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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 施吉昇 | |
| dc.contributor.author | Chun-Wei Ku | en |
| dc.contributor.author | 古君葳 | zh_TW |
| dc.date.accessioned | 2021-05-12T09:35:24Z | - |
| dc.date.available | 2020-03-02 | |
| dc.date.available | 2021-05-12T09:35:24Z | - |
| dc.date.copyright | 2018-03-02 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-02-12 | |
| dc.identifier.citation | [1] “The national highway traffic safety administration research in 2015,”https://www.nhtsa.gov/equipment/safety-technologies.
[2] P. P. D. W. R. Fernandes, C. Premebida and U. Nunes, “Road Detection Using HighResolution LIDAR,” Vehicle Power and Propulsion Conference (VPPC), pp. 1–6,2014. [3] A. A. A. P. R. Cristiano Premebida, Luis Garrote and U. Nunes, “High-resolution LIDAR-based Depth Mapping using Bilateral Filter,” International Conference onIntelligent Transportation Systems (ITSC), pp. 2469–2474, 2016. [4] B.-H. L. A. L. Sang-Mook Lee, Jeong Joon Im and A. Kurdila, “A real-time grid map generation and object classification for ground-based 3D LIDAR data using image analysis techniques,” International Conference on Image Processing, pp. 2253– 2256, 2010. [5] E. Guizzo, “How Google’s self-driving car works,” IEEE Spectr. Online, vol. 18, 2011. [6] “Google just made a big move to bring down the cost of self-driving cars,” http://www.businessinsider.com/googles-waymo-reduces-lidar-cost-90-in-effort-toscale-self-driving-cars-2017-1. [7] “Unece r131 regulation,” https://www.unece.org/trans/main/wp29/wp29regs121-140.html. [8] M. B.-A. J. P. C. C. L. Azevedo, J. L. Cardoso and M. Marques, “Automatic vehicle trajectory extraction by aerial remote sensing.” Proc. Soc. Behavioral Sci, vol. 111, pp. 849–858, 2013. [9] A. C. Shastry and R. A. Schowengerdt, “Airborne video registration and traffic-flow parameter estimation,” IEEE Trans. Intell. Transp. Syst, vol. 6, no. 4, pp. 391–405,2005. [10] R. G. J. Redmon, S. Divvala and A. Farhadi, “You only look once: Unified, real-time object detection,” in Computer Vision and Pattern Recognition, 2015. [11] J. Redmon and A. Farhadi., “Yolo9000: Better, faster, stronger.” in Computer Vision and Pattern Recognition, 2016. [12] A. Guttman, “R-Tree: A Dynamic Index Structure for Spatial Searching,” Proc. ACM SIGMODE, pp. 47–57, 1984. [13] I. Kamel and C. Faloutsos, “On packing r-trees.” In Proc. 2nd International Conference on Information and Knowledge Management(CIKM-93), pp. 490–499, 1993. [14] N. Roussopoulos and D. Leifker, “Direct spatial search on pictorial databases using packed r-trees.” Proc. ACM SIGMOD, pp. 17–31, 1985. [15] I. Kamel and C. Faloutsos, “Hilbert Rtree: An improved R-tree using fractals.” In Proceedings of the Twentieth International Conference on Very Large Data Bases, pp. 500–509, 1994. [16] H. tae Kiml and B. Songl, “Vehicle Recognition Based on Radar and Vision Sensor Fusion for Automatic Emergency Braking.” 2013 13th International Conference on Control, Automation and Systems (ICCAS 2013), pp. 1342–1346, 2013. [17] R. D. M. B. M. Fazeen, B. Gozick and M. C. Gonzalez, “Safe Driving Using Mobile Phones,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 3, pp. 1462–1468, 2012. [18] U. N. P. P. C. Premebida, G. Monteiro, “A Lidar and Vision-based Approach for Pedestrian and Vehicle Detection and Tracking.” Intelligent Transportation Systems Conference 2007, pp. 1044–1049, 2007. [19] H. tae Kiml and B. Songl, “Vehicle Recognition Based on Radar and Vision Sensor Fusion for Automatic Emergency Braking.” 13th International Conference on Control, Automation and Systems (ICCAS 2013), p. 1342–1346, 2013. [20] “Nvidia jetson. the embedded platform for autonomous everything.” http://www.nvidia.com/object/embedded-systems-dev-kits-modules.html. [21] “Surrounded by ai devices that do everything from flying to farming, nvidia launches jetson tx2.” https://blogs.nvidia.com/blog/2017/03/07/surrounded-by-aidevices-that-do-everything-from-flying-to-farming-nvidia-launches-jetson-tx2/. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/handle/123456789/1277 | - |
