Suchergebnisse

Objective: This study applies text mining to extract clusters of vehicle problems and associated trends from free-response data in the National Highway Traffic Safety Administration's vehicle owner's complaint database. Background: As the automotive industry adopts new technologies, it is important to systematically assess the effect of these changes on traffic safety. Driving simulators, naturalistic driving data, and crash databases all contribute to a better understanding of how drivers respond to changing vehicle technology, but other approaches, such as automated analysis of incident reports, are needed. Method: Free-response data from incidents representing two severity levels (fatal incidents and incidents involving injury) were analyzed using a text mining approach: latent semantic analysis (LSA). LSA and hierarchical clustering identified clusters of complaints for each severity level, which were compared and analyzed across time. Results: Cluster analysis identified eight clusters of fatal incidents and six clusters of incidents involving injury. Comparisons showed that although the airbag clusters across the two severity levels have the same most frequent terms, the circumstances around the incidents differ. The time trends show clear increases in complaints surrounding the Ford/Firestone tire recall and the Toyota unintended acceleration recall. Increases in complaints may be partially driven by these recall announcements and the associated media attention. Conclusion: Text mining can reveal useful information from free-response databases that would otherwise be prohibitively time-consuming and difficult to summarize manually. Application: Text mining can extend human analysis capabilities for large free-response databases to support earlier detection of problems and more timely safety interventions.

Objectives: An experiment was conducted to assess the effects of distraction mitigation strategies on drivers? performance and productivity while engaged in an in-vehicle information system task. Background: Previous studies show that in-vehicle tasks undermine driver safety and there is a need to mitigate driver distraction. Method: An advising strategy that alerts drivers to potential dangers and a locking strategy that prevents the driver from continuing the distracting task were presented to 16 middle-aged and 12 older drivers in a driving simulator in two modes (auditory, visual) and two road conditions (curves, braking events). Results: Distraction was a problem for both age groups. Visual distractions were more detrimental than auditory ones for curve negotiation, as depicted by more erratic steering, F(6, 155) = 26.76, p < .05. Drivers did brake more abruptly under auditory distractions, but this effect was mitigated by both the advising, t(155) = 8.37, p < .05, and locking strategies, t(155) = 8.49, p < .05. The locking strategy also resulted in longer minimum time to collision for middle-aged drivers engaged in visual distractions, F(6, 138) = 2.43, p < .05. Conclusions: Adaptive interfaces can reduce abrupt braking on curve entries resulting from auditory distractions and can also improve the braking response for distracted drivers. Application: These strategies can be incorporated into existing in-vehicle systems, thus mitigating the effects of distraction and improving driver performance.

Objective: This study investigated the effect of a nondriving cognitively loading task on the relationship between drivers' endogenous and exogenous control of attention. Background: Previous studies have shown that cognitive load leads to a withdrawal of attention from the forward scene and a narrowed field of view, which impairs hazard detection. Method: Posner's cue-target paradigm was modified to study how endogenous and exogenous cues interact with cognitive load to influence drivers' attention in a complex dynamic situation. In a driving simulator, pedestrian crossing signs that predicted the spatial location of pedestrians acted as endogenous cues. To impose cognitive load on drivers, we had them perform an auditory task that simulated the demands of emerging in-vehicle technology. Irrelevant exogenous cues were added to half of the experimental drives by including scene clutter. Results: The validity of endogenous cues influenced how drivers scanned for pedestrian targets. Cognitive load delayed drivers' responses, and scene clutter reduced drivers' fixation durations to pedestrians. Cognitive load diminished the influence of exogenous cues to attract attention to irrelevant areas, and drivers were more affected by scene clutter when the endogenous cues were invalid. Conclusion: Cognitive load suppresses interference from irrelevant exogenous cues and delays endogenous orienting of attention in driving. Application: The complexity of everyday tasks, such as driving, is better captured experimentally in paradigms that represent the interactive nature of attention and processing load.

