Normally, it is seen that 90% of road accidents happen just because of driver behavior and fault. Vehicle accidents not only cause fatalities and injuries but they are also the reason of financial losses and low productivity. The careless behavior of driver also put other drivers at risk. In fact, many people don’t even recognize they are a high-risk motorist. Many surveys have revealed this fact that realizing careless driving behavior can actually reduce the occurrences of road accidents. At times, it is seen that when drivers understand the direct connection of reckless driving with road accidents or they know a driving pattern is being monitored by a centralized system, the mode of driving improves automatically.
Nowadays, many transport companies start employing the solutions like Fleet Management Systems to improve the bad driving pattern of their drivers. In fact, they track, train and benchmark their drivers to develop more economical and environment-friendly fleets. Some insurance companies also use advanced technologies to track driving pattern to reduce their insurance settlement costs. Government authorities of developed countries install driver behavior monitoring system in their system for speed enforcement; mitigate theft and misuse of equipment.
Highly experienced drivers often use driver behavior monitoring apps to be safe and sound and also to decrease fuel costs. Telemetry devices are installed on vehicles with compatible software to track the driving pattern. In recent times, driver drowsiness and distraction have become important factors in accidents as they decrease driver’s perception level and decision-making ability. Installation of driver monitoring system is the only way to reduce the occurrences of road accidents. In case of drowsiness and distracted state and unsafe driving behavior, the system will automatically alert the driver to contribute to safe driving.
Now, I am going to discuss different methods for driving and driver monitoring and also to predict unsafe driving behaviors. Usually, to measure the driver drowsiness behavior, visual feature measurements like eye related measurements, facial expression, and yawning detection can be very useful. Driver drowsiness is detected using physiological signals and drowsiness detection methods. To know the status of driver distraction, the system is described with the head pose and gaze direction methods. To detect vehicle-based features, the system reads the steering wheel movement and deviation of lateral position. To trace out driver distraction, head position and gaze direction methods are employed in the system. This system is highly useful in reducing road accidents.