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Visualization of voxelization using sample point cloud data. (a) Example analysis area required for each observer pose; (b) voxelization of point cloud data.
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To prevent driver accidents in cities, local governments have established policies to limit city speeds and create child protection zones near schools. However, if the same policy is applied throughout a city, it can be difficult to obtain smooth traffic flows. A driver generally obtains visual information while driving, and this information is dir...
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Context 1
... the voxel resolution affects the processing time in this study, we determined 10 points and 0.5 m by investigating the point resolution in the collected data such that the mobile sensor resolution effectively captured spatial forms. Figure 5 shows a visualization of voxelization using sample point cloud data. The visibility analysis can be performed within the voxelized point cloud, which makes it possible to raycast objects such as trees that are hard to virtually describe. ...
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![Fig. 1. The consequences of traffic accident on a global scale [3]](publication/343061001/figure/fig1/AS:915180103942144@1595207348957/The-consequences-of-traffic-accident-on-a-global-scale-3_Q320.jpg)
![Fig. 2. Annual number of traffic accidents in Ukraine [4]](publication/343061001/figure/fig2/AS:915180108136448@1595207349003/Annual-number-of-traffic-accidents-in-Ukraine-4_Q320.jpg)
![Fig. 3. Annual number of traffic accidents in the European Union [5]](publication/343061001/figure/fig3/AS:915180108144641@1595207349041/Annual-number-of-traffic-accidents-in-the-European-Union-5_Q320.jpg)
The death and injuries of road users is one of the biggest problems that negatively affect the development of society and socio-economic progress. The price of human life is too high to neglect the least opportunity to save it. Therefore, the object of research is the huge amounts of information that modern society generates and which are known und...
Citations
... Finally, a region of interest (ROI) was defined based on the driver's viewpoint, and the image quality metrics were compared with conventional methods. Given that the study evaluates the driver's visual perception within a limited range of vision-similar to studies on geometric visual models [12]-the attentiveness of drivers in augmented reality visualization materials in HUD (head-up display) technology [13], or human visual characteristics in car-following models [14,15], the image quality evaluation focuses on the ROI area of the image. Therefore, the result of this study compares image feature metrics by cropping the image to the object-oriented ROI area. ...
Numerous studies are underway to enhance the identification of surroundings in nighttime environments. These studies explore methods such as utilizing infrared images to improve night image visibility or converting night images into day-like representations for enhanced visibility. This research presents a technique focused on converting the road conditions depicted in night images to resemble daytime scenes. To facilitate this, a paired dataset is created by augmenting limited day and night image data using CycleGAN. The model is trained using both original night images and single-scale luminance transform (SLAT) day images to enhance the level of detail in the converted daytime images. However, the generated daytime images may exhibit sharpness and noise issues. To address these concerns, an image processing approach, inspired by the Stevens effect and local blurring, which align with visual characteristics, is employed to reduce noise and enhance image details. Consequently, this study contributes to improving the visibility of night images by means of day image conversion and subsequent image processing. The proposed night-to-day image translation in this study has a processing time of 0.81 s, including image processing, which is less than one second. Therefore, it is considered valuable as a module for daytime image translation. Additionally, the image quality assessment metric, BRISQUE, yielded a score of 19.8, indicating better performance compared to conventional methods. The outcomes of this research hold potential applications in fields such as CCTV surveillance systems and self-driving cars.
... This "reasonable" design begins with multifaceted and multidisciplinary considerations that meet the needs of human vision and ergonomics and continues with humanized, detail-oriented, and simple-to-operate interface design, with User Experience Design (UED) and product icon design being the two most important aspects. We can only create excellent works if the two are perfectly integrated [8]. This paper proposes an interface design method and criteria for designers based on research and analysis of visual communication design of electronic equipment's human-computer interface. ...
Electronic equipment has high precision, high reliability, high stability, and high security, as well as the ability to adapt to a variety of challenging environments. With the rapid advancement of mechanization, automation, and electronization, the impact of human factors in production is growing, and designers are increasingly concerned about the problem of man-machine coordination. It is critical to have a man-machine interface that is suitable for operators’ thinking and behavior and has a guiding function. The accuracy and timeliness of equipment control are linked to the human-computer interaction interface. The visual characteristics of human cone cells are used to divide the neural visual perception intensity grades. With the visual communication index as the optimization goal, a mathematical model of human-computer interaction interface optimization is established and solved using a genetic algorithm. This method is used to optimize the design of a human-computer interaction interface, and the results show that the visual communication index of the optimized human-computer interaction interface has improved significantly.
... The area of human vision in which a certain amount of information can be collected through a brief look without moving the head is called the useful field of view (UFOV) (Choi, Byun, Kim, et al., 2020;Ringer, Throneburg, Walton, et al., 2015;Roenker, Cissell, Ball, et al., 2003;Sekuler, Bennett, and Mamelak, 2000). The term "field of view" is most often used in a situation in which the field of view of a human being is shaped/limited by technical devices (Jansen, Toet, and Delleman, 2010;Torrejon, Callaghan, and Hagras, 2013). ...
... The term "field of view" is most often used in a situation in which the field of view of a human being is shaped/limited by technical devices (Jansen, Toet, and Delleman, 2010;Torrejon, Callaghan, and Hagras, 2013). UFOV decreases with age and is also characterized by a decrease in the angle of view with increasing speed (Choi, Byun, Kim, et al., 2020;Ho, Scialfa, Caird, et al., 2001;Schall, Rusch, Lee, et al., 2013;Zhang, Dai, Pei, et al., 2016;Ward, Gaspar, Neider, et al., 2018). ...
... The study in [3] proposed a method for evaluating road safety by analyzing point cloud data collected by a mobile scanner. They proposed a novel geometric visual model to measure drivers' visual perception and analyze the corresponding information using the line-of-sight method. ...
For several decades, various sensors and sensing systems have been developed for smart cities and civil infrastructure systems [...].
The impact of roadside greenness on driving fatigue in real urban settings has been insufficiently investigated, presenting a critical knowledge gap for researchers, policymakers, professionals, and the public. In this onsite driving experiment, 34 urban residents completed seven driving tasks on different urban road routes in a randomized order with one-day intervals. A total of 238 tasks were conducted, each lasting an hour, assessing psychophysiological, visual, and muscular fatigue. A cardiovascular activity monitor (BioHarness) continuously measured the driver's heart rate, with lower rates indicating reduced psychophysiological fatigue. Visual and muscular fatigue were self-reported using a Visual Analog Scale questionnaire administered before, at the midpoint, and after completing the driving task. Deep transfer learning semantic segmentation analyzed road landscape characteristics and traffic conditions recorded from the drivers' view. Statistical analysis demonstrated that higher mean and variation in greenness significantly predicted lower psychophysiological fatigue after adjusting for multiple covariates. These results indicate that enhancing both the quantity and diversity of green landscapes along urban roads is vital for reducing driver's psychophysiological fatigue. This study reveals that roadside landscapes in urban settings are not trivial decorations, and they should be considered an essential component of transportation infrastructure.