Objective: This study investigates the effect of cognitive load on guidance of visual attention. Background: Previous studies have shown that cognitive load can undermine driving performance, particularly drivers' ability to detect safety-critical events. Cognitive load combined with the loss of exogenous cues, which can occur when the driver briefly glances away from the roadway, may be particularly detrimental. Method: In each of two experiments, twelve participants engaged in an auditory task while performing a change detection task. A change blindness paradigm was implemented to mask exogenous cues by periodically blanking the screen in a driving simulator while a change occurred. Performance measures included participants' sensitivity to vehicle changes and confidence in detecting them. Results: Cognitive load uniformly diminished participants' sensitivity and confidence, independent of safety relevance or lack of exogenous cues. Periodic blanking, which simulated glances away from the roadway, undermined change detection to a greater degree than did cognitive load; however, drivers' confidence in their ability to detect changes was diminished more by cognitive load than by periodic blanking. Conclusion: Cognitive load and short glances away from the road are additive in their tendency to increase the likelihood of drivers missing safety-critical events. Application: This study demonstrates the need to consider the combined consequence of cognitive load and brief glances away from the road in the design of emerging in-vehicle devices and the need to provide drivers with better feedback regarding these consequences.

Objective: This study assesses the influence of the auditory characteristics of alerts on perceived urgency and annoyance and whether these perceptions depend on the context in which the alert is received. Background: Alert parameters systematically affect perceived urgency, and mapping the urgency of a situation to the perceived urgency of an alert is a useful design consideration. Annoyance associated with environmental noise has been thoroughly studied, but little research has addressed whether alert parameters differentially affect annoyance and urgency. Method: Three 23 × 3 mixed within/between factorial experiments, with a total of 72 participants, investigated nine alert parameters in three driving contexts. These parameters were formant (similar to harmonic series), pulse duration, interpulse interval, alert onset and offset, burst duty cycle, alert duty cycle, interburst period, and sound type. Imagined collision warning, navigation alert, and E-mail notification scenarios defined the driving context. Results: All parameters influenced both perceived urgency and annoyance ( p < .05), with pulse duration, interpulse interval, alert duty cycle, and sound type influencing urgency substantially more than annoyance. There was strong relationship between perceived urgency and rated appropriateness for high-urgency driving scenarios and a strong relationship between annoyance and rated appropriateness for low-urgency driving scenarios. Conclusion: Sound parameters differentially affect annoyance and urgency. Also, urgency and annoyance differentially affect perceived appropriateness of warnings. Application: Annoyance may merit as much attention as urgency in the design of auditory warnings, particularly in systems that alert drivers to relatively low-urgency situations.

Bibliometric analyses use the citation history of scientific articles as data to measure scientific impact. This paper describes a bibliometric analysis of the 1682 papers and 2413 authors published in Human Factors from 1970 to 2000. The results show that Human Factors has substantial relative scientific influence, as measured by impact, immediacy, and half-life, exceeding the influence of comparable journals. Like other scientific disciplines, human factors research is a highly stratified activity. Most authors have published only one paper, and many papers are cited infrequently, if ever. A small number of authors account for a disproportionately large number of the papers published and citations received. However, the degree of stratification is not as extreme as in many other disciplines, possibly reflecting the diversity of the human factors discipline. A consistent trend of more authors per paper parallels a similar trend in other fields and may reflect the increasingly interdisciplinary nature of human factors research and a trend toward addressing human-technology interaction in more complex systems. Ten of the most influential papers from each of the last 3 decades illustrate trends in human factors research. Actual or potential applications of this research include considerations for the publication and distribution policy of Human Factors.

Collision warning systems offer a promising approach to mitigate rear-end collisions, but substantial uncertainty exists regarding the joint performance of the driver and the collision warning algorithms. A simple deterministic model of driver performance was used to examine kinematics-based and perceptual-based rear-end collision avoidance algorithms over a range of collision situations, algorithm parameters, and assumptions regarding driver performance. The results show that the assumptions concerning driver reaction times have important consequences for algorithm performance, with underestimates dramatically undermining the safety benefit of the warning. Additionally, under some circumstances, when drivers rely on the warning algorithms, larger headways can result in more severe collisions. This reflects the nonlinear interaction among the collision situation, the algorithm, and driver response that should not be attributed to the complexities of driver behavior but to the kinematics of the situation. Comparisons made with experimental data demonstrate that a simple human performance model can capture important elements of system performance and complement expensive human-in-the-loop experiments. Actual or potential applications of this research include selection of an appropriate algorithm, more accurate specification of algorithm parameters, and guidance for future experiments.

