- IUT Repository Home
- →
- Electrical and Electronics Engineering (EEE)
- →
- Thesis
- →
- Undergraduate
- →
- 2021
- →
- View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.
| dc.contributor.author | Khan, Ahnaf Asif | |
| dc.contributor.author | Hossain, Sharara | |
| dc.contributor.author | Islam, Abrar | |
| dc.date.accessioned | 2022-04-30T10:29:34Z | |
| dc.date.available | 2022-04-30T10:29:34Z | |
| dc.date.issued | 2021-03-30 | |
| dc.identifier.citation | [1] Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812115 [2] Chenyi Chen, Ari Seff, Alain Kornhauser, Jianxiong Xiao. “DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving”, Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 2722-2730 https://openaccess.thecvf.com/content_iccv_2015/html/Chen_DeepDriving_Learning_Af fordance_ICCV_2015_paper.html [3] Manikandasriram Srinivasan Ramanagopal, Cyrus Anderson, Ram Vasudevan, Matthew Johnson-Roberson. “Failing to Learn: Autonomously Identifying Perception Failures for Self-driving Cars”. arXiv: https://arxiv.org/abs/1707.00051v4 [cs.CV] [4] Christian Häne, Lionel Heng, Gim Hee Lee, Friedrich Fraundorfer, Paul Furgale, Torsten Sattler, Marc Pollefeys. “3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection”; https://arxiv.org/abs/1708.09839 [5] Jiachen Sun, Yulong Cao, Qi Alfred Chen, Z. Morley Mao; “Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures”, https://arxiv.org/abs/2006.16974 [6] Jonah Philion, Amlan Kar, Sanja Fidler; “Learning to Evaluate Perception Models using Planner-Centric MetricsLearning to Evaluate Perception Models using Planner-Centric Metrics”. https://arxiv.org/abs/2004.08745 [7] Der-Hau Lee, Kuan-Lin Chen, Kuan-Han Liou, Chang-Lun Liu, Jinn-Liang Liu; “Deep Learning and Control Algorithms of Direct Perception for Autonomous Driving”; https://arxiv.org/abs/1910.12031 [8] Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, Vladlen Koltun’ “CARLA: An Open Urban Driving Simulator”; https://arxiv.org/abs/1711.03938 [9] Felipe Codevilla, Matthias Müller, Antonio López, Vladlen Koltun, Alexey Dosovitskiy; “End-to-end Driving via Conditional Imitation Learning”; https://arxiv.org/abs/1710.02410 [10] Yudai Nagano, Yohei Kikuta; “SRGAN for Super-Resolving Low-Resolution Food Images.” https://dl.acm.org/doi/10.1145/3230519.3230587 [11] Coursera MOOC: “Introduction to Self-Driving Cars” by University of Toronto https://www.coursera.org/learn/intro-self-driving-cars [12] Larson, William; Zhao, Weihua (2020). "Self-driving cars and the city: Effects on sprawl, energy consumption, and housing affordability". Regional Science and Urban Economics. 81: 103484. doi:10.1016/j.regsciurbeco.2019.103484. ISSN 0166-0462. [13] How C-V2X in 5G will transform cars and save lives (Analyst Angle) https://www.rcrwireless.com/20200206/analyst-angle/c-v2x-5g-transform-cars-analyst-a ngle#prettyPhoto [14] Glenn Jocher, Alex Stoken, Jirka Borovec, NanoCode012, ChristopherSTAN, Liu Changyu, … Lijun Yu 于力军. (2021, January 5). ultralytics/yolov5: v4.0 - nn.SiLU() activations, Weights & Biases logging, PyTorch Hub integration (Version v4.0). Zenodo. http://doi.org/10.5281/zenodo.4418161 https://github.com/ultralytics/yolov5 [15] Shihavuddin, ASM; Mohammad Rifat Ahmmad Rashid, 2020, "DhakaAI", https://doi.org/10.7910/DVN/POREXF, Harvard Dataverse, V1 [16] M. Daily, S. Medasani, R. Behringer and M. Trivedi, "Self-Driving Cars," in Computer, vol. 50, no. 12, pp. 18-23, December 2017, doi: 10.1109/MC.2017.4451204. [17] Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Jake Zhao, Karol Zieba; “End to