and J. Schroeder, “Accident scenario generation with recurrent neural networks,” in International Conference on Intelligent Transportation Systems (ITSC), 2018, pp. 3340-3345. [14] J. Langner, J. Bach, L. Ries, S. Otten, M. Holzäpfel, and E. Sax, “Estimating the uniqueness of test scenarios derived from recorded real-world-driving-data using autoencoders,” in IEEE Intelligent Vehicles Symposium (IV), 2018, pp. 1860-1866. F. Montanari, R. German, and A. Djanatliev, “Pattern recognition for driving scenario detection in real driving data,” in IEEE Intelligent Vehicles Symposium (IV), 2020, pp. 590-597. H. Nakamura et al., "Defining Reasonably Foreseeable Parameter Ranges Using Real-World Traffic Data for Scenario-Based Safety Assessment of Automated Vehicles," IEEE Access, Vol. 10, 2022, pp. 37743-37760. F. Montanari, C. Stadler, J. Sichermann, R. German, and A. Djanatliev, “Maneuver-based resimulation of driving scenarios based on real driving data,” in IEEE Intelligent Vehicles Symposium (IV), 2021, pp. 1124-1131. A. Tenbrock, A. König, T. Keutgens, and H. Weber, “The ConScenD dataset: Concrete scenarios from the highD dataset according to ALKS regulation UNECE R157 in OpenX,” in IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), 2021, pp. 174-181. D. Karunakaran, J. S. Berrio, S. Worrall, and E. Nebot, “Automatic lane change scenario extraction and generation of scenarios in OpenX format from real-world data,” arXiv preprint, arXiv:2203.07521, Mar. 2022. P. Elspas, J. Langner, M. Aydinbas, J. Bach, and E. Sax, “Leveraging regular expressions for flexible scenario detection in recorded driving data,” in IEEE International Symposium on Systems Engineering (ISSE), 2020 , pp. 1-8. W. Wang and D. Zhao, “Extracting traffic primitives directly from naturalistically logged data for self-driving applications,” IEEE Robotics and Automation Letters, Vol. 3, No. 2, pp. 1223- 1229, Apr. 2018. G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, Vol. 313, No. 5786, pp. 504-507, Jul. 2006. D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint, arXiv:1312.6114, Dec. 2013. P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in International Conference on Machine learning, 2008, pp. 1096-1103. T. Kieu, B. Yang, C. Guo, and C. S. Jensen, “Outlier detection for time series with recurrent autoencoder ensembles,” in International Joint Conference on Artificial Intelligence, 2019, pp. 2725–2732. Y. Su, Y. Zhao, C. Niu, R. Liu, W. Sun, and D. Pei, “Robust anomaly detection for multivariate time series through stochastic recurrent neural network,” in ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, pp. 2828–2837. O. I. Provotar, Y. M. Linder, and M. M. Veres, “Unsupervised anomaly detection in time series using LSTM-based autoencoders,” in IEEE International Conference on Advanced Trends in Information Theory (ATIT), 2019, pp. 513-517. C. Olah, “Understanding LSTM networks,” [Online]. Available: https://colah.github.io/posts/2015-08-Understanding-LSTMs/. Accessed on: Dec. 30, 2023. K. Hundman, V. Constantinou, C. Laporte, I. Colwell, and T. Soderstrom, “Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding,” in ACM SIGKDD international conference on knowledge discovery & data mining, 2018, pp. 387-395. İlyurek Kılıç, “ROC curve and AUC: Evaluating model performance,” [Online]. Available: https://medium.com/@ilyurek/roc-curve-and-auc-evaluating-model-performance-c2178008b02. Accessed on: Mar. 3, 2024. F. Melo, “Area under the ROC curve,” in Encyclopedia of Systems Biology, 1st Edition, New York, U.S.A.: Springer New York, 2013, pp. 38-39. Y. Kang, H. Yin, and C. Berger, “Test your self-driving algorithm: An overview of publicly available driving datasets and virtual testing environments,” IEEE Transactions on Intelligent Vehicles, Vol. 