Team Lead
Shashidhar Reddy
20J41A6623
Led the project direction, system planning, and overall implementation workflow.
Adaptive Traffic Intelligence
Stop wasting green lights on empty roads.
RoadSense is a smart traffic analytics system concept where cameras above intersections use YOLOv8 vehicle detection and congestion scoring to reduce unnecessary red-light waiting. Instead of giving green time to roads with zero traffic, the system shifts priority toward the lanes that actually need movement, while still protecting emergency vehicles with instant safe override logic.
Camera A
Camera B
Emergency Watch
YOLOv8 Feed
Queue Score
Priority Alert
Adaptive Control
Green extends where density is higher
The Real Problem
One side waits too long while the other side gets an empty green.
01
Observe
Cameras mounted above signals continuously watch incoming lanes.
02
Detect
YOLOv8 identifies cars, buses, trucks, bikes, and emergency vehicles.
03
Measure
Vehicle counts become live density scores that estimate waiting pressure.
04
React
The controller gives green time to the lanes that actually need it most.
Adaptive Behavior
Traffic lights should understand what is happening on the road.
Instead of keeping fixed red and green intervals, the system analyzes real-time lane occupancy and changes the signal phase accordingly. This directly targets the common issue where stopped vehicles build up at one direction while another direction remains empty.
Emergency Handling
Emergency vehicles should not wait behind a blind red light.
If a police vehicle, ambulance, or fire truck is detected at a red phase, the controller starts an alarm event, turns the intersection into a safe all-red state, and prepares a clear path for fast emergency passage.
Mounted Hardware
CCTV camera + LED response unit
Each traffic light pole carries a road-facing camera on top and an LED alert unit underneath. During emergency detection, the LED module flashes red and blue so nearby drivers and operators know the junction has entered emergency priority mode.
Live Simulator
Watch one intersection think before it changes the signal.
Mode: Adaptive Balance
Analyzing queue intensity
North-South Density
82
East-West Density
31
Signal Decision
Extend North-South Green
Higher traffic load detected in vertical movement.
ALERT
System Engine
From camera feed to signal decision in one closed loop.
Camera Capture
YOLOv8 Detection
Density Scoring
Adaptive Signal Logic
Emergency Protection
Resume Summary
Traffic Analytics System
Built a real-time traffic monitoring and adaptive signal control system using Python and YOLOv8 to detect vehicles, analyze density, visualize congestion behavior, and trigger emergency-aware overrides from live video streams.
Project Team
Built as a collaborative smart mobility project.
This system was developed as a team effort combining computer vision, traffic logic, and system design, with Shashidhar Reddy leading the project direction and execution.
Core Team
Srinidhi Chanda
20J41A6611
Contributed to the project build and collaborative development of the traffic analytics solution.
Core Team
Rohitha Padala
20J41A6642
Contributed to the project build and collaborative development of the traffic analytics solution.
Next Development Phase
Turning traffic analytics into a security-aware junction platform.
Phase 01
Plate Reader Intelligence
Add OCR-based number plate recognition so vehicles entering the signal zone can be identified, tracked, and checked against flagged records in real time.
OHIO
JCT 4821
Phase 02
Police Reporting Workflow
Automatically alert nearby police systems when stolen or flagged vehicles are detected at the intersection.
Phase 03
Multi-Signal Network
Connect multiple intersections into one city-level traffic intelligence layer for operations and planning.