traffic light recognition system for ADAS
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Updated
Oct 6, 2021 - Python
traffic light recognition system for ADAS
Detection of traffic lights and driving on red light.
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.
Capstone Project: Assist the blind in moving around safely by warning them of impending obstacles using depth sensing, computer vision, and tactile glove feedback.
A project for ticketing automated challan for overspeeding vehicles on a decentralized network, and generating dynamic traffic signal timings based on traffic.
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)
the project includes system design of a t intersection traffic light controller and its verilog code in vivado design suite.
Pedestrian Traffic Light Detector for visually impaired people
An attempt of our team to tackle the problem of traffic congestion using deep learning and IoT
OpenTrafficLab is a MATLAB environment capable of simulating simple traffic scenarios with modular vehicle and junction controllers.
Capstone project for Udacity's Self Driving Car Nanodegree
🚥 An Artificial Neural Network based Traffic Light Controller for intersections. Computational Intelligence class final project.
In this project, we built ROS nodes to implement the core functionality of the autonomous vehicle system, including traffic light detection and classification, vehicle control control, and waypoint following.
Python3 modul and cli to controll the Cleware traffic light
Proyecto de control de trafico e intercepción de semáforos inteligentes.
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.
Traffic Light LED experiments with bash scripting for Raspberry Pi.
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