| dc.description.abstract | Nowadays, the bulk of these road collisions is caused by human unawareness or distraction. Since the most important thing is your safety and the safety of others, ADAS is developed to support enhanced vehicle system for safety and better driving. AEBS as an important part of the ADAS has become a hot research topic. Computer vision, together with Radar and Lidar, is at the forefront of technologies that enable the evolution of AEBS. Since the cost of long range radar and lidar is very high, we want to use camera-based system to construct AEBS. Instead of using a single monocular camera, we propose a heterogeneous camera-based system to use sensor fusion to combine the strengths of all the difference FoV cameras. Also,We use a heuristic false positive removal method to decrease the false positive rate that caused by the sensor fusion method. We optimize the sensor fusion method Because of the the limitation of computing resource on embedded system. As a result, the recall of YOLO can be increased up to 10% through our heterogeneous camera-based system. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-12T09:35:24Z (GMT). No. of bitstreams: 1 ntu-107-R04922133-1.pdf: 35022533 bytes, checksum: 05d17ae8a2edce861301e4897cf9dd92 (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 口試委員審定書 i
致謝 ii 摘要 iii Abstract iv 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Background and Related Work 6 2.1 AutonomousEmergencyBrakingSystem ................. 6 2.2 Vision-BasedVehicleDetection-YOLO.................. 7 2.3 R-tree.................................... 9 2.4 RelatedWork ................................ 12 3 System Architecture and Problem Definition 14 3.1 SystemArchitecture............................. 14 3.2 ProblemDefinition ............................. 16 4 Design and Implementation 18 4.1 TheImpactofInputImageSizes...................... 20 4.2 CoordinateSystemTransformation..................... 21 4.3 ExistedSensorFusionMethod ....................... 21 4.4 ProposedSensorFusionMethod ...................... 23 4.5 FalsePositiveRemoval........................... 26 4.6 SearchSpaceReducation.......................... 28 5 Performance Evaluation 34 5.1 EvaluationofSensorFusionMethod.................... 35 5.2 EvaluationofFalsePositiveRemoval ................... 37 5.3 PerformanceMeasurementonNVIDIATX2. . . . . . . . . . . . . . . . 38 6 Conclusion 39 Bibliography 40 | |
| dc.language.iso | en | |
| dc.title | 針對安全攸關之嵌入式即時系統的異質性資訊融合 | zh_TW |
| dc.title | Heterogeneous Sensing Fusion for Safety Critical Embedded Real-time Systems | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 逄愛君,叢培貴 | |
| dc.subject.keyword | 異質性資訊融合,異質性影像感測器系統,緊急煞車輔助系統,物體偵測, | zh_TW |
| dc.subject.keyword | Heterogeneous Sensing Fusion,Heterogeneous Camera-Based Sytem,Tri-focal camera,AEBS,Object Detection, | en |
| dc.relation.page | 41 | |
| dc.identifier.doi | 10.6342/NTU201800542 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2018-02-12 | |
| 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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| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-107-1.pdf | 34.2 MB | Adobe PDF | 檢視/開啟 |
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