United Services Automobile Association

The goal of this study is to clarify the usefulness of deep learning methods for pedestrian collision detection using dashcam videos for advanced automatic collision notification, focusing on pedestrians, as they make up the highest number of traffic fatalities in Japan. First, we created a dataset for deep learning from dashcam videos. A total of 78 dashcam videos of pedestrian-to-automobile accidents were collected from a video hosting website and from the Japan Automobile Research Institute (JARI). Individual frames were selected from the video data amounting to a total of 1,212 still images, which were added to our dataset with class and location information. This dataset was then divided to create training, validation, and test datasets. Next, deep learning was performed based on the training dataset to learn the features of pedestrian collision images, which are images that capture pedestrian behavior at the time of the collision. Pedestrian collision detection performance of the trained model was evaluated as the percentage of correct predictions of pedestrian collisions in image data according to varied test sets with different combinations of characteristics. Our results for the proposed method show high-precision collision detection for daytime, clear pedestrian wrap trajectory accident data, including accurate detection of pedestrian collision location information. However, nighttime, unclear accident data resulted in false detection or no detection. Reduction of exposure value and resolution was confirmed to reduce detection rate. The results of the present study suggest the possibility of pedestrian collision detection by deep learning using dashcam videos.

The vast majority of change initiatives fail to meet their objectives and most decimate their organization’s levels of engagement in the process. The effect of plummeting employee engagement during turbulent times creates a downward spiral that can result in permanent damage to the organizational culture and capabilities. This phenomenon has led some to believe that change can only be achieved at the cost of employee engagement and that engagement can only be improved during periods of stability. Our work suggests that this is a false dichotomy. Through careful planning and active management, some organizations utilize these times of change to deploy strength-based, positive approaches to successfully deliver their change agenda while simultaneously cultivating greater work meaningfulness and engagement. In this chapter, we examine a case study that demonstrates, through the use of Appreciative Inquiry (AI) as one such approach, how taking on aggressive change initiatives in this manner can be leveraged as an opportunity for widescale reinvention of the organization, enabling greater work meaningfulness, engagement, and flourishing.KeywordsOrganizational changeAppreciative inquiryWork meaningfulnessInspirationWorkforce engagement

BackgroundNew automobiles provide a variety of features that allow motorists to perform a plethora of secondary tasks unrelated to the primary task of driving. Despite their ubiquity, surprisingly little is known about how these complex multimodal in-vehicle information systems (IVIS) interactions impact a driver’s workload.ResultsThe current research sought to address three interrelated questions concerning this knowledge gap: (1) Are some task types more impairing than others? (2) Are some modes of interaction more distracting than others? (3) Are IVIS interactions easier to perform in some vehicles than others? Depending on the availability of the IVIS features in each vehicle, our testing involved an assessment of up to four task types (audio entertainment, calling and dialing, text messaging, and navigation) and up to three modes of interaction (e.g., center stack, auditory vocal, and the center console). The data collected from each participant provided a measure of cognitive demand, a measure of visual/manual demand, a subjective workload measure, and a measure of the time it took to complete the different tasks. The research provides empirical evidence that the workload experienced by drivers systematically varied as a function of the different tasks, modes of interaction, and vehicles that we evaluated.ConclusionsThis objective assessment suggests that many of these IVIS features are too distracting to be enabled while the vehicle is in motion. Greater consideration should be given to what interactions should be available to the driver when the vehicle is in motion rather than to what IVIS features and functions could be available to motorists.

dermatitis (ACD), 252, 529, 531, 532, 538-40, 563 allergic fungal sinusitis (AFS), 428, 429 allergic respiratory disease, 521 allergy laboratory animals, 526, 527 testing, 255 upper and lower respiratory allergy, 252 alpha-2 adrenoreceptors, 66 Alternaria, 425-7 altitude, atmospheric pressure and oxygen levels, 133 altitude-pressure-temperature relationships, 131,

This paper documents the successful use of small unmanned aerial systems (SUAS) for two floods in Fort Bend County, Texas, and identifies gaps in informatics, manpower, human-robot interaction, and cost-benefit analysis. The case studies focus on how emergency managers can use SUAS for flood assessment including flood mapping and projection of impact, verification of flood inundation models, providing justification for publicly accountable decisions, and public information. The case studies are particularly informative because they were flown at the direction of County experts during two actual flood events and represent 21 flights over four days.

This book grew out of notes for seminars and courses on reproducing kernel Hilbert spaces taught at the University of Houston beginning in the 1990s.

ABSTRACTChina’s split-share structure reform in 2005–2006 mitigates agency conflicts between controlling shareholders and minority shareholders and thus may bring substantial changes to corporate financing behaviour. This article examines the impact of that reform on the capital structure decisions of firms by applying a variety of trade-off and pecking-order models. Using data from 1176 non-financial Chinese listed firms during the period 2000–2012, we present empirical evidence indicating that equity tracks the financing deficit better than debt in Chinese firms, a finding which is not consistent with pecking-order theory. This phenomenon is more prominent after 2006 as share reform increases trading activity in the secondary stock market and improves the transparency of financial markets. In addition, Chinese firms have an optimal leverage ratio and they adjust below-target leverage ratios faster than above-target leverage ratios after the implementation of share structure reform, although they make symmetric adjustments towards the target leverage ratio before 2007. Finally, recent share reform has prompted Chinese firms to more quickly address the divergence of actual leverage ratios from long-term target levels, but has slowed their response to short-term target leverage divergence.

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