Can AI help alert the driver with a value of his fatigue levels?

The dangers of fatigued driving are clear to all. On average, more than 500 fatal accidents in the United States each year due to driver fatigue. Driver drowsiness can be deadly. The good news is that it is possible to avoid it.

Artificially ensuring sleep and avoiding alcohol before driving can effectively reduce accidents. Of course, it can also do technical aspects to warn to prevent fatal accidents caused by fatigue driving.

The Driver Drowsiness Detection System (DDS) can detect behaviors such as eye-opening and constant yawning and take reasonable measures to alert the driver.

So, how does the DDS work?

The AI-based algorithm comes up with a value by calculating the frequency of sudden movements, the time of the day, trip duration, deviations from lane markings, and the frequency of hitting the rumble strip. Suppose the value is above a certain level; the system flashes a coffee cup symbol on the car's instrument panel, indicating that the driver needs to take a break.

Drivers are constantly monitored to determine their level of fatigue, which is determined by tracking their facial features, head movements, blinks, and eye movements by infrared cameras facing the driver.

Such a system has been in use for several years. Leading car companies such as Mercedes-Benz, Land Rover, and Volvo have developed monitoring systems for driver condition or vehicle operation.

Advantages and disadvantages of driver drowsiness detection systems

First, it can significantly reduce the number of fatal accidents caused by driver fatigue. Through the warning system, timely avoid significant accidents and save the lives of drivers and co-passengers.

However, it is often straightforward to capture the eyes because some sunglasses, hats, and other occlusions are tough, which significantly affects the system's accuracy.

To train the DDS model accurately, you need a comprehensive training data set, especially for the image's real facial key point marking, so that the driver's facial features can be accurately identified.

In addition to the necessary eye blink detection, yawning is an important signal that makes the model highly reliable in application scenarios with a high-quality and highly accurate training dataset.

Nexdata has many years of experience and is recognized by the world's top vendors. Deploying your DDS model using our high-quality and high-accuracy training data can be efficient. Ready to meet the market challenges?