4, No. 2, pp. 171-185, Mar. 2019. J. Cai, W. Deng, H. Guang, Y. Wang, J. Li, and J. Ding, “A survey on data-driven scenario generation for automated vehicle testing,” Machines, Vol. 10, No. 11, p. 1101, Nov. 2022. D. Henclewood, M. Abramovich, and B. Yelchuru. Safety Pilot Model Deployment Data. U.S. DEPARTMENT of TRANSPORTATION. Washington, U.S.A. [Online]. Available: https://www.transportation.gov/ X. Huang et al., “The apolloscape dataset for autonomous driving,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018, pp. 1067-1073. F. Yu et al., “BDD100K: A diverse driving dataset for heterogeneous multitask learning,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 2633-2642. A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The KITTI dataset,” The International Journal of Robotics Research, Vol. 32, No. 11, pp. 1231-1237, Aug. 2013. V. Punzo, M. T. Borzacchiello, and B. Ciuffo, “On the assessment of vehicle trajectory data accuracy and application to the Next Generation SIMulation (NGSIM) program data,” Transportation Research Part C: Emerging Technologies, Vol. 19, No. 6, pp. 1243-1262, Dec. 2011. R. Krajewski, J. Bock, L. Kloeker, and L. Eckstein, “The highD Dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems,” in International Conference on Intelligent Transportation Systems (ITSC), 2018, pp. 2118-2125. H. Yu et al., “Dair-v2x: A large-scale dataset for vehicle-infrastructure cooperative 3d object detection,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 21361-21370. G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez, “The SYNTHIA dataset: A large collection of synthetic images for semantic segmentation of urban scenes,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 3234-3243. J. Fang et al., “Augmented LiDAR simulator for autonomous driving,” IEEE Robotics and Automation Letters, Vol. 5, No. 2, pp. 1931-1938, Apr. 2020. W. Li et al., “AADS: Augmented autonomous driving simulation using data-driven algorithms,” Science robotics, vol. 4, no. 28, p. eaaw0863, 2019. H. Winner, K. Lemmer, T. Form, and J. Mazzega, “PEGASUS—First steps for the safe introduction of automated driving,” in Road Vehicle Automation 5, 1st Edition, Berlin, Germany: Springer, 2019, pp. 185-195. S. Thal et al., "Incorporating safety relevance and realistic parameter combinations in test-case generation for automated driving safety assessment," in International Conference on Intelligent Transportation Systems (ITSC), 2020, pp. 1-6. A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “CARLA: An Open Urban Driving Simulator,”presented at Proceedings of the 1st Annual Conference on Robot Learning, Mountain View, U.S.A., 2017. Pyoscx, "PyOSC Scenario Generation," GitHub repository, 2023. [Online]. Available: https://github.com/pyoscx/scenariogeneration. [Accessed: June 18, 2024]. Y. Zhang, A. Carballo, H. Yang, and K. Takeda, "Perception and sensing for autonomous vehicles under adverse weather conditions: A survey," ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 196, pp. 146-177, Feb. 2023. M. Rosenblatt, “Remarks on some nonparametric estimates of a density function,” The Annals of Mathematical Statistics, Vol. 27, No. 3, pp. 832-835, Sep. 1956. E. Parzen, “On estimation of a probability density function and mode,” The Annals of Mathematical Statistics, Vol. 33, No. 3, pp. 1065-1076, Sep. 1962. X. Wang, M. Yang, and D. Hurwitz, "Analysis of cut-in behavior based on naturalistic driving data," Accident Analysis & Prevention, Vol. 124, pp. 127-137, Mar. 2019. D. Zhao et al., "Accelerated Evaluation of Automated Vehicles Safety in Lane-Change Scenarios Based on Importance Sampling Techniques," IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 3, pp. 595-607, 2017. esmini, "esmini: An open-source platform for simulation of intelligent driving systems," GitHub repository, 2023. [Online]. Available: https://github.com/esmini/esmini. [Accessed: Aug. 3, 2024]. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/96115 | - |
