Smart Parking Meter

Project Overview

• A multi-phase smart parking infrastructure starting with core device capabilities and evolving into AI-powered edge analytics.
• Phase 1: Sensor data collection and Thingsboard integration for remote management.
• Phase 2: AI-driven edge intelligence for real-time vehicle detection, license plate recognition, and tamper detection.
• Phase 3: Integration of EV charging capabilities for future-ready urban mobility.
• Beta testing across three distinct climates — India, Canada, and USA — to ensure environmental resilience.

Challenges

High-Performance Hardware Platform (NVIDIA Jetson)

• NVIDIA Jetson AGX Orin 32GB providing 200 TOPS of AI performance.
• 8-core ARM CPU and 1792-core NVIDIA Ampere GPU with 56 Tensor Cores.
• 32GB LPDDR5 RAM paired with 64GB eMMC and 256GB minimum NVMe SSD for encrypted data storage.
• Hardware watchdog timer for automatic system resets and battery backup ensuring 24-hour minimum runtime.

Comprehensive Sensor Suite

• Spatial Mapping: Solid-state LiDAR for 3D mapping with 40m maximum range and no mechanical parts.
• Visual Intelligence: 4K low-light camera capable of snapshots, video recording, and live streaming.
• Environmental Monitoring: Temperature, humidity, barometric pressure, and air quality (PM2.5, PM10, CO2, VOC).
• Proximity & Access: 4 ultrasonic sensors for object detection and dual-band RFID/NFC system for vehicle identification and payments.

Firmware & Secure Operations

• Ubuntu 20.04 LTS with NVIDIA JetPack 5.1 and Docker container orchestration.
• Redis Message Bus for inter-service communication and telemetry aggregation.
• Thingsboard MQTT platform for device management, telemetry, and RPC command processing.
• Primary 4G LTE cellular with auto-failover to WiFi or Ethernet.
• AWS S3 for secure storage of camera snapshots, video, and LiDAR point clouds.

Edge Intelligence & AI Models

• Vehicle Detection (98%): YOLOv8 architecture with real-time multi-vehicle tracking.
• License Plate Recognition (90%): Multi-region support (USA/CA/IN) with perspective correction.
• People Detection (99%): Safety-first monitoring with wheelchair and child detection.
• Tamper Detection (95%): Detects spray paint, sensor covering, and physical vandalism.
• Vehicle Classification (90%): 10+ categories including emergency vehicles, make/model recognition.

Real-Time Inference Pipeline

• Total pipeline latency from input to action maintained under 200ms.
• Multi-Sensor Fusion using Kalman Filter: Camera (40%), LiDAR (35%), Ultrasonic (15%), Radar (10%).
• Inference accelerated via TensorRT 8.5+ and DeepStream SDK for optimized GPU/DLA utilization.
• Automated real-time event generation for parking violations, beacon color changes, and audio announcements.

Predictive Analytics & Performance

• Occupancy Forecasting: LSTM-based time-series models for 4-hour prediction window of parking availability.
• Pattern Analysis: Identifies peak hour trends, turnover rates, and expected parking durations.
• Security & Privacy: Automatic face blurring for GDPR compliance and AES-256 encryption for data at rest.
• Scalable Deployment: Beta testing across India, Canada, and USA to ensure environmental resilience.

AI Model Performance

Hardware Specifications

Technology Stack