Search Results (4)

This paper presents a practical method for analyzing drivers’ eye movements, providing a valuable tool for understanding their behavior during driving simulations. The method, which utilizes an image processing technique, addresses the challenges when the driver’s attention is on points without information about the image depth. The screen image changes or moves with the simulation. It allows us to identify the gaze position relative to the road, determining whether the glance is inside or outside. This is achieved by transforming RGB images (frames) collected by the eye-tracker video camera into a b/w image using the Canny filter. This filter can identify objects’ contours by evaluating the change in color of their surfaces. A window is then applied to these new images to extract information about the gaze position in the real world. Four drivers were used as a sample for the method’s testing. The findings demonstrate various driver variations and a disparity between driving in curved and rectilinear segments. The gaze is typically inside the road in curved sections, whereas in rectilinear sections, the gaze is frequently outside. Full article

One way to reduce road crashes is to determine the main influential factors among a long list that are attributable to driver behavior, environmental conditions, vehicle features, road type, and traffic signs. Hence, selecting the best modelling tool for extracting the relations between crash factors and their outcomes is a crucial task. To analyze the road crash data of Milan City, Italy, gathered between 2014–2017, this study used artificial neural networks (ANNs), generalized linear mixed-effects (GLME), multinomial regression (MNR), and general nonlinear regression (NLM), as the modelling tools. The data set contained 35,182 records of road crashes with injuries or fatalities. The findings showed that unbalanced and incomplete data sets had an impact on outcome performance, and data treatment methods could help overcome this problem. Age and gender were the most influential recurrent factors in crashes. Additionally, ANNs demonstrated a superior capability to approximate complicated relationships between an input and output better than the other regression models. However, they cannot provide an analytical formulation, but can be used as a baseline for other regression models. Due to this, GLME and MNR were utilized to gather information regarding the analytical framework of the model, that aimed to construct a particular NLM. Full article

The driving behaviour of Connected and Automated Vehicles (CAVs) may influence the final acceptance of this technology. Developing a driving style suitable for most people implies the evaluation of alternatives that must be validated. Intelligent Virtual Drivers (IVDs), whose behaviour is controlled by a program, can test different driving styles along a specific route. However, multiple combinations of IVD settings may lead to similar outcomes due to their high variability. The paper proposes a method to identify the IVD settings that can be used as a reference for a given route. The method is based on the cluster analysis of vehicular data produced by a group of IVDs with different settings driving along a virtual road scenario. Vehicular data are clustered to find IVDs representing a driving style to classify human drivers who previously drove on the same route with a driving simulator. The classification is based on the distances between the different vehicular signals calculated for the IVD and recorded for human drivers. The paper includes a case study showing the practical use of the method applied on an actual road circuit. The case study demonstrated that the proposed method allowed identifying three IVDs, among 29 simulated, which have been subsequently used as a reference to cluster 26 human driving styles. These representative IVDs, which ideally replicate the driving style of human drivers, can be used to support the development of CAVs control logic that better fits human expectations. A closing discussion about the flexibility of the method in terms of the different natures of data collection, allowed for depicting future applications and perspectives. Full article

An obvious sign of ageing is the loss of hair colour due to a decrease or lack of melanin in hair fibres. An examination of the lipid levels and structure of grey hair determined using µ–FTIR revealed a high correlation between the characteristics of lipids located in the cuticle and the water dynamics of the fibres. Therefore, a deep study based on external and internal lipid extraction, an analysis using thin layer chromatography coupled to an automated flame ionisation detector, calorimetric analyses and the physico-chemical evaluation of the delipidated fibres were performed. Hairs were evaluated to identify changes in the organisation of these lipids using Fourier transform infrared spectroscopy and their effect on the water dynamics of the fibres. The primary differences observed for the lipid extracts from white hair compared to brown hair were the lower amount of the internal lipids extracted, which were primarily composed of free fatty acids (FFAs) and ceramides, with a higher content of lower phase transition peaks, indicating increased unsaturated compounds that promote higher fluidity of the lipid bilayers. The virgin white fibres exhibited lower levels of embedded water, with lower binding energies and higher water diffusion, indicating higher permeability. The IR study confirmed the low lipid levels and the greater disorder of white hair. These results may be of interest for cosmetic treatments to which patients with grey hair may be subjected. Full article