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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 施吉昇 | |
dc.contributor.author | Yi-Tung Yen | en |
dc.contributor.author | 顏逸東 | zh_TW |
dc.date.accessioned | 2021-06-17T09:11:09Z | - |
dc.date.available | 2019-09-03 | |
dc.date.copyright | 2019-09-03 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-30 | |
dc.identifier.citation | [1] C. Desjardins and B. Chaib-draa, “Cooperative adaptive cruise control: A reinforcement learning approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 4, pp. 1248–1260, Dec 2011.
[2] 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 2018 IEEE 21st International Conference on Intelligent Transportation Systems (ITSC), 2018. [3] A. Reuschel, “Fahrzeugbewegungen in der Kolonne Beigleichformig beschleunigtem oder vertzogerten Leitfahrzeub, Zeit. D. (Vehicle Movements in a Platoon with Uniform Acceleration or Deceleration of the Lead Vehicle),” Oster. Ing. U. Architekt Vereines Ed., pp. 50–62 and 73–7, 1950. [4] L. A. Pipes, “An Operational Analysis of Traffic Dynamic, Journal of Applied Physics 24, pp. 271–281, 1953. [5] C.-Y. Chan, “Advancements, prospects, and impacts of automated drivingsystems,” International Journal of Transportation Science and Technology, 2017. [6] B. Schoettle and M. Sivak, “A survey of public opinion about autonomous and self-driving vehicles in the U.S., the U.K., and Australia,” 2014. [7] T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” 2015. [8] M. Zhu, Y. Wang, J. Hu, X. Wang, and R. Ke, “Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving,” 2019. [9] W. Shi and S. Dustdar, “The promise of edge computing, Computer, vol. 49, no. 5, pp. 78–81, May 2016. [10] Gettman, D., L. Pu, T. Sayed, and S. Shelby, “ Surrogate Safety Assessment Model and Validation,” 2008. [11] S. Wang, D. Jia, and X. Weng, “Deep reinforcement learning for autonomous driving,” 11 2018. [12] “ISO 15622:2018: Intelligent transport systems – Adaptive cruise control systems – Performance requirements and test procedures,” International Organization for Standardization, 2018. [13] “Freeway bureau motc, freeway and expressway traffic control regulations.” [Online]. Available: https://www.freeway.gov.tw/english/Publish.aspx?cnid=1094& p=522 [14] J. C. Hayward, “Near miss determination through use of a scale of danger,” 1972. [15] M. M. Minderhoud and P. H. Bovy, “Extended time-to-collision measures for road traffic safety assessment,” Accident Analysis & Prevention, vol. 33, no. 1, pp. 89– 97, 2001. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0001457500000191 [16] C. Oh, S. Park, and S. G. Ritchie, “A method for identifying rear-end collision risks using inductive loop detectors,” Accident Analysis & Prevention, vol. 38, no. 2, pp. 295 – 301, 2006. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0001457505001570 [17] K. Vogel, “A comparison of headway and time to collision as safety indicators,” Accident Analysis & Prevention, vol. 35, no. 3, pp. 427 – 433, 2003. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0001457502000222 [18] “Wikibooks: Fundamentals of transportation/traffic flow.” [Online]. Available: https://en.wikibooks.org/wiki/Fundamentals_of_Transportation/Traffic_Flow [19] W. J. Schakel and B. van Arem, “Improving traffic flow efficiency by in-car advice on lane, speed, and headway,” IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 4, pp. 1597–1606, Aug 2014. [20] K. Vogel, “A comparison of headway and time to collision as safety indicators,” Accident Analysis & Prevention, vol. 35, no. 3, pp. 427 – 433, 2003. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0001457502000222 [21] M. Elbanhawi, M. Simic, and R. Jazar, “In the passenger seat: investigating ride comfort measures in autonomous cars,” IEEE Intelligent Transportation Systems Magazine, vol. 7, no. 3, pp. 4–17, 2015. [22] R. Zanasi, R. Morselli, A. Visconti, and M. Cavanna, “Head-neck model for the evaluation of passenger’s comfort,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2. IEEE, 2002, pp. 1403–1408. [23] J. Elsner, “Optimizing passenger comfort in cost functions for trajectory planning,” CoRR, vol. abs/1811.06895, 2018. [Online]. Available: http: //arxiv.org/abs/1811.06895 [24] H. Bellem, B. Thiel, M. Schrauf, and J. F. Krems, “Comfort in automated driving: An analysis of preferences for different automated driving styles and their dependence on personality traits,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 55, pp. 90 – 100, 2018. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1369847817301535 [25] “Jerk (physics).” [Online]. Available: https://en.wikipedia.org/wiki/Jerk_(physics) [26] P. A. Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y.-P. Flötteröd, R. Hilbrich, L. Lücken, J. Rummel, P. Wagner, and E. Wießner, “Microscopic traffic simulation using sumo,” in The 21st IEEE International Conference on Intelligent Transportation Systems. IEEE, 2018. [Online]. Available: https://elib.dlr.de/124092/ [27] V. Milanes and S. Shladover, “Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data,” Transportation Research Part C: Emerging Technologies, vol. 48, p. 285–300, 11 2014. [28] L. Xiao, M. Wang, and B. Arem, “Realistic car-following models for microscopic simulation of adaptive and cooperative adaptive cruise control vehicles,” Transportation Research Record Journal of the Transportation Research Board, vol. 2623, 12 2017. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74954 | - |
