Abstract
Drowsiness is not only a core challenge to safe driving in traditional driving conditions but also a serious obstacle for the wide acceptance of added services of self-driving cars (because drowsiness is, in fact, one of the most representative early-stage symptoms of self-driving carsickness). In view of the importance of detecting drivers’ drowsiness, this paper reviews the algorithms of electroencephalogram (EEG)-based drivers’ drowsiness detection (DDD). To facilitate the review, the EEG-based DDD approaches are organized into a tree structure taxonomy, having two main categories, namely “detection only (open-loop)” and “management (closed-loop)”, both aimed at designing better DDD systems that ensure early detection, reliability and practical utility. To achieve this goal, we addressed seven questions, the answers of which helped in developing an EEG-based DDD system that is superior to the existing ones. A basic assumption in this review article is that although driver drowsiness and carsickness-induced drowsiness are caused by different factors, the brain network that regulates drowsiness is the same.
Highlights
The other related issue is the length of time window, because it determines the frequency of EEG features generation, which is important in the ability of early detection
The lengths of minimum and regular time windows for heart rate variability (HRV) analysis are 3 min and 5 min, respectively [97]; in contrast to this, we find that 1 min is the most favored length of time window for EEG‐based drivers’ drowsiness detection (DDD) methods
The closed‐loop studies are structured in terms of the following two important fields of real‐
Summary
Driverless cars seem to be a ground‐breaking and once‐and‐for‐all solution for the driver drowsiness issue. This is especially pertinent given that General. Duce drivers’ drowsiness being a cause for accidents in traditional driving scenarios, but would address a serious obstacle in the widespread acceptance of driverless cars. This is imperative in semi‐self‐driving scenarios, requiring drivers to rapid‐. In this kind of system, EEG sensors are used to record the noise‐contaminated and weak brain bio‐potentials.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have