02 — Case Study
Driver Monitoring System
An AI-powered fleet safety platform that monitors driver behavior in real time using Computer Vision and intelligent analytics.
- Year
- 2026
- Role
- Full Stack & AI Engineer
- Status
- Shipped
01
The Problem
Fleet operators struggle to monitor unsafe driving behaviors across large vehicle fleets. Manual supervision is inefficient, incidents are often discovered too late, and safety decisions lack real-time evidence and actionable insights.
02
The Solution
Built a full-stack fleet management platform that combines real-time Computer Vision, AI-powered analytics, GPS safety monitoring, intelligent alerts, and an administrative dashboard. The system continuously detects risky driver behaviors, stores photographic evidence, tracks trips, and enables fleet managers to monitor operations through a secure web application with an integrated RAG-powered AI assistant.
03
The Result
Delivered a scalable AI-driven platform capable of monitoring drivers in real time, detecting multiple safety violations simultaneously, generating automated alerts, maintaining complete audit trails, and providing natural-language access to fleet data through an intelligent assistant.
Engineering highlights
- Real-time Computer Vision pipeline powered by multiple YOLO models for drowsiness, smoking, phone usage, seatbelt, and hands-on-wheel detection
- AI-powered RAG assistant using LangChain, MongoDB Atlas Vector Search, sentence-transformers, and Groq LLMs for natural language fleet analytics
- Live monitoring with Socket.IO, instant safety alerts, and real-time dashboard updates
- GPS tracking with speed-limit detection, harsh braking analysis, and location-based safety monitoring
- Secure admin platform featuring JWT authentication, RBAC, refresh-token rotation, CSRF protection, audit logging, and role-based management
- Fleet management modules for drivers, buses, routes, trips, violations, reporting, and historical analytics
- Python FastAPI AI microservices integrated with a Node.js & Express backend using a modular service architecture
- Evidence-based violation logging with image snapshots, confidence scoring, and automated notification workflows
Stack
- React
- Node.js
- Express
- MongoDB Atlas
- Socket.IO
- FastAPI
- Python
- YOLOv8
- OpenCV
- LangChain
- Groq LLM
- MongoDB Atlas Vector Search
- Sentence Transformers
- JWT
- Tailwind CSS
