Modular autonomous driving platform running on the CARLA simulator and real-world vehicles.

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.

Traffic light detection using deep learning with the YOLOv3 framework. PyTorch => YOLOv3

Detect traffic lights and classify the state of them, then give the commands "go" or "stop".

Entire Self-Driving Car Software Stack Tested on Real Vehicle

Module for detecting traffic lights in the CARLA autonomous driving simulator. Based on the YOLO v2 deep learning object detection model and implemented in keras, using the tensorflow backend.

traffic light recognition system for ADAS

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

Traffic Light Detection using the tensorflow object detection API

一种基于 YOLOv8 的路口交通信号灯通行规则识别模型及算法

Autonomous Self-Driving Car Prototype - with automatic steering control, traffic sign recognition, traffic light detection and other object detection features.

traffic-lights-detection-and-color-recognition-using-yolov8

A simple yet effective repo for object detection based on the FCOS architecture.

Traffic light detection

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

Self driving car capstone project based on ROS and light-weight traffic light detection CNN model

Program a real Self-Driving Car by writing ROS nodes to implement core functionality of the autonomous vehicle system.

Detect traffic lights and classify the state of them, then give the commands "go" or "stop".

Detect traffic lights and their locations from images using computer vision

The Traffic Light Detection and Classification project aims to enhance autonomous driving systems by accurately detecting and classifying traffic lights. The model is designed to generate appropriate physical responses for vehicles equipped with it.