In-vehicle fragrance administration as a countermeasure for driver fatigue.

Driver fatigue is a contributing factor in about 10-30% of all fatal crashes. Prevention of fatigue-related crashes relies on robust detection of driver fatigue and application of effective countermeasures. A potential countermeasure is fragrance administration since odors can have alerting effects on humans. The aim here was to investigate if a fragrance incorporating trigeminal components could be used as an in-vehicle countermeasure for driver fatigue. The fragrance was tested in a driving simulator with 21 healthy but sleep-deprived participants. Each participant performed a monotonous driving task twice, once with active fragrance containing a trigeminal component and once with olfactory fragrance, in a cross-over single-blind design. The order of trigeminal/olfactory fragrance was randomized and blinded to the participants. Both fragrances (trigeminal/olfactory) were administered either when the participant fell asleep (defined as eye closure > 3 s) or after approximately 45 min if the participant did not fall asleep. Self-reported sleepiness was assessed using the Karolinska Sleepiness Scale (KSS) every 5 min during driving. Variability in speed and lateral position and line crossing frequency were logged for each drive to measure driving performance. Heart rate measurements (ECG) and eye blinks (EOG) were collected to investigate potential arousing effects of the fragrance and to track objective signs of sleepiness. Mean blink duration, which was used as an objective measure of sleepiness, decreased significantly, after fragrance exposure, as did the frequency of line crossings, but there were no statistically significant differences between the fragrance with trigeminal stimulus and the pure olfactory fragrance. The results are in line with the effects found for other commonly used fatigue countermeasures, like playing loud music. These countermeasures can restore alertness and driving performance for a short while. Whether this is sufficient to support driving performance until the driver can make a safe stop in real traffic remains a topic for future studies.

Let Complexity Bring Clarity: A Multidimensional Assessment of Cognitive Load Using Physiological Measures.

The effects of cognitive load on driver behavior and traffic safety are unclear and in need of further investigation. Reliable measures of cognitive load for use in research and, subsequently, in the development and implementation of driver monitoring systems are therefore sought. Physiological measures are of interest since they can provide continuous recordings of driver state. Currently, however, a few issues related to their use in this context are not usually taken into consideration, despite being well-known. First, cognitive load is a multidimensional construct consisting of many mental responses (cognitive load components) to added task demand. Yet, researchers treat it as unidimensional. Second, cognitive load does not occur in isolation; rather, it is part of a complex response to task demands in a specific operational setting. Third, physiological measures typically correlate with more than one mental state, limiting the inferences that can be made from them individually. We suggest that acknowledging these issues and studying multiple mental responses using multiple physiological measures and independent variables will lead to greatly improved measurability of cognitive load. To demonstrate the potential of this approach, we used data from a driving simulator study in which a number of physiological measures (heart rate, heart rate variability, breathing rate, skin conductance, pupil diameter, eye blink rate, eye blink duration, EEG alpha power, and EEG theta power) were analyzed. Participants performed a cognitively loading n-back task at two levels of difficulty while driving through three different traffic scenarios, each repeated four times. Cognitive load components and other coinciding mental responses were assessed by considering response patterns of multiple physiological measures in relation to multiple independent variables. With this approach, the construct validity of cognitive load is improved, which is important for interpreting results accurately. Also, the use of multiple measures and independent variables makes the measurements (when analyzed jointly) more diagnostic-that is, better able to distinguish between different cognitive load components. This in turn improves the overall external validity. With more detailed, diagnostic, and valid measures of cognitive load, the effects of cognitive load on traffic safety can be better understood, and hence possibly mitigated.

Automation Expectation Mismatch: Incorrect Prediction Despite Eyes on Threat and Hands on Wheel.

OBJECTIVE: The aim of this study was to understand how to secure driver supervision engagement and conflict intervention performance while using highly reliable (but not perfect) automation. BACKGROUND: Securing driver engagement-by mitigating irony of automation (i.e., the better the automation, the less attention drivers will pay to traffic and the system, and the less capable they will be to resume control) and by communicating system limitations to avoid mental model misconceptions-is a major challenge in the human factors literature. METHOD: One hundred six drivers participated in three test-track experiments in which we studied driver intervention response to conflicts after driving highly reliable but supervised automation. After 30 min, a conflict occurred wherein the lead vehicle cut out of lane to reveal a conflict object in the form of either a stationary car or a garbage bag. RESULTS: Supervision reminders effectively maintained drivers' eyes on path and hands on wheel. However, neither these reminders nor explicit instructions on system limitations and supervision responsibilities prevented 28% (21/76) of drivers from crashing with their eyes on the conflict object (car or bag). CONCLUSION: The results uncover the important role of expectation mismatches, showing that a key component of driver engagement is cognitive (understanding the need for action), rather than purely visual (looking at the threat), or having hands on wheel. APPLICATION: Automation needs to be designed either so that it does not rely on the driver or so that the driver unmistakably understands that it is an assistance system that needs an active driver to lead and share control.

