Driver inattention monitoring system for intelligent vehicles: A review

Abstract

This paper gives a review ofthe literature on driver inattention monitoring system for the purpose of active safe driving. In this paper driving inattention is classified into two categories: fatigue and distraction, while fatigue and distraction can also contain many types and levels. Individual difference on inattention phenomenon makes it more complicated to correctly detect and recognize driving inattention. Driver attention monitoring has been intensively researched in recent years and many approaches have been proposed, which include biological signal (EEG, ECG, EOG and sEMG) processing method, subjective report method, and behavior analysis method. This survey reviews a number of promising approaches and provides an overview of recent developments in this domain. The emphasis ofthis paper is to discuss the various methodologies to monitor driving inattention. We conclude with some thoughts about future directions.

Investigating the Feasibility of Vehicle Telemetry Data as a Means of Predicting Driver Workload

“…A taxonomy of driver inattention is provided by Regan et al (2011), who divides it into diverted (performing tasks unrelated to driving), restricted (fatigued or unwell), misprioritized (prioritizing unimportant driving tasks above critical tasks), neglected (lack of due care because of familiarity to the road environment), and cursory (rushed or panicked driving). Dong et al (2011) describes activities where the required attentional resources of the driver are increased as distractions, and fatigue as when driver attention is reduced generally. In this paper we consider distractions, and in particular their effects on workload when attentional resources are diverted from the driving task.…”

“…For instance, the resources available are lowered for a driver that is tired or fatigued, which can be increased by opening a window, cooling the interior of the vehicle, or encouraging the driver to take a break (Dong, Hu, Uchimura, & Murayama, 2011). The demands of a driver can be reduced, through both design and real time adaptation in the vehicle.…”

“…The allocation of attentional resources can be considered as driver workload, which describes the impact of tasks on drivers Dong et al, 2011). Workload is usually measured using subjective or physiological measures, or by analysing the performance or behaviour of the driver.…”

Redundant Feature Selection for Telemetry Data

“…Based on these inferences, the agent can determine whether or not to present the driver with new information that might unnecessarily add to their workload. Traditionally, such agents have monitored physiological signals such as heart rate or skin conductance [2,16]. However, such approaches are not practical for everyday use, as drivers cannot be expected to attach electrodes to themselves before driving.…”

Detecting emotional stress from facial expressions for driving safety

“…Most of the research focused on detecting drowsiness of the driver, which according to [1], causes a large percentage of the car accidents. Recently, reports [2,3] also show that the emotional status (e.g.…”