Michael Schrauf

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

OBJECTIVE The 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. METHODS An 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. RESULTS Statistical 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. CONCLUSIONS EEG alpha spindle parameters increase both fatigue detection sensitivity and specificity as compared to EEG alpha-band power. SIGNIFICANCE It is demonstrated that alpha spindles are superior to EEG band power measures for assessing driver fatigue under real traffic conditions.

Electrophysiology-based detection of emergency braking intention in real-world driving

OBJECTIVE The fact that all human action is preceded by brain processes partially observable through neuroimaging devices such as electroencephalography (EEG) is currently being explored in a number of applications. A recent study by Haufe et al (2011 J. Neural Eng. 8 056001) demonstrates the possibility of performing fast detection of forced emergency brakings during driving based on EEG and electromyography, and discusses the use of such neurotechnology for braking assistance systems. Since the study was conducted in a driving simulator, its significance regarding real-world applicability needs to be assessed. APPROACH Here, we replicate that experimental paradigm in a real car on a non-public test track. MAIN RESULTS Our results resemble those of the simulator study, both qualitatively (in terms of the neurophysiological phenomena observed and utilized) and quantitatively (in terms of the predictive improvement achievable using electrophysiology in addition to behavioral measures). Moreover, our findings are robust with respect to a temporary secondary auditory task mimicking verbal input from a fellow passenger. SIGNIFICANCE Our study serves as a real-world verification of the feasibility of electrophysiology-based detection of emergency braking intention as proposed in Haufe et al (2011 J. Neural Eng. 8 056001).

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.

A visual approach for scan path comparison

Several algorithms, approaches, and implementations have been developed to support comparison of scan paths and finding of interesting scan path structures. In this work we contribute a visual approach to support scan path comparison. A key feature of this approach is the combination of a clustering algorithm using Levenshtein distance with the parallel scan path visualization technique. The combination of computational methods with an interactive visualization allows us to use both the power of pattern finding algorithms and the human ability to visually recognize patterns. To use the concept in practice we implemented the approach in a prototype and show its application in two scan path analysis scenarios from automobile usability testing and visualization research.

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.

Can We Study Autonomous Driving Comfort in Moving-Base Driving Simulators? A Validation Study:

Objective: To lay the basis of studying autonomous driving comfort using driving simulators, we assessed the behavioral validity of two moving-base simulator configurations by contrasting them with a test-track setting. Background: With increasing level of automation, driving comfort becomes increasingly important. Simulators provide a safe environment to study perceived comfort in autonomous driving. To date, however, no studies were conducted in relation to comfort in autonomous driving to determine the extent to which results from simulator studies can be transferred to on-road driving conditions. Method: Participants (N = 72) experienced six differently parameterized lane-change and deceleration maneuvers and subsequently rated the comfort of each scenario. One group of participants experienced the maneuvers on a test-track setting, whereas two other groups experienced them in one of two moving-base simulator configurations. Results: We could demonstrate relative and absolute validity for one of the two simulator configurations. Subsequent analyses revealed that the validity of the simulator highly depends on the parameterization of the motion system. Conclusion: Moving-base simulation can be a useful research tool to study driving comfort in autonomous vehicles. However, our results point at a preference for subunity scaling factors for both lateral and longitudinal motion cues, which might be explained by an underestimation of speed in virtual environments. Application: In line with previous studies, we recommend lateral- and longitudinal-motion scaling factors of approximately 50% to 60% in order to obtain valid results for both active and passive driving tasks.

EEG alpha spindles as indicators for prolonged brake reaction time during auditory secondary tasks in a real road driving study

Driver distraction accounts for a substantial number of traffic accidents. Therefore, the impact of auditory secondary tasks on driving performance was examined. In addition to performance measures, i.e. reaction time on emergency brakings of a leading vehicle, mental driver states were described by electroencephalographic (EEG: alpha spindles, alpha band power) as well as cardiac activity (ECG: heart rate variability). Results show that brake reaction time (RT) increased with time-on-task during all conditions (p<.001), and was significantly higher while performing the secondary task (p<.001). Physiological measures showed similar effects. Alpha spindle rate, alpha band power as well as heart rate variability (HRV) increased with time-on-task and were significantly different during the secondary task, indicating inhibited visual information processing and reduced concentration ability. This study shows that reduced driving performance measured by means of prolonged brake reactions during increased cognitive load elicited by auditory secondary tasks is indicated by EEG measures as well as cardiac activity, enabling the direct quantification of driver distraction in experiments during real road driving.

Measuring driver’s mental workload using EEG

According to analyses of traffic accident data, 90 % of traffic accidents are caused by driving failures due to an impaired driver mental state [1, 2]. The development of optimal Advanced Driver Assistance Systems (ADAS) requires a profound understanding of the causes and factors that lead to an impaired driver mental state, such as fatigue, inattention or mental overload. However, the interaction of driver, vehicle and environment is extremely complex and the traditional analytical methods of behavioral psychology are often insufficient and difficult to assess. Using EEG, the Daimler AG has brought neurophysiological approaches from the laboratory into the vehicle and is able to perform real-world driving studies.

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.