It’s no secret that most car crash fatalities are a result of human error. Getting computers to handle car driving may be an uncomfortable prospect for many. It’s not the only solution, for sure. But with adequate checks and balances, combining human intelligence with machine learning for safer driving heralds an exciting future. The why behind drowsiness detection systems is a no-brainer. How they function deserves much more attention, in our view. Detection systems assess driver alertness in several incredible ways. For instance, they evaluate factors like driving duration or how drivers operate the steering wheel – reminding drivers to take a break if patterns seem off.
Biology and Facial Characteristics
Algorithms analyze facial characteristics like yawning, blinking, or head movement to indicate drowsiness. Detection systems go further and study minute details like inner and outer eye corners, the tip of the nose, pulse rate, heart rate, and EEG. Varying physiological indicators are accurate ways to determine driver drowsiness.
Vehicle Performance and Behavior
Driver fatigue and vehicle behavior are closely linked. Detection systems continually monitor a car’s speed, angle, and position according to car type, road conditions, and the weather. Together, these elements are telling indicators of drowsiness.
Images and Videos for Analysis
Computers analyze a ton of facial expression images – sorting out useful data and discarding the rest. Similarly, they convert video frames into a series of relevant images.
Active safety technology goes miles ahead in ensuring driver safety. Not only does it evaluate driver behavior but accounts for vehicle performance and surroundings. Early warning systems help drivers prevent accidents altogether or considerably reduce the impact in dangerous situations.