A detailed tutorial on how to build a traffic light classifier with TensorFlow for the capstone project of Udacity's Self-Driving Car Engineer Nanodegree Program.

🖍️ This project achieves some functions of image identification for Self-Driving Cars. First, use yolov5 for object detection. Second, image classification for traffic light and traffic sign. Furthermore, the GUI of this project makes it more user-friendly for users to realize the image identification for Self-Driving Cars.

Machine Learning Based Real-Time Traffic Light Alert on Your Car with Raspberrypi

HSV色彩空间下的交通灯识别

System Integration (project 9 of 9 from Udacity Self-Driving Car Engineer Nanodegree)

Traffic Light Protocol - meeting classification

Artificial Intelligence Based spectacles for blind people that enables them to know what is happening in their surrounding

System Integration

Integration of Multiple Algorithms using ROS to run on Carla, Udacity's Self-Driving Car

Capstone Project : In this project, we implement a Real Self Driving Car in python to maneuver the vehicle around the track while following the traffic rules.

ROS-based code to control a real Self-Driving Car. Final project in Udacity's Self-Driving Car Engineer Nanodegree.

Capstone project of Udacity's Self Driving Car Engineer Nanodegree

[Udacity] Projects for Introduction to Self-Driving Cars Nanodegree by Udacity

Traffic lights tracking and color detection with OpenCV. Combination of a MOSSE tracker and inner freehand rectangles.

Udacity ISDC Traffic Light Classifier Project v01