SENCAR Smoke Detection Prototype
Developing an AI-powered smoke and odor detection system for rental car fleets using embedded AI chips and IoT sensors with 15-30 seconds detection accuracy.
Project Overview
SENCAR, a leading private rental car company in Ho Chi Minh City, partnered with AlphaBits to develop and test an innovative AIoT prototype for their fleet management. The project focused on creating an embedded AI system capable of detecting smoke and specific odors (including durian) in rental vehicles with high accuracy and reliability.
Our solution involved developing a prototype using cutting-edge AI embedded chips that could provide real-time detection within 15-30 seconds while maintaining continuous monitoring for extended periods. This technology addresses critical fleet management challenges including vehicle damage prevention, customer safety, and policy enforcement.
Key Challenge
SENCAR needed a reliable, automated system to detect smoking and strong odors in their rental vehicles to maintain fleet quality, ensure customer satisfaction, and enforce rental policies without manual inspection of every vehicle return.
Project Details
Technologies Used
About SENCAR
Our trusted partner in automotive innovation and fleet management solutions.
Leading Private Rental Car Company
SENCAR is a prominent private rental car company based in Ho Chi Minh City, Vietnam. They specialize in providing high-quality vehicle rental services with a focus on customer satisfaction and fleet excellence.
With a commitment to innovation and technology adoption, SENCAR continuously seeks advanced solutions to enhance their fleet management capabilities and maintain the highest standards of service quality.
Visit SENCAR Website
SENCAR's Rental Services
Prototype Testing in Action
Real-world testing of the smoke detection system in SENCAR's rental vehicles.
Live demonstration of the AI-powered smoke detection system during field testing at SENCAR's Ho Chi Minh City HQ.
Technical Solution
Advanced AI embedded chip technology for real-time odor and smoke detection.
AI Embedded Chip
Custom AI chip designed for real-time smoke and odor detection with machine learning capabilities optimized for automotive environments.
- • 15-30 second detection accuracy
- • Continuous 24-hour monitoring
- • Low power consumption
- • Edge computing capabilities
Multi-Odor Detection
Advanced sensor array capable of detecting various odors including smoke, durian, and other prohibited substances in rental vehicles.
- • Smoke detection algorithms
- • Durian odor identification
- • Customizable sensitivity levels
- • False positive reduction
Real-Time Analytics
Instant data processing and alert system providing immediate notifications when violations are detected in the vehicle.
- • Instant alert notifications
- • Data logging and reporting
- • Fleet management integration
- • Historical trend analysis
Prototype Performance Results
Impressive accuracy and reliability metrics achieved during the 4-week testing period.
Client Testimonial
"Working with AlphaBits on this AIoT prototype has been transformative for our business. Their innovative approach to smoke and odor detection using embedded AI chips demonstrates the future of fleet management technology. The 15-30 second detection accuracy and continuous monitoring capabilities will help us maintain the highest standards of vehicle quality while ensuring customer satisfaction."
4-Week Implementation Process
Agile development approach with rapid prototyping and continuous testing.
Requirements Analysis & Design (Week 1)
Comprehensive analysis of SENCAR's fleet management needs, technical specifications definition, and AI chip architecture design for automotive environment.
Prototype Development (Week 2)
AI embedded chip programming, sensor integration, and initial machine learning model training for smoke and durian odor detection algorithms.
Testing & Optimization (Week 3)
Extensive field testing in SENCAR vehicles, algorithm refinement, accuracy optimization, and continuous monitoring validation.
Final Validation & Documentation (Week 4)
Final prototype validation, comprehensive testing documentation, performance analysis, and recommendations for production scaling.
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