There are 14 repositories under traffic-light-detection topic.
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.
Detect traffic lights and classify the state of them, then give the commands "go" or "stop".
Traffic light detection using deep learning with the YOLOv3 framework. PyTorch => YOLOv3
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
Traffic Light Detection using the tensorflow object detection API
Autonomous Self-Driving Car Prototype - with automatic steering control, traffic sign recognition, traffic light detection and other object detection features.
A simple yet effective repo for object detection based on the FCOS architecture.
Traffic light detection
traffic-lights-detection-and-color-recognition-using-yolov8
一种基于 YOLOv8 的路口交通信号灯通行规则识别模型及算法
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
Detect traffic lights and classify the state of them, then give the commands "go" or "stop".
Program a real Self-Driving Car by writing ROS nodes to implement core functionality of the autonomous vehicle system.
Detect traffic lights and their locations from images using computer vision
OpenLendaのPythonでのONNX推論サンプル
Here a transfer learning solution for traffic light detection is presented. It uses Mask Region-Based Convolutional Neural Network as it base.
System Integration (project 9 of 9 from Udacity Self-Driving Car Engineer Nanodegree)
Walking Assistance System for the Visually Imparied in real-time
This is the final project in Udacity's Self-Driving Car Engineer Nanodegree where we will implement ROS nodes to control Carla - Udacity's self-driving car.
System Integration Project using ROS to move a car and detect and obey traffic lights
SURVIVE is a system that deters and helps punish red light violations. This is the software part of our prototype (hardware-sensors) as long as some algorithms to detect cars using only CV (Computer Vision)
A self-driving car prototype built using a Raspberry Pi and remote-control car with end-to-end steering prediction, traffic light detection, and obstacle avoidance.
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.
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.
System Integration
Our implementation of System Integration project for Udacity's Self-Driving Car Nano Degree Program!
Final Project of the Udacity Self-Driving Car Engineer Nanodegree with the goal to develop a an architecture and its underlying components to steer a vehicle autonomously through a full physics simulation environment.
Final project of the Udacity Self-Driving Car Nanodegree
Lane detection and Traffic light detection, for Ohio University PAVE student organization