This blog series is focused on the wealth of possibilities offered by an interaction between the car’s driver and the smart car powered by means of smart electronic systems that are able to detect the physiological status of the driver and take preventive and corrective actions as well to preserve the safety and the comfort of the occupants of the e-car.
A huge solution, in terms of effective monitoring of the status of drowsiness and related safety procedures to avoid dangerous situations, has been implemented by the Panasonic Company; this can be done by means of scanning of the driver’s eyes and also facial scanning (see Figure 1):
“Eye tracking, of course, is already a feature of some high-end vehicles, used to judge whether the driver is about to doze off, but suppliers are using the latest eye-tracking and facial recognition tech to take things further. Panasonic, most notably, showed a demo in its private automotive suite of a fully biometric cockpit, with a facial recognition system designed not just so that it can judge that you’re about to nod off, but based on your expression (and thus mood) it can actively predict whether you might be in about 15 minutes’ time. The technology raises numerous questions. The Panasonic system showed an interesting feedback avatar, giving the driver a real-time analysis of their mood and predictive state of drowsiness.” (Source: cardesignnews )
The Avatar created by Panasonic to give feedback signals about a driver’s drowsiness
The key element for facial recognition are the cameras that are utilized to scan the face of the driver while driving, the more effective and accurate is the scanning, the more safe will be the car occupants because of the assistance and support of smart electronic devices (see Figure 2):
“Most developers are already familiar with face detection. This is the process of analyzing an image and determining where a face is present. Many smartphones have this capability. Facial recognition is the next stage after face detection. It is the process of analyzing a face and developing a mathematical model using data derived from relative distances of facial features. These include the distance between the eyes, and relative position of the eyes to the nose, jaw, ears, and cheekbones. The angle of the face with respect to the facial recognition camera is also determined, as it is important in determining the relative distances. Skin tone can also be detected and used for recognition. That model is then compared to a database of stored mathematical models of known faces and an algorithm determines the best match. There is a probability (in percent) that the face detected matches a stored face. The alternative is that there is not a match, and therefore the face that is detected is unknown.” (Source: digikey)
Do you think that this type of solution is really effective and it will be largely utilized in the near future by the automotive industry?