Mobile IoT Driving Monitor for Real-Time Drowsiness Alerts
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Solution Overview
Problem
Current systems lack real-time monitoring and warning capabilities to prevent traffic accidents, particularly those caused by drowsy driving, as they rely on post-accident reports and data aggregation, which are ineffective in anticipating and mitigating such incidents.
Innovation Solution
A mobile IoT agent-based safe driving support system that collects and analyzes real-time data from sensors, including heart rate, vehicle position, and driving status, using a GNSS platform to detect potential hazards and alert drivers, and provides an accident response service to prevent traffic accidents by constructing big data for predictive analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time sensor data collection and analysis is implemented, then traffic accident prevention capability is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the monitoring function into multiple independent sensor modules (heart rate sensor, acceleration sensor, position sensor) that can be independently deployed and managed. Each sensor collects specific physiological or vehicle parameters, and the data is processed separately before being integrated for comprehensive drowsy driving detection.
Solution Approach 2:
The patent introduces an intermediary processing layer that receives data from multiple sensors, performs preliminary analysis, and filters information before transmitting to the central system. This intermediary layer reduces the complexity of direct multi-sensor integration and enables scalable system architecture.
2Measurement precision
If multiple sensors are deployed for comprehensive monitoring, then detection accuracy is improved, but energy consumption and device weight increase
Solution Approach 1:
The system employs partial monitoring by selectively activating specific sensor groups based on driving conditions and risk levels. Instead of continuously operating all sensors at full capacity, the system adjusts monitoring intensity dynamically, reducing energy consumption while maintaining adequate detection accuracy for critical safety parameters.
3Speed
If real-time data processing is performed, then response time is improved, but computational load and processing time increase
Solution Approach 1:
The system performs preliminary data processing and pattern recognition at the sensor level and edge devices before transmitting data to central servers. Basic anomaly detection and feature extraction are completed in advance, reducing the computational load and processing time required for real-time decision-making and alert generation.
Data Source
AI summary
The present disclosure relates to a safe driving support system based on a mobile Internet of Things (IoT) agent, and a processing method thereof. The safe driving support system based on a mobile IoT agent may provide an accident response service for preventing a traffic accident in advance by obtaining GNSS-based position information of each of vehicles, collecting various information on a driver's status and a vehicle driving status of the vehicle that is being driven in real time to construct big data, and analyzing and repeatedly learning the collected information. According to the present disclosure, it is possible to provide the ground for providing various services capable of decreasing traffic accidents by obtaining driving information of a short cycle using a GNSS platform and analyzing the driving information to apply the driving information to traffic safety and accident prevention activities.