Objective This study examines how driving styles of fully automated vehicles affect drivers' trust using a statistical technique—the two-part mixed model—that considers the frequency and magnitude of drivers' interventions. Background Adoption of fully automated vehicles depends on how people accept and trust them, and the vehicle's driving style might have an important influence. Method A driving simulator experiment exposed participants to a fully automated vehicle with three driving styles (aggressive, moderate, and conservative) across four intersection types (with and without a stop sign and with and without crossing path traffic). Drivers indicated their dissatisfaction with the automation by depressing the brake or accelerator pedals. A two-part mixed model examined how automation style, intersection type, and the distance between the automation's driving style and the person's driving style affected the frequency and magnitude of their pedal depression. Results The conservative automated driving style increased the frequency and magnitude of accelerator pedal inputs; conversely, the aggressive style increased the frequency and magnitude of brake pedal inputs. The two-part mixed model showed a similar pattern for the factors influencing driver response, but the distance between driving styles affected how often the brake pedal was pressed, but it had little effect on how much it was pressed. Conclusion Eliciting brake and accelerator pedal responses provides a temporally precise indicator of drivers' trust of automated driving styles, and the two-part model considers both the discrete and continuous characteristics of this indicator. Application We offer a measure and method for assessing driving styles.

As computer applications for cars emerge, a speech-based interface offers an appealing alternative to the visually demanding direct manipulation interface. However, speech-based systems may pose cognitive demands that could undermine driving safety. This study used a car-following task to evaluate how a speechbased e-mail system affects drivers' response to the periodic braking of a lead vehicle. The study included 24 drivers between the ages of 18 and 24 years. A baseline condition with no e-mail system was compared with a simple and a complex e-mail system in both simple and complex driving environments. The results show a 30% (310 ms) increase in reaction time when the speech-based system is used. Subjective workload ratings and probe questions also indicate that speechbased interaction introduces a significant cognitive load, which was highest for the complex e-mail system. These data show that a speech-based interface is not a panacea that eliminates the potential distraction of in-vehicle computers. Actual or potential applications of this research include design of in-vehicle information systems and evaluation of their contributions to driver distraction.

It is estimated that, in the United States, around 20 percent of all police-reported road crashes involve driver distraction as a contributing factor. This figure increases if other forms of inattention are considered. Evidence (reviewed in this volume) suggests that the situation is similar in other countries and that driver distraction and inattention are even more dangerous as contributing factors in crashes than drug and alcohol intoxication. Having a solid evidence-base from which to develop injury countermeasures is a cornerstone of road-safety management. This book adds to the accumulating evidence-base on driver distraction and inattention. With 24 chapters by 52 authors from more than 10 countries, it provides important new perspectives on the definition and meaning of driver distraction and inattention, the mechanisms that characterize them, the measurement of their effects, strategies for mitigating their effects, and recommendations for further research. The goal of this book is to inspire further research and countermeasure development to prevent and mitigate the potentially adverse effects of driver distraction and driver inattention, and, in doing so, to save lives.

Objective The objective of this study was to estimate trust from conversations using both lexical and acoustic data. Background As NASA moves to long-duration space exploration operations, the increasing need for cooperation between humans and virtual agents requires real-time trust estimation by virtual agents. Measuring trust through conversation is a novel and unintrusive approach. Method A 2 (reliability) × 2 (cycles) × 3 (events) within-subject study with habitat system maintenance was designed to elicit various levels of trust in a conversational agent. Participants had trust-related conversations with the conversational agent at the end of each decision-making task. To estimate trust, subjective trust ratings were predicted using machine learning models trained on three types of conversational features (i.e., lexical, acoustic, and combined). After training, model explanation was performed using variable importance and partial dependence plots. Results Results showed that a random forest algorithm, trained using the combined lexical and acoustic features, predicted trust in the conversational agent most accurately [Formula: see text]. The most important predictors were a combination of lexical and acoustic cues: average sentiment considering valence shifters, the mean of formants, and Mel-frequency cepstral coefficients (MFCC). These conversational features were identified as partial mediators predicting people's trust. Conclusion Precise trust estimation from conversation requires lexical cues and acoustic cues. Application These results showed the possibility of using conversational data to measure trust, and potentially other dynamic mental states, unobtrusively and dynamically.

