Integrating visual and community environments in a motorcycle crash and casualty estimation.

Motorcycle crashes pose a serious problem because their probability of causing casualties is greater than that of passenger vehicle crashes. Therefore, accurately identifying the factors that influence motorcycle crashes is essential for enhancing traffic safety and public health. The aim of this study was to address three major research gaps: first, existing studies have relatively overlooked the built environment in relation to visual factors; second, existing crash prediction models have not fully reflected the differences in built environment characteristics between areas with frequent motorcycle crashes and areas with frequent casualties; and third, multidimensional analysis for variable selection is limited, and the interpretability of the models is insufficient.

Deep Learning and Geometry Flow Vector Using Estimating Vehicle Cuboid Technology in a Monovision Environment.

This study introduces a novel model for accurately estimating the cuboid of a road vehicle using a monovision sensor and road geometry information. By leveraging object detection models and core vectors, the proposed model overcomes the limitations of multi-sensor setups and provides a cost-effective solution. The model demonstrates promising results in accurately estimating cuboids by utilizing the magnitudes of core vectors and considering the average ratio of distances.

Vision-Based Pedestrian's Crossing Risky Behavior Extraction and Analysis for Intelligent Mobility Safety System.

Crosswalks present a major threat to pedestrians, but we lack dense behavioral data to investigate the risks they face. One of the breakthroughs is to analyze potential risky behaviors of the road users (e.g.

Analyzing vehicle-pedestrian interactions: Combining data cube structure and predictive collision risk estimation model.

Road traffic accidents are a severe threat to human lives, particularly to vulnerable road users (VRUs) such as pedestrians causing premature deaths. Therefore, it is necessary to devise systems to prevent accidents in advance and respond proactively, using potential risky situations as one of the surrogate safety measurements. This study introduces a new concept of a pedestrian safety system that combines the field and the centralized processes.

SafetyCube: Framework for potential pedestrian risk analysis using multi-dimensional OLAP.

In the past decade, the number of road traffic accidents and fatalities has remained about the same level. One of strategies to protect vulnerable road users (VRUs) is to analyze the factors that cause traffic accident and then to deploy safety facilities. However, most traffic safety systems currently in operation rely on historical data, which is post-facto approach.

Stress detection and classification of laying hens by sound analysis.

Stress adversely affects the wellbeing of commercial chickens, and comes with an economic cost to the industry that cannot be ignored. In this paper, we first develop an inexpensive and non-invasive, automatic online-monitoring prototype that uses sound data to notify producers of a stressful situation in a commercial poultry facility. The proposed system is structured hierarchically with three binary-classifier support vector machines.