In an age where all major manufacturers are trying to get a better understanding of when an emergency response should be triggered, it becomes imperative to learn how humans respond to emergency events. If one can understand driver behavior, systems can be designed around the user to either assist drivers where they fail to perform well or completely eliminate them from the accident avoidance maneuver. In this study, 169 crash and near crash events from the SHRP2 dataset were analyzed. The response behavior of drivers was measured in events where the through drivers’ path was intruded upon by another vehicle perpendicular to its path. Overall, drivers responded significantly faster when the other vehicle failed to stop, and at intersection locations.
As part of the Nuclear Regulatory Commission’s recertification of Texas A&M University’s AGN-201M nuclear reactor, a human factors analysis was performed to evaluate the drawbacks of the current system and make design recommendations for a new console layout. The process involved three phases. Background development consisted of a literature review and expert interviews (both structured and unstructured). Process analysis was performed using hierarchical task analysis, critical incident analysis, and heuristic usability walkthroughs. Control panel redesign utilized an expanded version of link analysis through adding modern social networking analysis techniques. While social network analysis has previously been used for design, particular emphasis in this paper is placed on the novel application of faction and centrality analysis to identify group categories for console redesign.
Crash statistics reveal that newly licensed teenage drivers experience a higher risk of crashing than more experienced drivers, particularly when turning left across the path of approaching traffic. Research has also demonstrated that novice drivers exhibit poor hazard mitigation skills. The current study assesses the effectiveness of a training program aimed at improving novice drivers’ hazard mitigation and speed selection behaviors as both the through driver and turning driver in left turn across path scenarios. In this study, novice drivers were randomly assigned to one of two training cohorts: anticipation-control-terminate (ACT) or placebo. Phase 1 of ACT is a video game where drivers must select where to look, where they would steer, and when they would slow when observing the approach to known fatal crash risk scenarios. Placebo training involved reaction time tests and street sign definitions. In phase 2 the ACT-trained participants were shown where their choices were similar to, or different than, those of drivers aged 26 through 61who had not had crashed in the previous 10 years. In phase 3, ACT-trained drivers were compared with placebo-trained drivers at left turn scenarios both as through driver and turning driver, using a driving simulator. ACT-trained drivers were more likely to exhibit anticipatory glances and slowing behaviors, and were significantly less likely to crash than were placebo-trained drivers. The results indicate that ACT was effective as a countermeasure for training novice drivers to select better speed management strategies in the simulated scenarios utilized in this research.
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