Driver behavior in car-to-pedestrian incidents: An application of the Driving Reliability and Error Analysis Method (DREAM).

To develop relevant road safety countermeasures, it is necessary to first obtain an in-depth understanding of how and why safety-critical situations such as incidents, near-crashes, and crashes occur. Video-recordings from naturalistic driving studies provide detailed information on events and circumstances prior to such situations that is difficult to obtain from traditional crash investigations, at least when it comes to the observable driver behavior. This study analyzed causation in 90 video-recordings of car-to-pedestrian incidents captured by onboard cameras in a naturalistic driving study in Japan. The Driving Reliability and Error Analysis Method (DREAM) was modified and used to identify contributing factors and causation patterns in these incidents. Two main causation patterns were found. In intersections, drivers failed to recognize the presence of the conflict pedestrian due to visual obstructions and/or because their attention was allocated towards something other than the conflict pedestrian. In incidents away from intersections, this pattern reoccurred along with another pattern showing that pedestrians often behaved in unexpected ways. These patterns indicate that an interactive advanced driver assistance system (ADAS) able to redirect the driver's attention could have averted many of the intersection incidents, while autonomous systems may be needed away from intersections. Cooperative ADAS may be needed to address issues raised by visual obstructions.

Fatal intersection crashes in Norway: patterns in contributing factors and data collection challenges.

Fatal motor vehicle intersection crashes occurring in Norway in the years 2005-2007 were analyzed to identify causation patterns among their underlying contributing factors, and also to assess if the data collection and documentation procedures used by the Norwegian in-depth investigation teams produces the information necessary to do causation pattern analysis. 28 fatal accidents were analyzed. Causation charts of contributing factors were first coded for each driver in each crash using the Driving Reliability and Error Analysis Method (DREAM). Next, the charts were aggregated based on a combination of conflict types and whether the driver was going straight or turning. Analysis results indicate that drivers who were performing a turning maneuver in these crashes faced perception difficulties and unexpected behavior from the primary conflict vehicle, while at the same time trying to negotiate a demanding traffic situation. Drivers who were going straight on the other hand had less perception difficulties but largely expect any turning drivers to yield, which led to either slow reaction or no reaction at all. In terms of common contributing factors, those often pointed to in literature as contributing to fatal crashes, e.g. high speed, drugs and/or alcohol and inadequate driver training, contributed in 12 of 28 accidents. This confirms their prevalence, but also shows that most drivers end up in these situations due to combinations of less auspicious contributing factors. In terms of data collection and documentation, there was an asymmetry in terms of reported obstructions to view due to signposts and vegetation. These were frequently reported as contributing for turning drivers, but rarely reported as contributing for their counterparts in the same crashes. This probably reflects an involuntary focus of the analyst on identifying contributing factors for the driver held legally liable, while less attention is paid to the driver judged not at fault. Since who to blame often is irrelevant from a countermeasure development point of view, this underlying investigator approach needs to be addressed to avoid future bias in crash investigation reports.

Generalization of case studies in road traffic when defining pre-crash scenarios for active safety function evaluation.

To define pre-crash scenarios for evaluation of active safety functions, data from crash investigations is often used. Typical data sources include official databases with police reported crashes (macroscopic data) and in-depth case studies (microscopic data). Macroscopic data is often representative but has little detail on causation, while the opposite is true of microscopic data. Combining the sources by coupling causation information from a set of case studies to a macroscopic crash type would therefore seem ideal. For the coupling to be valid however, it must be verified that the selected case study set is representative of the crash type. The aim of this study is to describe and test a new methodology for such verification by means of an intermediate layer of representatively sampled crash information (questionnaire responses from crash involved drivers). The methodology was applied to intersection crashes. For the data sets used, the similarity in crash causation for case studies and questionnaire crashes, together with the context similarity for questionnaire crashes and the macroscopic crash type, was sufficient to argue that the case studies were representative of the crash type. While results must be considered preliminary given the limited data sets used, the proposed methodology shows promise for future work related to defining pre-crash scenarios for ADAS evaluation.