End Learning for Self-Driving Cars”; arXiv:1604.07316 [cs.CV] [18] C. Urmson and W. ". Whittaker, "Self-Driving Cars and the Urban Challenge," in IEEE Intelligent Systems, vol. 23, no. 2, pp. 66-68, March-April 2008, doi: 10.1109/MIS.2008.34. [19] Gim Hee Lee, Friedrich Faundorfer, Marc Pollefeys; Motion Estimation for Self-Driving Cars with a Generalized Camera; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 2746-2753. [20] Weihuang Xu, Nasim Souly, Pratik Prabhanjan Brahma; Reliability of GAN Generated Data to Train and Validate Perception Systems for Autonomous Vehicles ; https://openaccess.thecvf.com/content/WACV2021W/AVV/papers/Xu_Reliability_of_GA N_Generated_Data_to_Train_and_Validate_Perception_WACVW_2021_paper.pdf [21] D. N. -N. Tran, H. -H. Nguyen, L. H. Pham and J. W. Jeon, "Object Detection with Deep Learning on Drive PX2," 2020 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia), Seoul, Korea (South), 2020, pp. 1-4, doi: 10.1109/ICCE-Asia49877.2020.9276954. [22] The 3 basic elements to building self-driving cars https://www.cnbc.com/2017/01/05/the-3-basic-elements-to-building-self-driving-cars.htm l [23] J. Kocić, N. Jovičić and V. Drndarević, "Sensors and Sensor Fusion in Autonomous Vehicles," 2018 26th Telecommunications Forum (TELFOR), Belgrade, Serbia, 2018, pp. 420-425, doi: 10.1109/TELFOR.2018.8612054. [24] R. Domínguez, E. Onieva, J. Alonso, J. Villagra and C. González, "LIDAR based perception solution for autonomous vehicles," 2011 11th International Conference on Intelligent Systems Design and Applications, Cordoba, Spain, 2011, pp. 790-795, doi: 10.1109/ISDA.2011.6121753. [25] Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi; “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network”; https://arxiv.org/pdf/1609.04802.pdf [26] Yahui Liu, Xiaoqian Fan, Chen Lv, Jian Wu, Liang Li, Dawei Ding, An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles, Mechanical Systems and Signal Processing, Volume 100, 2018, Pages 605-616, ISSN 0888-3270, https://doi.org/10.1016/j.ymssp.2017.07.051. [27] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio; “Generative Adversarial Nets”; https://arxiv.org/pdf/1406.2661v1.pdf [28] A. Kuramoto, M. A. Aldibaja, R. Yanase, J. Kameyama, K. Yoneda and N. Suganuma, "Mono-Camera based 3D Object Tracking Strategy for Autonomous Vehicles," 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China, 2018, pp. 459-464, doi: 10.1109/IVS.2018.8500482. [29] The COCO (Common Objects in Context) dataset; https://cocodataset.org/#home [30] Smith, Graeme E., and Christopher J. Baker. "Echoic flow for radar and sonar." Electronics letters 48.18 (2012): 1160-1161. [31] Pendleton, Scott D.; Andersen, Hans; Du, Xinxin; Shen, Xiaotong; Meghjani, Malika; Eng, You H.; Rus, Daniela; Ang, Marcelo H. 2017. "Perception, Planning, Control, and Coordination for Autonomous Vehicles" Machines 5, no. 1: 6. https://doi.org/10.3390/machines5010006 [32] Schwarting, Wilko, Javier Alonso-Mora, and Daniela Rus. "Planning and decision-making for autonomous vehicles." Annual Review of Control, Robotics, and Autonomous Systems (2018). [33] The 5 Autonomous Driving Levels Explained https://www.iotforall.com/5-autonomous-driving-levels-explained [34] FIVE LEVELS TO AUTONOMY https://www.blickfeld.com/blog/5-automation-levels/ [35] Automated Vehicles for Safety https://www.nhtsa.gov/technology-innovation/automated-vehicles-safety [36] Farag, Wael. (2019). “Track Maneuvering using PID Control for Self-Driving Cars”. Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering). 12. 