we present the plans of a driver-assistance system, which will be capable of road lane and traffic sign detection by using an OPEN-CV.

AI-powered Driver Drowsiness Detection System using Computer Vision & Machine Learning for real-time driver alertness monitoring and accident prevention.

This repository is for self driving car project developed as a part of Software Engineering project at NIIT University

The Safer Streets Priority Finder enables you to analyze the risk to bicyclists and pedestrians on your community’s roads.

Multi-dimensional Analytics Project on Road Accidents of India.

A dataset for traffic accident analysis in the US

The "Driver Coach" system is based on a camera, sensors and machine learning algorithms. It monitors car driver behaviour and provides feedback to improve road safety. The system can be part of a larger Road Safety ECO system sharing data between driver and organisations (Smart City).

Emergency Index (EI): A two-dimensional surrogate safety measure considering vehicles' interaction depth

Demo telematics app for Flutter. The application walks you through the telematics SDK integration. The technology is suitable for UBI (Usage-based insurance), shared mobility, transportation, safe driving, tracking, family trackers, drive-coach, and other driving mobile applications

A project to detect accident and send notification to hospitals whenever a accident happens.

✔👉A Centralized WebApp to Ensure Road Safety by checking on with the activities of the driver and activating label generator using NLP.

Detect road anomalies such as cracks, potholes, and bumps using our trained YOLOv8 models with visual demo. Real-time detection via Streamlit and Flask app

Codebase for the paper 'SS SFDA: Self-Supervised Source Free Domain Adaptation for Road Segmentation in Hazardous Environments'

Semi-autonomous system for lane keeping based on C++ & OpenCV 4.x

A Repository to Train a Custom Yolov4 based object detector for road damage detection using the RDD2020 dataset

A project which helps prevent accidents caused by the driver getting drowsy. The project is built on python using OpenCV library.

R Package: Road Condition Analysis

This repository offers code to reuse methodology and repeat experiments in the study "Learning Collision Risk Proactively from Naturalistic Driving at Scale".

Reproducible road safety research with R: a practical user manual

Here, I analyze the Road Safety and Traffic Demographics dataset (UK), containing accidents reported by the police between the years of 2004 - 2017.

This is my bachelor's thesis, which contains three main features: lane detection, road segmentation, and a Forward Collision Warning (FCW) system

Detects motorcycles with more than two riders, helmet compliance, and mobile usage using YOLOv8, trained on a custom dataset of 6,000+ images.

Bosch Hackathon

R package to support road safety and traffic calming measures

Road transport is the most widely used means of transportation around the world. With this high use of road transport, the safety of travellers’ becomes the prime concern for any governing authority. While some safety concerns arise from driver errors and environmental factors, most cases are a result of poor maintenance of these roads. Potholes, specifically, are one of the leading causes of road accidents throughout the world and need to be taken care of immediately, by the authorities. This paper presents a solution that makes use of civilians’ mobile sensors, along with image-based alternatives to detect potholes in real-time, using Machine Learning. The concerned authorities are then notified about the same through a web-based portal, to take the necessary action. The solution also incorporates pivoting existing complaints, location tagging and prioritization. Additionally, the solution provides a forecast of the likelihood of issues regarding potholes, constantly updating time series data of the locations.

Citizen radar for slow streets 🛜 tracking velocity, not identity

A project developed for Robert Bosch UN IRSC Road Safety Hackthon for Driver Intent Classification.

A comprehensive software system leveraging convolutionary neural networks, drones, and IoT for real-time road quality monitoring and alerts. Built using Python, TensorFlow, Django, and Vue.js.

Road accident risk regression (PS S5E10): LightGBM + residual XGBoost + NNLS blend for stable OOF RMSE.

This repository follows the paper "Identifying urban features for vulnerable road user safety in Europe".