Drowsy Driver Smart Detection System for Heavy Vehicles
Studies have shown that driver fatigue and drowsiness can cause shortfalls in performance, including slower response times, attention failures, and poor decision making.
The main objective of the drowsy driver smart detection system is to detect whether the driver is drowsy or not and deploy different alert systems based on the inputs taken from the driver.
Key Contributions
Conducted a thorough literature survey to identify physiological, vehicular, and behavioral parameters as key determining factors. Designed and developed a measurement unit integrating a complex sensor array with output indicators customized to the project’s requirements.
Mentor
Dr. Chidanand Dhondappa Koshti Department of Mechanical Engineerinng MIT-WPU
Milestones
2 nd MARCH 2023 HACK MIT- WPU 2023 , Runner Up South African Patent Granted (2023/03138)
System Architecture
The system processes three key input parameters to assess driver alertness. Vehicular data is gathered through pressure sensors on the steering wheel, behavioral data is analyzed via facial expression recognition for signs of drowsiness, and physiological data is monitored using a wearable band tracking heart rate. The collected data follows a structured sequence—behavioral first, followed by vehicular, and then physiological—before being parsed into the database. The control system then determines the appropriate response and activates the necessary actuators, including a buzzer, alarm lights, and vibrating motors, to alert the driver.
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Development
The system was crafted in three distinct parts—physiological, behavioral, and vehicular parameters—developed independently and seamlessly integrated. The priority feature designates behavioral as the primary drowsiness indicator, followed by vehicle and physiological parameters for comprehensive detection.
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Prototype
These three visuals represent outcomes generated by the camera parameter, a behavioral assessment tool.The algorithm behind this parameter utilizes facialfeatures and eye movement to evaluate a driver'scondition in three key positions:
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