How to Cite
Abstract
In this field, complex systems can be developed to ensure that driving a car is safe in a given area. In automated cars, there are two approaches: adapting advanced technology from automotive to adapt to a car, creating a platform-based multimedia sensor-based data acquisition and processing of all air commercial and experimental solutions. We focus on AI-based recognition where ML algorithms infer information from road, environment, e.g. as it has been done in recent years and used a camera system only by AI [1].
[2] [3]- Driving is an attention-demanding task that can be greatly impacted by various factors, including drowsiness, use of alcohol (Rizos and Hunt, 2017; Durosai and Wen, 2020), and poor vehicle conditions (Wang and Zohar, 2003). According to the National Safety Council (NSC), in 2020 42,060 people died in motor vehicle crashes in the US, which is the highest number since 2007 and almost an 8% increase (Road safety, 2020). The most frequent accidents are caused by disorientation, inadequate routing information, or loss of concentration, and are more likely to originate from one of several physiological phenomena. Factors that may lead to an accident include: a. Drowsiness, b. Alcohol and drugs, c. Emotions, d. Physical impairment. There are various solutions, from physiological (PCR) to automatic (emissions) early detection of driver fatigue / sleepiness, which can prevent accidents. The ability to predict drowsiness and ultimately sleep is very useful for additional vulnerabilities, such as monitoring system operators.
References
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