Abstract
In next-generation cars, safety equipment related to assisted driving systems commonly known as ADAS (advanced driver-assistance systems) are of particular interest for the major car-makers. When we talk about the “ADAS system”, we mean the devices and sensors having the precise objective of improving and making car driving safer, and among which it is worth mentioning rain sensors, the twilight sensor, adaptive cruise control, automatic emergency braking, parking sensors, automatic signal recognition, and so on. All these devices and sensors are installed on the new homologated cars to minimize the risk of an accident and make life on board of the car easier. Some sensors evaluate the movement and the opening of the eyes, the position of the head and its angle, or some physiological signals of the driver obtainable from the palm of the hands placed in the steering. In the present contribution, the authors will present an innovative recognition and monitoring system of the driver’s attention level through the study of the photoplethysmographic (PPG) signal detectable from the palm of the driver’s hands through special devices housed in the steering of the car. Through a particular and innovative post-processing algorithm of the PPG signal through a hyper-filtering framework, then processed by a machine learning framework, the entire pipeline proposed will be able to recognize and monitor the attention level of the driver with high accuracy and acceptable timing.
Highlights
The continuous technological progress has seen, in recent years, modern cars become real traveling laboratories equipped with electronic driver assistance systems developed to protect the safety of the driver and passenger as much as possible
Once we have identified the optimal frequency setup using the described RL algorithm in order to proceed to the hyper filtering of the PPG signal, the latter will be disconnected from the pipeline as it is no longer needed for the operation of the proposed system concerning the discrimination and monitoring of the driver’s level of drowsiness
The competitive advantage of the method described with respect to the methods proposed in the literature lies in the fact that it does not require any data analysis in the frequency domain, as it happens in the methods based on the use of the heart rate variability (HRV) indicator
Summary
The continuous technological progress has seen, in recent years, modern cars become real traveling laboratories equipped with electronic driver assistance systems developed to protect the safety of the driver and passenger as much as possible. These electronic aids are indicated with the acronym. ADAS, that is, Advanced Driver Assistance Systems, and with this acronym, identify all the devices present in the car to increase driving comfort and safety levels. The possibility to detect an attention state of a driver may facilitate evaluation of his/her fitness to drive a vehicle, facilitating the prevention of road accidents. It is known that a correlation exists between drowsiness and heart rate variability (HRV), that is, a measure of heart activity over a beat-to-beat interval, so that estimating HRV of, for example, a driver, may permit obtaining useful information concerning possible
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