The paper focuses on road safety assessment. The main objective was to assess the impact of different types and sizes of advertising devices as a potential distraction for drivers. Distraction of driver’s attention in real traffic was evaluated using the “Wiener Fahrprobe” structured observation method. As a method for reliable data collection, the eye tracking method was used to accurately define the time delay caused by the observation of the advertising device. As part of the assessment of the direct impact on drivers, test runs were carried out with 40 drivers on a pre-defined section of road on which different types of advertising devices were present. As an additional, supporting measurement, a vehicle simulator was also used. From the obtained knowledge it was possible to create a unique classification system that can be used to assess the severity of any installed advertising device in general. In the case of distraction, it was found that dynamic advertising devices attract the most attention than conventional static devices and appear to be a significant risk to road safety.

This study investigates the safety risks of Unmanned Aerial System (UAS) collisions with humans, focusing on impact dynamics through controlled crash testing. Utilizing a custom-designed drop mechanism, 49 impact tests were conducted on a Hybrid III anthropomorphic test device at various velocities and kinetic energies (7–24 m/s and 1–280 J). Tested UASs included a range of designs and weights (20 g to over 1 kg). Results demonstrated limitations in using kinetic energy models and peak head acceleration for injury prediction. The Head Injury Criterion showed greater consistency, reflecting the temporal profile of impacts. Findings revealed that UAS structural fragility reduces energy transfer at higher impacts through deformation and fracturing. This work underscores the need for improved testing protocols and nuanced safety standards.

The aim of this article is to improve road safety. Specifically, it deals with the development of a mathematical model that will more accurately define the severity of a defect in a road restraint system. Currently, that evaluation is based only on the subjective perception of individual safety auditors. The mathematical model was developed based on the principle of Bayesian statistics. The determination of the specific risk was made by comparing the results of the model for two datasets. In the first case, the model was based on accident data correlated with recorded defects in road restraint systems. In the second case, the dataset represented accident events with crash barriers where no defect was identified. Based on the comparison, a total of 64 risk combinations were identified. The mathematical model confirmed 26 combinations (41% of all selected combinations of the defect levels of the crash barriers). Although not even half of the identified combinations were confirmed, more than 90% of all correlated records are found in these exposures of confirmed combinations. The verification was able to clearly define the risk of safety defects and thus brings potential accuracy to subsequent decision-making related to the repair of road restraint systems.

The article focuses on the assessment and evaluation of the passability in densely populated parts of cities with multi-storey housing estates, in terms of the operation of the integrated rescue system (IRS) in the Czech Republic. The aim of the research is to minimize the arrival times to conduct the intervention as efficiently as possible. The presented problem is caused by unsystematic development of housing estates and the emergence of secondary problems in the form of inability to reach the place of intervention by the larger IRS vehicles. The vision presented in this document presents a systematic approach to improve the serviceability of individual blocks of flats. The main aim is to ensure passability, even for the largest equipment, such as fire engine ladders. Detailed mapping of the selected sites by drones, construction of their digital model, and subsequent virtual verification of the passability by specific vehicle models on identified access roads was performed. The results obtained by this procedure can then be implemented in the navigation of the fire safety forces and facilitate their arrival at the site of intervention. At the end, specific ways are presented in which the whole system can be modified to be able to intuitively change and choose individual access routes in real time, based on the current situation in the area.

This paper deals with evaluation of photogrammetric measurements of pavement macrotexture for the purposes of new non-contact method for determination of pavement skid resistance. The aim was to create a method that yields results comparable to existing pavement friction measurement techniques, particularly the sand method, and to assess whether the measurements obtained using the non-contact approach are equally effective. Additionally, the goal was to introduce and define limits for camera calibration applicable to similar cases of object measurements at close distances. Based on existing standards, specific pavement samples exhibiting various macrotexture qualities were selected. The authors conducted 23 different combinations of photogrammetric measurements based on their findings during the evaluations. A comparison of the applied procedures with a reference measurement using the sand method was made, and the measurement error rate was calculated. By evaluating the different variations of the road surface models created, it was concluded that the most reliable results for defining friction are obtained by interleaving the test specimen through a plane based on the principle of best fit using the four highest points of the macrotexture. The ideal size of the sub-areas for applying the mask is 7.5–10 mm. Photogrammetry has proven to be a suitable and accurate tool for assessing pavement surface texture and can be considered as a suitable alternative to other skid resistance measurement approaches.

This paper proposes, describes, and applies an algorithm for the automatic detection of selected elements of road infrastructure, along with the option to determine their spatial information. The principle is based on the evaluation of the color spectrum of the selected object on orthographic images. As a source image used for the processing, output from low-altitude aerial photogrammetry or terrestrial laser scanning can be used, together with the option to implement digital elevation models into the processing. The approach is based on the detection of the color composition of the selected element of the road, followed by clustering of the identified elements within the image and mathematical transformation of the clusters into a spatial vector form. Prior to the processing, the target objects are filtered out based on user input, for which vectorization is performed. The outputs are in the form of contours or the determined basic structure of the object. The main difference compared to existing methods is that the vectorization is only performed on the selected, pre-filtered parts of the raster image with identified target objects, not the whole image. This approach makes it possible to effectively and automatically identify and analyze, e.g., the edge of the road, road markings, or road features. This enables the subsequent implementation of the identified outputs into more complex spatial models of the road or its proximity. Additionally, the processing of the data to create a digital model of the environment can be automated, with a significant saving of time and related costs.