dc.description.abstract | 隨著人工智慧以及科技的快速發展,也帶動了自駕車領域的興起,而一個良好的跟車模型在自駕車當中扮演著不可或缺的角色。一個良好的跟車模型,能使自駕車以安全、舒適且有效率的方式駕駛,不僅提高車輛行駛的安全性及乘客的舒適性,更能進一步的提升整體道路的使用率。
本研究致力於建構一個更加良好的跟車模型,根據現行文獻、法規,將安全、舒適以及效率精確地量化成報酬函數,並透過強化學習使電腦不斷地與環境互動,藉由環境的反饋學習以達到報酬最大化。 結果顯示我們的模型不僅能夠減少人類駕駛中低效率以及不安全的時間車距,也減小了過大的急煞。更進一步地,我們的模型在效率評估方面勝過了79%的人類駕駛,達到了相當於98%的最佳解。除此之外,與SUMO中的主動式定速巡航模型相比,在同樣的發車數目之下,我們能夠在同樣實驗時間區間內提升抵達車輛的數目以及平均車速,進而達到整體道路使用率的改善。 | zh_TW |
dc.description.abstract | With the rapid evolution of artificial intelligence and technology, autonomous vehicle is regarded as the future of transportation. One of the important functions that autonomous vehicle should be equipped is a well-designed car-following model. With a well-designed car-following model, autonomous vehicle can drive in a safe, comfortable and efficient manner. This will increase driving safety, passenger comfort and improve road efficiency.
This thesis designs and implements an enhanced car-following model. According to the laws, regulations and standards, we modeled the safety, comfort and efficiency into quantified reward functions. Using reinforcement learning, the network agent learns the best policy to achieve the maximum reward by repeated the learning process. The evaluation results show that our model not only reduces the number of inefficient and dangerous headways but also eliminates the jerk to achieve more efficient and comfortable driving than human drivers. The model outperformed 79% human drivers in public dataset. The achieved efficiency is 98% of the optimal bound. Furthermore, compared to the SUMO’s ACC model, given the same number of departed vehicles, our model enables more arrived vehicles and higher average speed to improve the overall road capacity. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T09:11:09Z (GMT). No. of bitstreams: 1 ntu-108-R06922083-1.pdf: 2154842 bytes, checksum: 107381b981cba0574ff223c6334211d0 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | Acknowledgments ii
摘要iii Abstract iv 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2 Background and Related Work 4 2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.1 Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . 4 2.1.2 Deep Deterministic Policy Gradient . . . . . . . . . . . . . . . . 5 2.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3 System Architecture and Problem Definition 9 3.1 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.3 Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 4 Design and Implementation 11 4.1 DDPG Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.2 Design of Reward Functions . . . . . . . . . . . . . . . . . . . . . . . . 13 4.2.1 Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.2.2 Road Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.2.3 Passenger Comfort . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.3 Dataset for training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.3.1 HighD dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.3.2 Data Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.4 Reinforcement Learning Environment Setup . . . . . . . . . . . . . . . . 21 5 Performance Evaluation 24 5.1 Reward Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 5.2 Validation of Performance Metrics . . . . . . . . . . . . . . . . . . . . . 24 5.3 Efficiency Evaluation Results . . . . . . . . . . . . . . . . . . . . . . . . 28 6 Conclusion 32 Bibliography 33 | |
dc.language.iso | en | |
dc.title | 基於深層強化學習之增強式跟車模型 | zh_TW |
dc.title | Enhanced Car-Following Model with Deep Reinforcement Learning | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林忠緯,辛賢楷 | |
dc.subject.keyword | 自駕車,機器學習,深層強化學習,跟車模型,主動式定速巡航, | zh_TW |
dc.subject.keyword | Autonomous Vehicle,Self-driving Vehicle,Adaptive Cruise Control,Car-Following Model,Machine Learning,Deep Reinforcement Learning, | en |
dc.relation.page | 35 | |
dc.identifier.doi | 10.6342/NTU201904015 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-08-30 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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