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
python train.py to train the model
python run.py to run the model on a live webcam feed
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.
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.
O’Kelly, 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. https://arxiv.org/abs/1704.03952
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, 1–12.
Message me if I've missed anything!