References

Here are the references for all sources we used in the project (APA Format)

Bechtel, M. G., Mcellhiney, E., Kim, M., & 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). https://doi.org/10.1109/rtcsa.2018.00011

Bojarski, M. (2016, Aug 17). Deep Learning for Self-Driving Cars. NVIDIA Developer Blog. Retrieved July 5, 2023, from https://developer.nvidia.com/blog/deep-learning-self-driving-cars/

Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., Zhang, X., Zhao, J., & Zieba, K. (2016). End-to-End Learning for Self-Driving Cars. Retrieved from https://arxiv.org/abs/1604.07316

Brown, S. (2021, Apr 21). Machine Learning Explained. MIT Sloan School of Management. Retrieved July 5, 2023, from https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained

Chameleon Cloud. (2022). Getting Started. Chameleon Cloud GitBook. Retrieved July 5, 2023, from https://chameleoncloud.gitbook.io/chi-edge/getting-started

CoderDojo Twin Cities. (n.d.). Resources. Retrieved July 5, 2023, from https://www.coderdojotc.org/ai-racing-league/resources/

Donkey Car. (n.d.). Roll Your Own. Donkey Car Documentation. Retrieved July 5, 2023, from https://docs.donkeycar.com/cars/roll_your_own/

Raffin, A. (2019, Jan 7). Learning to Drive Smoothly in Minutes. Towards Data Science. Retrieved July 5, 2023, from https://towardsdatascience.com/learning-to-drive-smoothly-in-minutes-450a7cdb35f4

Raffin, A. (2023). DLR-RM/RL-baselines3-zoo. GitHub. https://github.com/DLR-RM/rl-baselines3-zoo

Rashidinejad, P., Zhu, B., Ma, C., Jiao, J., & Russell, S. (2022). Bridging offline reinforcement learning and imitation learning: A tale of pessimism. IEEE Transactions on Information Theory, 68(12), 8156-8196. https://doi.org/10.1109/tit.2022.3185139

Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0197-0

Silva, P., Costan, A., & Antoniu, G. (2019). Investigating edge vs. cloud computing trade-offs for stream processing. 2019 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/bigdata47090.2019.9006139

Surianarayanan, C., Lawrence, J. J., Chelliah, P. R., Prakash, E., & Hewage, C. (2023). A survey on optimization techniques for edge artificial intelligence (AI). Sensors, 23(3), 1279. https://doi.org/10.3390/s23031279

Tampuu, A., Matiisen, T., Semikin, M., Fishman, D., & Muhammad, N. (2022). A survey of end-to-end driving: Architectures and training methods. IEEE Transactions on Neural Networks and Learning Systems, 33(4), 1364-1384. https://doi.org/10.1109/tnnls.2020.3043505

Tian, D. (2019, Apr 19). DeepPiCar Part 1. Towards Data Science. Retrieved July 5, 2023, from https://towardsdatascience.com/deeppicar-part-1-102e03c83f2c

Tsen, J. (2022, April 18). One Fish, Two Fish: Choosing Optimal Edge Topologies for Real-Time Autonomous Fish Surveys. Chameleon Cloud Blog. Retrieved July 5, 2023, from https://www.chameleoncloud.org/blog/2022/04/18/one-fish-two-fish-choosing-optimal-edge-topologies-for-real-time-autonomous-fish-surveys/

Wu, J. (2018, Oct 9). Donkey Car Project Part 2: Data Analysis. Medium. Retrieved July 5, 2023, from https://medium.com/@jasonwu_49390/donkey-car-project-part-2-data-analysis-e9c5ef947c2f