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

一种基于 YOLOv8 的路口交通信号灯通行规则识别模型及算法

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

Traffic Light Protocol - meeting classification

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

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

System Integration

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

Udacity ISDC Traffic Light Classifier Project v01