1/10th scale autonomous vehicle based on convolutional neural network
References
- Balaji, B., Mallya, S., Genc, S., Gupta, S., Dirac, L., Khare, V., Roy, G., Sun, T., Tao, Y., Townsend, B., et al. 2019. DeepRacer: educational autonomous racing platform for experimentation with Sim2Real reinforcement learning. arXiv preprint arXiv:1911.01562.
- Bechtel, M. G., Mcellhiney, E., Kim, M. and Yun, H. 2018. DeepPicar: a low-cost deep neural network-based autonomous car. 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), Hakodate, pp. 11–21, doi: 10.1109/RTCSA.2018.00011.
- Behringer, R. et al. 2004. The DARPA grand challenge – development of an autonomous vehicle. IEEE Intelligent Vehicles Symposium, 2004, Parma, pp. 226-231, doi: 10.1109/IVS.2004.1336386.
- Blaga, B., Deac, M., Al-doori, R. W. Y., Negru, M. and Daˇnescu, R. 2018. Miniature autonomous vehicle development on Raspberry Pi. 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, pp. 229–236, doi: 10.1109/ICCP.2018.8516589.
- Bojarski, M., Testa, D. D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., Zhang, X., Zhao, J. and Zieba, K. 2016. End to end learning for self-driving cars. arXiv:1604.07316, April, available at: http://arxiv.org/abs/1604.07316.
- Goldfain, B., Drews, P., You, C., Barulic, M., Velev, O., Tsiotras, P. and Rehg, J. M. 2019. Autorally: an open platform for aggressive autonomous driving. IEEE Control Systems Magazine 39(1): 26–55.
- Gui, P., Tang, L. and Mukhopadhyay, S. 2017. Tree pruning robot tilting control using fuzzy logic. Proceedings of the 2017 Eleventh International Conference on Sensing Technology (ICST), 978-1-5090-6526-4/17/$31.00 © 2017, pp. 153–157.
- How, J. P., Behihke, B., Frank, A., Dale, D. and Vian, J. 2008. Real-time indoor autonomous vehicle test environment. IEEE Control Systems Magazine 28(2): 51–64, doi: 10.1109/MCS.2007.914691.
- Karaman, S. et al. 2017. Project-based, collaborative, algorithmic robotics for high school students: Programming self-driving race cars at MIT. 2017 IEEE Integrated STEM Education Conference (ISEC), Princeton, NJ, pp. 195–203, doi: 10.1109/ISECon.2017.7910242.
- Leni, A. E. S. 2017. Instance vehicle monitoring and tracking with internet of things using arduino. International Journal on Smart Sensing and Intelligent Systems 10: 123–136.
- Man, C. K. Y. L. L., Koonjul, Y. and Nagowah, L. 2018. A low cost autonomous unmanned ground vehicle. Future Computing and Informatics Journal 3(2): 304–320.
- Miao, X., Li, S. and Shen, H. 2012. On-board lane detection system for intelligent vehicle based on monocular vision. International Journal of Smart Sensing and Intelligent System 5(4): 957–972.
- Nag, A., Menzies, B. and Mukhopadhyay, S. C. 2018. Performance analysis of flexible printed sensors for robotic arm applications. Sensors and Actuators A: Physical 276: 226–236.
- O’Kelly, M., Sukhil, V., Abbas, H., Harkins, J., Kao, C., Pant, Y., Mangharam, R., Agarwal, D., Behl, M., Burgio, P. and Bertogna, M. 2019. F1/10: an open-source autonomous cyber-physical platform. ArXiv, abs/1901.08567.
- Olgun, M. C., Baytar, Z., Akpolat, K. M. and Sahingoz, O. K. 2018. Autonomous vehicle control for lane and vehicle tracking by using deep learning via vision. 2018 6th International Conference on Control Engineering & Information Technology (CEIT), Istanbul, pp. 1–7, doi: 10.1109/CEIT.2018.8751764.
- Pannu, G., Ansari, M. and Gupta, P. 2015. design and implementation of autonomous car using Raspberry Pi’. International Journal of Computer Applications 113(9): 22–29.
- Patel, N. et al. 2019. A deep learning gated architecture for UGV navigation robust to sensor failures. Robotics and Autonomous Systems 116: 80–97.
- Paull, L., Tani, J., Ahn, H., Alonso-Mora, J., Carlone, L., Cap, M., Chen, Y. F., Choi, C., Dusek, J., Fang, Y. et al. 2017. Duckietown: an open, inexpensive and flexible platform for autonomy education and research. 2017 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 1497–1504.
- Pomerleau, D. A. 1989. Alvinn, an autonomous land vehicle in a neural network. In Advances in Neural Information Processing Systems (NIPS). Technical report, Carnegie Mellon University, Computer Science Department, Pittsburgh, PA, pp. 305–313.
- Roscoe, W. 2020. Donkey Car: an opensource DIY self driving platform for small scale cars, available at: http://www.donkeycar.com (accessed June 17, 2020).
- Schwartz, J. D. and Milam, M. 2008. On-line path planning for an autonomous vehicle in an obstacle filled environment. 2008 47th IEEE Conference on Decision and Control, IEEE, pp. 2806–2813.
- Seelye, M., Gupta, G. S., Seelye, J. and Mukhopadhyay, S. C. 2010. Camera-in-hand robotic system for remote monitoring of plant growth in a laboratory. Proceedings of 2010 IEEE I2MTC Conference, Austin, TX, May 4-6, pp. 809–814.
- Sen Gupta, G., Mukhopadhyay, S. C. and Finnie, M. 2009. Wi-Fi based control of a robotic arm with remote vision. Proceedings of 2009 IEEE I2MTC Conference, Singapore, May 7, pp. 557–562.
- Sen Gupta, G., Mukhopadhyay, S. C., Demidenko, S. and Messom, C. H. 2006. Master-slave control of a teleoperated anthropomorphic robotic arm with gripping force sensing. IEEE Transactions on Instrumentation and Measurement 55(6): 2136–2145.
- Srinivasa, S. S., Lancaster, P., Michalove, J., Schmittle, M., Rockett, C. S. M., Smith, J. R., Choudhury, S., Mavrogiannis, C. and Sadeghi, F. 2019. Mushr: A low-cost, open-source robotic racecar for education and research. arXiv preprint arXiv:1908.08031.
- Tian, Y. et al. 2018. Deeptest: automated testing of deep-neural-network-driven autonomous cars. Proceedings of the 40th International Conference on Software Engineering, Gothenburg, May, pp. 303–314.
- Toupet, O. et al. 2019. Terrain-adaptive wheel speed control on the Curiosity Mars rover: algorithm and flight results. Journal of Field Robotics 37(5): 699–728.
- Wu, B. et al. 2017. SqueezeDet: unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, July, pp. 129–137.
- Zhang, Q. and Du, T. 2019. Self-driving scale car trained by deep reinforcement learning. arXiv preprint arXiv:1909.03467.
- Zhang, W. and Mahale, T. 2018. End to end video segmentation for driving: lane detection for autonomous car. arXiv 2018, arXiv:1812.05914.
DOI: https://doi.org/10.21307/ijssis-2020-021 | Journal eISSN: 1178-5608
Language: English
Page range: 1 - 17
Submitted on: Jul 26, 2020
Published on: Aug 25, 2020
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
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© 2020 Avishkar Seth, Alice James, Subhas C. Mukhopadhyay, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.