Hypovigilance warning system: A review on driver alerting techniques

Abstract

In recent years, driver hypovigilance (drowsiness and inattentiveness) is one of the major causes for road accidents and it leads to severe traumas such as physical injuries, deaths, and economic losses. Several researchers have reported on driver drowsiness detection and only very few works have been reported on detecting the driver hypovigilance. The physiological signals and vision based approaches are mostly used for estimating the driver's hypovigilance. When the physical state of the driver is drowsy or inattentive, it is important to alert the driver through alerting methods to avoid the accidents. In this paper, we have reviewed various methods used to alert drivers and the issues involved in detecting the hypovigilance. There are many methods adopted for alerting the driver during hypovigilance such as alarms, visual effects, vibrations, etc. This present work discusses in detail about the different methods used for driver alertness system. The level of alertness is based on the hypovigilance level of the driver. The hypovigilance detection system should be designed in such a way to efficiently trigger the alert system only when the driver is in hypovigilance state.

A BCI Based Alerting System for Attention Recovery of UAV Operators

“…There are a vast amount of studies on the inattention detection of drivers or pilots using diverse measures, such as subjective, vehicle-based, behavioral, and physiological measures [ 48 ]. Subjective measures indicate the result of a survey, which is hard to be utilized in a real-time system, and vehicle-based and behavioral measures capture the event that occurred due to an operator’s inattention state.…”

Psychophysiological models of hypovigilance detection: A scoping review

“…These physiological responses can thus be integrated into predictive models to detect hypovigilance for safety purposes (EuroNCAP, 2017; Schwarz & Fuchs, 2018). Multiple reviews have been published to summarize methods for assessing hypovigilance and other related concepts from multiple perspectives (e.g., Arun et al, 2011; Bafna & Hansen, 2021; Bier et al, 2020; Duffy & Feltman, 2022; Larue et al, 2010; Mogilever et al, 2018; Mohanavelu et al, 2017; Sahayadhas et al, 2012). For instance, Bendak and Rashid (2020) provide a systematic review of the causes of fatigue observed in the aviation industry and the ways to measure it.…”

Effects of Individual Vehicle Differences on Advanced Driver-Assist System Takeover Alert Behavior

“…The driver monitoring component is paired with an alerting system that presents important updates to the driver, including requests for the driver to take over control of the vehicle. Driver takeovers are more effective when multiple alert modalities are combined, although haptic feedback is often most effective whereas visual feedback is often least effective (7)(8)(9). Regardless of the alerting modality, drivers are generally faster in taking control when they anticipate that the vehicle will be unable to perform, when they are less distracted by secondary tasks, and when they experience lower levels of cognitive load (10)(11)(12).…”