Coding

Python

Python is an interpreted high-level general-purpose programming language. It is versatile, easy to use and fast to develop, which makes it ideal for rapid prototyping. Some of it’s downsides is that it has speed limitation, it does not perform well with multithreading, making it inferior in performances to other, low-level programming languages. The links below represent a good starting point for this type of applications. - https://www.raspberrypi.org/documentation/usage/python - https://www.digikey.com/en/maker/blogs/2018/how-to-run-python-programs-on-a-raspberry-pi

CPP

It is one of the oldest, most used and most efficient programming languages. It has a wide support, it is powerful, fast and has a small amount of standard libraries. It’s major downside being it’s complexity. - https://www.aranacorp.com/en/program-your-raspberry-pi-with-c/

Bash

To be done!

Image processing

In this part, you can find some useful link for image processing on Raspberry pi.

Basic Python libraries:

• Opencv Official Documentation

• Opencv with python

• Lane detection link 1

• Lane detection link 2

• Traffic sign recognition link 1

• Traffic sign recognition link 2

• Automatic exposure control

• Automatic gain control

Articles for Road Sign Recognition:

• A. Mogelmose, M. M. Trivedi and T. B. Moeslund, “Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey,” in IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 4, pp. 1484-1497, Dec. 2012. [link2]

• S. Maldonado-Bascon, S. Lafuente-Arroyo, P. Gil-Jimenez, H. Gomez-Moreno and F. Lopez-Ferreras, “Road-Sign Detection and Recognition Based on Support Vector Machines,” in IEEE Transactions on Intelligent Transportation Systems, vol. 8, no. 2, pp. 264-278, June 2007. [link3]

• Y. Han and E. Oruklu, “Traffic sign recognition based on the NVIDIA Jetson TX1 embedded system using convolutional neural networks,” 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Boston, MA, 2017, pp. 184-187. [link4]

Articles for Lane detection and tracking:

• R. Danescu, S. Nedevschi, M. M. Meinecke and T. B. To, “Lane Geometry Estimation in Urban Environments Using a Stereovision System,” 2007 IEEE Intelligent Transportation Systems Conference, Seattle, WA, 2007, pp. 271-276. [link5]

• R. Labayrade, J. Douret and D. Aubert, “A multi-model lane detector that handles road singularities,” 2006 IEEE Intelligent Transportation Systems Conference, Toronto, Ont., 2006, pp. 1143-1148. [link6]

• Yue Dong, Jintao Xiong, Liangchao Li and Jianyu Yang, “Robust lane detection and tracking for lane departure warning,” 2012 International Conference on Computational Problem-Solving (ICCP), Leshan, 2012, pp. 461-464. [link7]