The article outlines various approaches to developing a fuzzy decision algorithm designed for monitoring and issuing warnings about driver drowsiness. This algorithm is based on analyzing EOG (electrooculography) signals and eye state images with the aim of preventing accidents. The drowsiness warning system comprises key components that learn about, analyze and make decisions regarding the driver’s alertness status. The outcomes of this analysis can then trigger warnings if the driver is identified as being in a drowsy state. Driver drowsiness is characterized by a gradual decline in attention to the road and traffic, diminishing driving skills and an increase in reaction time, all contributing to a higher risk of accidents. In cases where the driver does not respond to the warnings, the ADAS (advanced driver assistance systems) system should intervene, assuming control of the vehicle’s commands.

Compared to conventional signalised intersections, roundabouts are increasingly recognised for their ability to improve traffic safety and operational efficiency. However, accurately modelling their complex traffic dynamics remains challenging, particularly in multilane configurations characterised by lane-changing manoeuvres and gap-acceptance interactions. This study presents a behaviour-driven microscopic simulation framework based on agent-based modelling (ABM) for evaluating roundabout performance under varying geometric and traffic demand conditions. In the proposed framework, each vehicle is represented as an autonomous agent capable of route selection, yielding, lane-changing, and speed adaptation according to predefined behavioural rules. This enables a detailed representation of local traffic interactions and operational conflicts that are not fully captured by traditional aggregate traffic models. The simulation environment is used to analyse idealised one-, two-, and three-lane roundabout configurations and to assess the operational impact of targeted geometric modifications. The proposed methodology is further validated using real-world traffic data collected from the Brașov Central Roundabout, Romania. Simulation results demonstrate that the ABM framework can realistically reproduce traffic throughput, average speed, number of stops, and travel time under high traffic demand conditions. Furthermore, the introduction of a channelised right-turn lane resulted in measurable operational improvements, including increased average speed and reduced delay. The findings highlight the applicability of agent-based simulation as a decision-support tool for roundabout design, traffic management, and infrastructure optimisation, contributing to safer and more efficient urban mobility systems.

Pedestrian head injuries represent one of the most severe consequences of vehicle–pedestrian collisions, primarily resulting from the impact between the pedestrian head and the vehicle hood or windshield region. Active bonnet systems have been developed to mitigate injury severity by increasing the clearance between the hood and rigid engine components during impact; however, their effectiveness strongly depends on rapid impact detection and timely deployment. This study proposes an approach for analysing pedestrian impact dynamics and evaluating the design performance of a contact-triggered active bonnet system using MATLAB/Simulink and Simscape Multibody. The pedestrian–vehicle interaction is reproduced using spatial contact force models, while the bonnet deployment is initiated through a contact-based trigger mechanism coupled with a simplified actuator integrated into the hood hinge. The model was experimentally validated using controlled pedestrian impact tests at 17 km/h and 29 km/h, including frame-by-frame kinematic analysis and measured head acceleration signals. Additional simulations were performed at multiple vehicle velocities with and without bonnet deployment to evaluate the influence of impact configuration and pedestrian trajectory on head injury severity. The results indicate that the active bonnet system can reduce peak head acceleration and HIC15 values under several investigated impact conditions. The comparative simulations further show that the protective effect is influenced by pedestrian positioning and head trajectory relative to the vehicle front-end geometry, with higher effectiveness when the head impact remains within the deformable bonnet region.

Trailers and semi-trailers for road freight operate under extreme dynamic loads that cause fatigue damage, deformation and structural failure. This review synthesises the literature on the structural optimisation of truck, trailer and semi-trailer structures under such loads, based on a targeted, iterative search of two databases and three publisher platforms, extended by citation chaining, that yielded 68 primary studies. The studies are grouped into three method families (topology, parametric and artificial intelligence (AI)-assisted optimisation) and compared by outcome, type of redesign and validation level. Same-material optimisations of complete frames and components save about 7–16% of mass, with or without tests; the largest values, 28% for steel longitudinal beams and a theoretical 67% for a fully composite trailer, come from beam shape optimisation and composite substitution in one numerical study. A coverage map of 56 scored studies shows that more than half do not consider time-varying loads, that fatigue, buckling and fail-safe criteria are rarely optimised together, and that optimised designs are seldom tested, while advanced formulations are demonstrated mostly on generic structures. The review proposes minimum safeguards for AI-assisted design, discusses manufacturability and economic feasibility, and outlines an integrated workflow linking measured load spectra, multi-criteria optimisation and staged experimental validation.