10.2174/2352096512666190118161122. ttps://www.researchgate.net/publication/331475267_Track_Maneuvering_using_PID_C ontrol_for_Self-Driving_Cars [37] How do self driving cars drive? Part 1: Lane keeping assist https://medium.com/@edersantana/how-do-self-driving-cars-drive-part-1-lane-keeping-a ssist-581f6ff50349 [38] Semantic Segmentation to Solve The Lyft Perception Challenge https://medium.com/@kylesf/udacitylyftchallenge-ebffe71a6b22 [39] Single Image Super Resolution Using GANs — Keras https://medium.com/@birla.deepak26/single-image-super-resolution-using-gans-keras-a ca310f33112#id_token=eyJhbGciOiJSUzI1NiIsImtpZCI6ImU4NzMyZGIwNjI4NzUxNTU 1NjIxM2I4MGFjYmNmZDA4Y2ZiMzAyYTkiLCJ0eXAiOiJKV1QifQ.eyJpc3MiOiJodHRwc zovL2FjY291bnRzLmdvb2dsZS5jb20iLCJuYmYiOjE2MTUxODE2MzQsImF1ZCI6IjIxNjI 5NjAzNTgzNC1rMWs2cWUwNjBzMnRwMmEyamFtNGxqZGNtczAwc3R0Zy5hcHBzLm dvb2dsZXVzZXJjb250ZW50LmNvbSIsInN1YiI6IjExMjk3MTI5Nzk4ODk4MDU3MzE2NC IsImVtYWlsIjoiYWhuYWZhc2lmN0BnbWFpbC5jb20iLCJlbWFpbF92ZXJpZmllZCI6dHJ1 ZSwiYXpwIjoiMjE2Mjk2MDM1ODM0LWsxazZxZTA2MHMydHAyYTJqYW00bGpkY21z MDBzdHRnLmFwcHMuZ29vZ2xldXNlcmNvbnRlbnQuY29tIiwibmFtZSI6IkFobmFmIEFz aWYiLCJwaWN0dXJlIjoiaHR0cHM6Ly9saDMuZ29vZ2xldXNlcmNvbnRlbnQuY29tL2EtL 0FPaDE0R2pmX2Nnck8xZUhoSFhWTFE2NGZmX1I1ZWZlVWxZTjFUWnBqUS1CN2c 9czk2LWMiLCJnaXZlbl9uYW1lIjoiQWhuYWYiLCJmYW1pbHlfbmFtZSI6IkFzaWYiLCJp YXQiOjE2MTUxODE5MzQsImV4cCI6MTYxNTE4NTUzNCwianRpIjoiMDI1Yzk4MzM1N GVjZWQ4Yjk5ZWRhMmFhNDJiZmFlM2I5MmFmMDk1OCJ9.J1Kx62kTLrxVcVeg9V9S cOVqXXFSDCRWkyGD44qEPBfB2F3cZK1meipfww9e_sp8OFYDkXy7BbRmTdJfjvW7 DONg3VQj6y17fLUWvK7zm0shwPevLjJo4Xy69VATjcVC5RaJrrztCnHlxWqceg_ZR2A mUoCCCVqR-Mpo40l4xajCsCnAijs6gf2XUnicfF6hbr4n4-y43eaT6M2yavGrsyMpSB7iq6 9p0VNBku83Da3q7ddaT3qsU4pOUj_hxpwp8-dmyE851fGeKdpHhHx5pPoxyXjf5Qiw00 ePlVFkqq4uyE9jDfRUvhq2_vqdk1nzdw9g-K7_G8PxhvNY9Moy_A | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/1466 | |
| dc.description | Supervised by Mr. Mirza Fuad Adnan, Assistant Professor, Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT) Boardbazar, Gazipur-1704. | en_US |
| dc.description.abstract | Autonomous vehicle research is currently one of the trendings fields. This technology has immense potential to revolutionize present socio-economic dynamics. We address the notion of self-driving cars deployment in the context of Bangladesh. This thesis addresses mainly the perception and planning parts of an autonomous vehicle. The detection and tracking of objects around an autonomous vehicle is essential to operate safely. This paper can be divided mainly into two parts. Firstly, it presents the use of CARLA simulator for the purpose of lane detection and navigation of an autonomous vehicle and secondly, it shows the use of YOLOv5 for object detection. Both the longitudinal and lateral controls for lane navigation are done with the help of the built-in map in CARLA. And for object detection part, at first we have used SRGAN algorithm to enhance the quality of the images. Then using those enhanced images, YOLOv5 was used in order to detect objects in those images. For further improvement, these two different parts can be integrated by feeding the outcome of YOLOv5 to the CARLA simulator so that the autonomous vehicle can detect objects and obstacles. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT) The Organization of Islamic Cooperation (OIC) Board Bazar, Gazipur-1704, Bangladesh | en_US |
| dc.title | Lane Navigation through Lateral-Longitudinal Control and Traffic Detection for Autonomous Vehicles | en_US |
| dc.type | Thesis | en_US |
Files in this item
This item appears in the following Collection(s)
-
2021 [63]