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Driver drowsiness detection is a car safety technology which helps prevent accidents caused by the driver getting drowsy. Various studies have suggested that around 20% of all road accidents are fatigue-related, up to 50% on certain roads.
Advanced driver-assistance systems (ADAS) are technologies that assist drivers with the safe operation of a vehicle. Through a human-machine interface, ADAS increase car and road safety. ADAS use automated technology, such as sensors and cameras, to detect nearby obstacles or driver errors, and respond accordingly.
2008: Improved driver monitoring system added on the Crown for detecting whether the driver's eyes are properly open. [58] It monitors the driver's eyes to detect the driver's level of wakefulness. This system is designed to work even if the driver is wearing sunglasses at night. 2008: PCS with GPS-navigation linked brake assist function on the ...
Adaptive cruise control does not provide full autonomy: the system only provides some help to the driver, but does not drive the car by itself. [3] For example, the driver is able to set the cruise control to 55mph, if the car while traveling that speed catches up to another vehicle going only 45mph, the ACC will cause the car to automatically brake and maintain a safe distance behind the ...
If no action is taken, the vehicle will apply the brakes (a warning alarm will sound followed by a brief automatic application of the braking system). This system is said to be the first of its kind. [2] In 2008, the Toyota Crown system went further and can detect if the driver is becoming sleepy by monitoring the eyelids. [4] [5]
A lane detection system used behind the lane departure warning system uses the principle of Hough transform and Canny edge detector to detect lane lines from realtime camera images fed from the front-end camera of the automobile. A basic flowchart of how a lane detection algorithm works to help lane departure warning is shown in the figures.
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Other major algorithms for character recognition includes Haar-like features, Freeman Chain code, AdaBoost detection and deep learning neural networks methods. Haar-like features can be used to create cascaded classifiers which can then help detect the sign board characters. Deep learning can be incorporated into traffic-sign detection.