| dc.description.abstract | 本研究針對自動駕駛系統(Automated Driving System, ADS)的測試，提出了一套從真實世界資料中提取有用資訊，轉化為測試情境，並建立模擬平臺的方法。流程包括數據預處理、車輛行為篩選、車輛行為資料分析和情境模擬呈現等步驟。這種方法不僅能彌補文獻中使用速度定值產生測試情境的限制，而且具有通用性，可適用於不同地區。考慮到車輛行為的複雜程度，本研究以車輛跟隨和車道變換作為示範，分別使用簡單篩選和深度學習的方式提取資料集中特定的車輛行為。在資料分析階段，能獲取不同地區的參數範圍，有助於生成具覆蓋性或專注於單一速度區間的測試情境。這些分析結果有助於更全面地瞭解並針對不同地區進行更準確的情境生成。情境模擬則結合OpenSCENARIO 格式，實現在不同測試軟體間的兼容性並提高情境測試的效率。總的來說，本研究結合數據驅動，提供了一個完整的情境生成流程，有望朝著高擬真度測試情境的方向發展，透過測試情境找出ADS 的缺陷和潛在風險。 | zh_TW |
| dc.description.abstract | This study proposes a methodology for testing Automated Driving Systems (ADS) by extracting useful information from real-world data, converting it into test scenarios, and applying it to a simulation platform. The process includes data preprocessing, vehicle maneuver filtering, vehicle maneuver data analysis, and scenario simulation presentation.This approach not only addresses the limitations of regulatory testing scenarios generated with constant speeds but also offers general applicability to different regions. Considering the complexity of vehicle maneuvers, this study demonstrates vehicle following and lane changing maneuvers by using simple filtering and deep learning methods to extract specific vehicle maneuvers from the dataset. During the data analysis phase, obtaining parameter ranges from different regions helps in generating test scenarios that are either comprehensive or focused on a single speed range. These analytical results contribute to a more thorough understanding and accurate scenario generation for different regions.The scenario simulation integrates the OpenSCENARIO format, ensuring compatibility across different testing software and enhancing the efficiency of scenario testing. Overall, this study combines data-driven approaches to provide a complete scenario generation process, aiming towards high fidelity testing scenario. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-10-31T16:05:40Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-10-31T16:05:40Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 目次
誌謝 I 摘要 II ABSTRACT III 目次 IV 圖次 VI 表次 IX 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究貢獻 2 第二章 文獻回顧 4 2.1 基於情境的方法 4 2.1.1 知識導向的方法 4 2.1.2 對抗生成的方法 6 2.1.3 數據驅動的方法 6 2.2 車輛行為辨識 7 2.3 時間序列深度異常檢測 8 2.3.1 自編碼器(Autoencoder) 8 2.3.2 循環神經網路 10 2.3.2 模型之評估標準 13 2.4 資料集介紹與應用 15 2.4.1 常見資料集 15 2.4.2 SPMD資料集 17 2.5 OpenX格式 20 第三章 研究方法 22 3.1 情境生成流程 22 3.2 SPMD資料集參數介紹 23 3.3 車輛跟隨篩選 26 3.4 車道變換篩選 28 3.4.1 實驗流程 28 3.4.2 模型訓練架構 30 3.4.3 模型分類性能 32 3.5 車輛行為資料分析 34 3.5.1 車輛跟隨 35 3.5.2 車道變換 39 3.6 模擬平臺 41 3.7 情境生成 42 第四章 研究結果 50 4.1 方法比較與分析 50 4.1.1 地區差異對情境生成的影響 50 4.1.2 情境參數選擇的比較 50 4.1.3 車輛行為還原程度的比較 51 4.1.4 車道變換篩選方法的比較 52 4.1.5 情境生成的兼容性與彈性比較 53 第五章 結論與未來建議 55 5.1 結論 55 5.2 未來建議 56 參考文獻 57 | - |
| dc.language.iso | zh_TW | - |
| dc.title | 數據驅動的高擬真度自動駕駛系統測試情境生成 | zh_TW |
| dc.title | Data-Driven High-Fidelity Testing Scenario Generation for Automated Driving System | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 周家蓓;蘇偉儁 | zh_TW |
| dc.contributor.oralexamcommittee | Chia-Pei Chou;Wei-Jiun Su | en |
| dc.subject.keyword | 自動駕駛系統,生成情境,車輛行為篩選,OpenSCENARIO 格式,數據驅動, | zh_TW |
| dc.subject.keyword | Automated Driving System,Scenario Generation,Vehicle Maneuver Filtering,OpenSCENARIO Format,Data-Driven, | en |
| dc.relation.page | 61 | - |
| dc.identifier.doi | 10.6342/NTU202403720 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-08-10 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 機械工程學系 | - |
| dc.date.embargo-lift | 2029-08-06 | - |
| 顯示於系所單位： | 機械工程學系 | |
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