ImVisible: Pedestrian Traffic Light (PTL) Dataset, Lightweight CNN (LytNet), and Mobile Application for the Visually Impaired (CAIP '19, ICCV Workshops '19)
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
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.
A simple yet effective repo for object detection based on the FCOS architecture.
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.
Python3 modul and cli to controll the Cleware traffic light
Capstone project for Udacity's Self Driving Car Nanodegree
Our solutions for Google Hashcode 2021 Traffic Signalling
🎓Repository for masters labs on FCSN, BSUIR
Traffic Light Reinforcement Learning
Optimize traffic flow at intersections with a Deep Q-Learning agent. Utilizes reinforcement learning to control traffic signals efficiently.
Despite the fact that computer vision is today a highly developed field, capabilities for recognition and classification are not frequently used in day-to-day activities. Therefore, in this project we will make use of the current traffic infrastructure with some additional requirements, to create and demonstrate an advanced traffic light management
An attempt of our team to tackle the problem of traffic congestion using deep learning and IoT
An advanced traffic management system (TMS) is a context-aware solution that relies on real-time data from connected road infrastructure and predictive analytics to effectively coordinate traffic across city arteries.
OpenLendaのPythonでのONNX推論サンプル
Use of Computer Vision to control Traffic Light
A TensorFlow implementation of TrafficLightNeuralNetwork (ANN-based Traffic Light Controller).
Q-Learning for Urban Traffic Optimization
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