Objective: The aim of this study was to design and evaluate an algorithm for detecting drowsiness-related lane departures by applying a random forest classifier to steering wheel angle data. Background: Although algorithms exist to detect and mitigate driver drowsiness, the high rate of false alarms and missed detection of drowsiness represent persistent challenges. Current algorithms use a variety of data sources, definitions of drowsiness, and machine learning approaches to detect drowsiness. Method: We develop a new approach for detecting drowsiness-related lane departures using steering wheel angle data that employ an ensemble definition of drowsiness and a random forest algorithm. Data collected from 72 participants driving the National Advanced Driving Simulator are used to train and evaluate the model. The model's performance was assessed relative to a commonly used algorithm, percentage eye closure (PERCLOS). Results: The random forest steering algorithm had a higher classification accuracy and area under the receiver operating characteristic curve than PERCLOS and had comparable positive predictive value. The algorithm succeeds at identifying two key scenarios associated with the drowsiness detection task. These two scenarios consist of instances when drivers depart their lane because they fail to modulate their steering behavior according to the demands of the simulated road and instances when drivers correctly modulate their steering behavior according to the demands of the road. Conclusion: The random forest steering algorithm is a promising approach to detect driver drowsiness. The algorithm's ties to consequences of drowsy driving suggest that it can be easily paired with mitigation systems.

Objective: The aim of this study was to assess how scrolling through playlists on an MP3 player or its aftermarket controller affects driving performance and to examine how drivers adapt device use to driving demands. Background: Drivers use increasingly complex infotainment devices that can undermine driving performance. The goal activation hypothesis suggests that drivers might fail to compensate for these demands, particularly with long tasks and large search set sizes. Method: A total of 50 participants searched for songs in playlists of varying lengths using either an MP3 player or an aftermarket controller while negotiating road segments with traffic and construction in a medium-fidelity driving simulator. Results: Searching through long playlists (580 songs) resulted in poor driving performance and required more long glances (longer than 2 s) to the device compared with other playlist lengths. The aftermarket controller also led to more long glances compared with the MP3 player. Drivers did not adequately adapt their behavior to roadway demand, as evident in their degraded driving performance. No significant performance differences were found between short playlists, the radio-tuning task, and the no-task condition. Conclusion: Selecting songs from long playlists undermined driving performance, and drivers did not sufficiently adapt their use of the device to the roadway demands, consistent with the goal activation hypothesis. The aftermarket controller degraded rather than enhanced performance. Application: Infotainment systems should support drivers in managing distraction. Aftermarket controllers can have the unintended effect of making devices carried into the car less compatible with driving. These results can motivate development of new interfaces as alternatives to scrolling lists.

Rear-end collisions account for almost 30% of automotive crashes. Rear-end collision avoidance systems (RECASs) may offer a promising approach to help drivers avoid these crashes. Two experiments performed using a high-fidelity motion-based driving simulator examined driver responses to evaluate the efficacy of a RECAS. The first experiment showed that early warnings helped distracted drivers react more quickly---and thereby avoid more collisions---than did late warnings or no warnings. Compared with the no-warning condition, an early RECAS warning reduced the number of collisions by 80.7%. Assuming collision severity is proportional to kinetic energy, the early warning reduced collision severity by 96.5%. In contrast, the late warning reduced collisions by 50.0 % and the corresponding severity by 87.5%. The second experiment showed that RECAS benefits even undistracted drivers. Analysis of the braking process showed that warnings provide a potential safety benefit by reducing the time required for drivers to release the accelerator. Warnings do not, however, speed application of the brake, increase maximum deceleration, or affect mean deceleration. These results provide the basis for a computational model of driver performance that was used to extrapolate the findings and identify the most promising parameter settings. Potential applications of these results include methods for evaluating collision warning systems, algorithm design guidance, and driver performance model input.