Smart Speed Control and Warning System in Vehicles using IoT & GPS-based Dynamic Speed Regulation

S.No	Team Member Name
1	Ashik Mohamed S
2	Akash A
3	Jayasuriya S
4	Mohamed Imthiyas H

1. Project Overview

This project implements a simulation of an Intelligent Speed Adaptation (ISA) system using Python. It models a vehicle navigating a geofenced environment where speed limits are dynamically enforced based on spatial location. The system integrates IoT telemetry via MQTT to broadcast real-time vehicle state, simulating a connected vehicle architecture.

2. Technical Architecture

The application is built upon a real-time simulation loop operating at 60 Hz.

Subsystem	Core Functionality
Physics Engine (vehicle.py)	Kinematics: 2D vector physics (pygame.Vector2) for movement. Dynamics: Simulates friction, braking, and steering. Control: Decoupled input handling via delta-time (dt).
Geofencing (world.py)	Zone Detection: Real-time AABB collision detection. Mapping: Manages Zone objects with metadata (Limit, Name).
Control Logic (speed_controller.py)	State Machine: Manages vehicle status (SAFE, WARNING, OVER_SPEED). Hysteresis: Uses time thresholds (0-3s) to prevent flickering. Intervention: Enforces speed limits during regulation.
IoT Telemetry (mqtt_client.py)	Async Comm: Runs on a daemon thread to avoid blocking. Protocol: Publishes JSON telemetry via MQTT v3.1.1.

3. Module Breakdown

File	Description
main.py	Entry Point & Orchestrator: Manages the main event loop, delta-time calculation, and subsystem coordination.
vehicle.py	Physics Model: Handles vector math for movement, rotation matrices for steering, and speed clamping.
world.py	Spatial Manager: Defines the coordinate system and manages the list of Zone entities.
speed_controller.py	Logic Core: Pure logic class implementing the ISA rules and timing mechanisms.
ui.py	Renderer: Handles blitting of HUD elements, text rendering, and coordinate transformation for the minimap.
mqtt_client.py	Network Interface: Wraps the paho-mqtt library for threaded publish/subscribe operations.
datalogger.py	Persistence Layer: Writes structured CSV data for post-simulation analysis.

4. Key Features

Feature	Description
Vector-Based Movement	Realistic acceleration and drift mechanics.
Dynamic Speed Limiting	Real-time modification of vehicle constraints based on geospatial data.
Event-Driven Logging	Captures state transitions (e.g., Regulation Activation) with timestamps.
Remote Monitoring	Live data stream via MQTT topic vehicle/telemetry.

5. Controls & Configuration

Category	Details
Input Controls	Arrow Keys (Movement), O (Override), R (Reset), F (Fullscreen).
Configuration	Constants for physics, network, and display centralized in config.py.

6. Execution

Step	Action	Command
1. Dependencies	Install required libraries	pip install pygame paho-mqtt numpy geopy or pip install -r requirements.txt
2. Launch	Run the simulation	python main.py

Tech Stack: Python 3, Pygame (SDL Wrapper), Paho-MQTT, NumPy.