Open Access
| Issue |
SHS Web Conf.
Volume 77, 2020
The 2nd ACM Chapter International Conference on Educational Technology, Language and Technical Communication (ETLTC2020)
|
|
|---|---|---|
| Article Number | 04002 | |
| Number of page(s) | 6 | |
| Section | Topics in Computer Science | |
| DOI | https://doi.org/10.1051/shsconf/20207704002 | |
| Published online | 08 May 2020 | |
- Yuchi Tian et al. “Deeptest: Automated testing of deep-neural-network-driven autonomous cars”. In: Proceedings of the 40th international conference on software engineering. 2018, pp. 303–314. [CrossRef] [Google Scholar]
- Hazem Rashed et al. “Fusemodnet: Real-time camera and lidar based moving object detection for robust low-light autonomous driving”. In: Proceedings of the IEEE International Conference on Computer Vision Workshops. 2019, pp. 0–0. [Google Scholar]
- Justin Zheng, Kazuhide Okamoto, and Panagiotis Tsiotras. “Vision-Based Autonomous Vehicle Control using the Two-Point Visual Driver Control Model”. In: arXiv preprint arXiv:1910.04862 (2019). [Google Scholar]
- Xinlei Pan et al. “Virtual to real reinforcement learning for autonomous driving”. In: arXiv preprint arXiv:1704.03952 (2017). [Google Scholar]
- Qing Rao and Jelena Frtunikj. “Deep learning for self-driving cars: chances and challenges”. In: Proceedings of the 1st International Workshop on Software Engineering for AI in Autonomous Systems. 2018, pp. 35–38. [Google Scholar]
- Qi Zhang and Tao Du. “Self-driving scale car trained by Deep reinforcement Learning”. In: arXiv preprint arXiv:1909.03467 (2019). [Google Scholar]
- Juntae Kim et al. “Deep learning algorithm using virtual environment data for self-driving car”. In: 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE. 2019, pp. 444–448. [Google Scholar]
- Donkey Car Documentation. url: docs.donkeycar.com. [Google Scholar]
- Henryk Blasinski et al. “Optimizing image acquisition systems for autonomous driving”. In: Electronic Imaging 2018.5 (2018), pp. 161–1. [Google Scholar]
- Ali Ghofrani, Rahil Mahdian Toroghi, and Seyed Mojtaba Tabatabaie. “ICPS-net: an end-to-end RGB-based indoor camera positioning system using deep convolutional neural networks”. In: Twelfth International Conference on Machine Vision (ICMV 2019). Vol. 11433. International Society for Optics and Photonics. 2020, p. 1143323. [Google Scholar]
- Rachel Blin et al. “Road scenes analysis in adverse weather conditions by polarization-encoded images and adapted deep learning”. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE. 2019, pp. 27–32. [CrossRef] [Google Scholar]
- Raj Shirolkar et al. “Self-Driving Autonomous Car using Raspberry Pi”. In: International Journal of Engineering Research Technology (IJERT) Volume 08, Issue 05 (2019). [Google Scholar]
- Anju Sudhakaran and Manju Manuel. “High-Quality Image Scaling Using V-Model”. In: 2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET). IEEE. 2018, pp. 1–4. [Google Scholar]
- Yunfeng Zhang et al. “Single-image super-resolution based on rational fractal interpolation”. In: IEEE Transactions on Image Processing 27.8 (2018), pp. 3782–3797. [CrossRef] [Google Scholar]
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