Join "Traffic Light & Sign Detection for Driverless Cars", which continues the driverless cars topic introduced in courses "Build a Lane Following Driverless Car" and "Human Tracking with Driverless Cars" on Robociti, to study how to harness the power of Computer Vision, Machine Learning and Python programming to detect road signs and traffic lights on the video streams and help driverless cars see their environment better and navigate modern urban environments. Students will firstly learn how to extract features from images and videos and use them to perform feature matching between different images and videos. Then, the detection of traffic signs and lights will be analyzed along with all relevant challenges and we will see how to apply Machine learning for that challenge. Afterwards, we will learn how to use that knowledge for a practical application of feature matching with Python and OpenCV and how to use TensorFlow Lite and YOLO for autonomously detecting the desired target objects in video streams. Finally, students are presented with the challenge of using the results of our Neural Network framework to make a wheeled robot move based on the road signs and traffic lights that are detected.