Wilhelm E. Kincses

Drivers’ misjudgement of vigilance state during prolonged monotonous daytime driving

To investigate the effects of monotonous daytime driving on vigilance state and particularly the ability to judge this state, a real road driving study was conducted. To objectively assess vigilance state, performance (auditory reaction time) and physiological measures (EEG: alpha spindle rate, P3 amplitude; ECG: heart rate) were recorded continuously. Drivers judged sleepiness, attention to the driving task and monotony retrospectively every 20 min. Results showed that prolonged daytime driving under monotonous conditions leads to a continuous reduction in vigilance. Towards the end of the drive, drivers reported a subjectively improved vigilance state, which was contrary to the continued decrease in vigilance as indicated by all performance and physiological measures. These findings indicate a lack of self-assessment abilities after approximately 3h of continuous monotonous daytime driving.

EEG alpha spindle measures as indicators of driver fatigue under real traffic conditions

OBJECTIVEnThe purpose of this study is to show the effectiveness of EEG alpha spindles, defined by short narrowband bursts in the alpha band, as an objective measure for assessing driver fatigue under real driving conditions.nnnMETHODSnAn algorithm for the identification of alpha spindles is described. The performance of the algorithm is tested based on simulated data. The method is applied to real data recorded under real traffic conditions and compared with the performance of traditional EEG fatigue measures, i.e. alpha-band power. As a highly valid fatigue reference, the last 20 min of driving from participants who aborted the drive due to heavy fatigue were used in contrast to the initial 20 min of driving.nnnRESULTSnStatistical analysis revealed significant increases from the first to the last driving section of several alpha spindle parameters and among all traditional EEG frequency bands, only of alpha-band power; with larger effect sizes for the alpha spindle based measures. An increased level of fatigue over the same time periods for drop-outs, as compared to participants who did not abort the drive, was observed only by means of alpha spindle parameters.nnnCONCLUSIONSnEEG alpha spindle parameters increase both fatigue detection sensitivity and specificity as compared to EEG alpha-band power.nnnSIGNIFICANCEnIt is demonstrated that alpha spindles are superior to EEG band power measures for assessing driver fatigue under real traffic conditions.

Alpha spindles as neurophysiological correlates indicating attentional shift in a simulated driving task

The intention of this paper is to describe neurophysiological correlates of driver distraction with highly robust parameters in the EEG (i.e. alpha spindles). In a simulated driving task with two different secondary tasks (i.e. visuomotor, auditory), N=28 participants had to perform full stop brakes reacting to appearing stop signs and red traffic lights. Alpha spindle rate was significantly higher during an auditory secondary task and significantly lower during a visuomotor secondary task as compared to driving only. Alpha spindle duration was significantly shortened during a visuomotor secondary task. The results are consistent with the assumption that alpha spindles indicate active inhibition of visual information processing. Effects on the alpha spindles while performing secondary tasks on top of the driving task indicate attentional shift according to the task modality. As compared to alpha band power, both the measures of alpha spindle rate and alpha spindle duration were less vulnerable to artifacts and the effect sizes were larger, allowing for a more accurate description of the current driver state.

Improved Team Performance Using EEG- and Context-Based Cognitive-State Classifications for a Vehicle Crew

We present an augmented cognition (AugCog) system that utilizes two sources to assess cognitive state as a basis for actions to improve operator performance. First, continuous EEG is measured and signal processing algorithms utilized to identify patterns of activity indicative of high cognitive demand. Second, data from the automobile is used to infer the ongoing driving context. Subjects participated as eleven 2-person crews consisting of a driver/ navigator and a commander/gunner. While driving a closed-loop test route, the driver received through headphones a series of communications and had to perform two secondary tasks. Certain segments of the route were designated as threat zones. The commander was alerted when entering a threat zone and their task was to detect targets mounted on the roadside and engage those targets To determine targeting success, a photo was taken with each activation of the trigger and these photos were assessed with respect to the position of the reticle relative to the target. In a secondary task, the commander was presented a series of communications through headphones. Our results show that it is possible to reliably discriminate different cognitive states on the basis of neuronal signals. Results also confirmed our hypothesis: improved performance at the crew level in the AugCog condition for a secondary communications tasks, as compared to a control condition, with no change in performance for the primary tasks.

Using Context to Identify Difficult Driving Situations in Unstructured Environments

We present a context-based machine-learning approach for identifying difficult driving situations using sensor data that is readily available in commercial vehicles. The goal of this system is improve vehicle safety by alerting drivers to potentially dangerous situations. The context-based approach is a two-step learning process by first performing unsupervised learning to discover meaningful regularities, or contexts, in the vehicle data and then performing supervised learning, mapping the current context to a measure of driving difficulty. To validate the benefit of this approach, we collected driving data from a set of experiments involving both on-road and off-road driving tasks in unstructured environments. We demonstrate that context recognition greatly improves the performance of identifying difficult driving situations and show that the driving-difficulty system achieves a human level of performance on cross-validation data.

Mobiles EEG zur Messung der mentalen Fahrerbeanspruchung

Analysen von Unfalldaten zeigen, dass 90 % aller Verkehrsunfalle auf menschliche Ursachen zuruckzufuhren sind [1, 2]. Fur die Entwicklung von Fahrerassistenzsystemen (FAS) ist es deshalb von zentraler Bedeutung, diese Ursachen und Faktoren zu kennen. Die Interaktion zwischen Fahrer, Fahrzeug und Umwelt ist allerdings sehr komplex und kann mit Hilfe traditioneller analytischer Methoden oft nur unzureichend untersucht werden. Die Daimler AG hat mit dem EEG ein bildgebendes Verfahren aus dem Labor ins Fahrzeug gebracht und fur die Untersuchung der Fahrerbeanspruchung unter realen Fahrbedingungen nutzbar gemacht.