Autopilot-TensorFlow
A TensorFlow implementation of this Nvidia paper with some changes. For a summary of the design process and FAQs, see this medium article I wrote.
IMPORTANT
Absolutely, under NO circumstance, should one ever pilot a car using computer vision software trained with this code (or any home made software for that matter). It is extremely dangerous to use your own self-driving software in a car, even if you think you know what you're doing, not to mention it is quite illegal in most places and any accidents will land you in huge lawsuits.
This code is purely for research and statistics, absolutley NOT for application or testing of any sort.
How to Use
Download the dataset and extract into the repository folder
Use python train.py to train the model
Use python run.py to run the model on a live webcam feed
Use python run_dataset.py to run the model on the dataset
To visualize training using Tensorboard use tensorboard --logdir=./logs, then open http://0.0.0.0:6006/ into your web browser.
Check out the cool work people did with this repo!
Abbas, Waseem; Khan, Muhammad Fakhir; Taj, Murtaza; Mahmood, Arif. "Statistically correlated multi-task learning for autonomous driving." (2021). Neural Computing & Applications 33(9) 12921-12938.
Lu Xu, Chen Hu, Kuizhi Mei. "Semi-supervised regression with manifold: A Bayesian deep kernel learning approach." (2022) Neurocomputing (497), 76-85.
Riboni, A; Ghioldi, N.; Candelieri, A; Borrotti, M. "Bayesian optimization and deep learning for steering wheel angle prediction." (2022) Sci. Rep., 12(1)
H. Zhou et al., "DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems," 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE), 2020, pp. 347-358.
Yu Shen, Laura Yu Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming Lin. "Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering." Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021).
M. K. Islam, M. N. Yeasmin, C. Kaushal, M. A. Amin, M. R. Islam and M. I. Hossain Showrov, "Comparative Analysis of Steering Angle Prediction for Automated Object using Deep Neural Network," 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2021, pp. 1-7
D. Qian et al., "End-to-End Learning Driver Policy using Moments Deep Neural Network," 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), Kuala Lumpur, Malaysia, 2018, pp. 1533-1538.
OβKelly, M., Sinha, A., Namkoong, H., Duchi, J., & Tedrake, R. (2018). Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation.
Pan, X., You, Y., Wang, Z., & Lu, C. (2017). Virtual to Real Reinforcement Learning for Autonomous Driving. The British Machine Vision Conference.
Xu, N., Tan, B., & Kong, B. (2018). Autonomous Driving in Reality with Reinforcement Learning and Image Translation.
Jiang J., Wang C., Chattopadhyay S., Zhang W. (2020) Road Context-Aware Intrusion Detection System for Autonomous Cars. In: Zhou J., Luo X., Shen Q., Xu Z. (eds) Information and Communications Security. ICICS 2019. Lecture Notes in Computer Science, vol 11999. Springer, Cham.
Machiraju, H., Balasubramanian, V.N. (2020). A Little Fog for a Large Turn. https://arxiv.org/abs/2001.05873
Olmschenk, G. (2019). Semi-super Semi-supervised Regr vised Regression with Gener ession with Generative Adversarial Networks Using Minimal Labeled Data. The Graduate Center, City University of New York. https://core.ac.uk/download/pdf/228318691.pdf.
Olmschenk, G., Zhu, Z., & Tang, H. (2019). Generalizing semi-supervised generative adversarial networks to regression using feature contrasting. Computer Vision and Image Understanding, 186, 1β12.
https://medium.com/@maxdeutsch/how-to-build-a-self-driving-car-in-one-month-d52df48f5b07
https://mc.ai/self-driving-car-on-indian-roads/
https://www.ctolib.com/amp/cyanamous-Self-Driving-Car-.html
Message me if I've missed anything!