traffic light recognition system for ADAS
-
Updated
Oct 6, 2021 - Python
traffic light recognition system for ADAS
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.
A deep learning and computer vision based warning indicator system for the vehicle drivers using live dash-cam footage.
A test simulation of all projects and models from time to time.
ImVisible: Pedestrian Traffic Light (PTL) Dataset, Lightweight CNN (LytNet), and Mobile Application for the Visually Impaired (CAIP '19, ICCV Workshops '19)
An attempt of our team to tackle the problem of traffic congestion using deep learning and IoT
Capstone project for Udacity's Self Driving Car Nanodegree
Python3 modul and cli to controll the Cleware traffic light
Our solutions for Google Hashcode 2021 Traffic Signalling
TAG Team implementation of the system integration capstone project in the Udacity Self-Driving Car Nanodegree Program.
Use of Computer Vision to control Traffic Light
Detect and recognise traffic lights using Hough circle transform implemented with OpenCV and Python
🎓Repository for masters labs on FCSN, BSUIR
Q-Learning for Urban Traffic Optimization
A TensorFlow implementation of TrafficLightNeuralNetwork (ANN-based Traffic Light Controller).
Experiments around traffic lights
Traffic Light Reinforcement Learning
Implementation of a multi-agent system for the modeling of carpooling in a city with one-way streets. Used Python and the Mesa package for multi-agent modeling.
A neural network that controls a traffic light simulator
Add a description, image, and links to the traffic-light topic page so that developers can more easily learn about it.
To associate your repository with the traffic-light topic, visit your repo's landing page and select "manage topics."