An Intelligent Online Drunk Driving Detection System Based on Multi-Sensor Fusion Technology.

Area of Science:

• Automotive Engineering
• Sensor Technology
• Road Safety

Background:

• Drunk driving remains a major threat to road safety.
• Existing single-sensor devices lack accuracy and reliability.
• Need for dependable online drunk driving detection systems in vehicles.

Purpose of the Study:

• To design and develop an advanced multi-sensor fusion system for online drunk driving detection.
• To overcome limitations of single-sensor devices in accuracy and dependability.
• To enhance vehicle safety by preventing impaired driving.

Main Methods:

• Designed a sensor array utilizing gas diffusion models and steering wheel characteristics.
• Developed a data fusion algorithm incorporating support matrices for data consistency.
• Employed an adaptive weighted fusion algorithm for multi-sensor data integration.
• Conducted simulation experiments with multiple participants and alcohol consumption levels.

Main Results:

• The multi-sensor fusion system accurately detects drunk driving when alcohol consumption exceeds 50 mL and the driver is in the driver's seat.
• The system correctly distinguishes between a drunk driver and a drunk passenger.
• Vehicles equipped with the system automatically lock upon detecting an impaired driver after a voice prompt.

Conclusions:

• The developed online intelligent detection device effectively enhances drunk driving prevention.
• Multi-sensor fusion significantly improves the accuracy and reliability of in-vehicle detection systems.
• This technology provides crucial support for reducing alcohol-impaired driving incidents and improving road safety.