Search Results (63)

This study quantifies the effect of air and fuel filter restriction on fuel consumption, regulated pollutants (CO and HC), and CO₂ greenhouse gas emissions under real driving conditions in a hilly high-altitude environment. Four filter configurations were evaluated: clean air filter–clean fuel filter (CAF–CFF, reference), dirty air filter–clean fuel filter (DAF–CFF), clean air filter–dirty fuel filter (CAF–DFF), and dirty air filter–dirty fuel filter (DAF–DFF). Each test was repeated three times over the same RDE route in Quito (≈2100–2900 m). Fuel consumption was estimated from ECU-based signals, and CO₂ emission factors and regulated pollutant (CO and HC) emission factors were computed from measured exhaust concentrations and distance normalization. Results were analyzed by RDE section (urban, rural, motorway) and expressed as percent changes relative to the reference configuration to directly isolate filter restriction effects. Relative to CAF–CFF, DAF–CFF produced the largest increase in average fuel consumption (+7.2%) and the largest urban CO₂ penalty (+22.7%), indicating a strong efficiency sensitivity to intake restriction under transient operation. CAF–DFF increased average fuel consumption by 6% and produced the strongest motorway penalties for CO (+77.3%) and HC (+44.4%), suggesting that fuel delivery restriction has a stronger influence on incomplete oxidation products under sustained higher load. The combined restriction (DAF–DFF) showed non-additive responses depending on the operating regime. Random Forest models were trained to estimate CO₂, CO, and HC, achieving R² values of 0.8571, 0.8229, and 0.7690, respectively, while multiple linear regression achieved an R² of 0.852 for fuel consumption. The proposed approach supports data-driven monitoring of filter restriction effects under real driving operation, while acknowledging that fuel consumption and CO₂ are obtained through different measurement and conversion paths and may not yield identical percent changes. Full article

The article presents the development and results of emission studies conducted in Poland in the context of global real-driving emissions research. Although the European Union has continuously tightened exhaust-emission standards, road transport remains one of the major sources of air pollution. Several research centers in Poland—including Rzeszów University of Technology, Poznan University of Technology, and the Motor Transport Institute—have been conducting on-road emission measurements for many years across a wide spectrum of vehicles: conventional, hybrid (including plug-in hybrids), and fully electric. The findings show that emissions under real-world driving conditions often differ from those obtained in homologation tests, particularly for nitrogen oxides and particulate matter. Ambient temperature, road gradient, and driving phases (urban, rural, motorway) were also identified as influential factors. Polish research centers have developed analytical tools enabling comparison between laboratory and on-road tests and allowing real-driving emissions to be estimated based on chassis-dynamometer data. Studies on plug-in hybrids highlighted that these vehicles remain environmentally beneficial only when regularly charged; otherwise, their emissions can increase sharply. Overall, the research confirms that on-road testing is essential for a reliable evaluation of vehicle performance, and the results can contribute to designing more eco-friendly technologies and improving future emission regulations. Full article

Road transport constitutes a crucial element of the European economy, but it also generates significant external costs. In the process of reducing the impact of road transport on the environment and society, numerous actions are being undertaken to implement the concept of sustainable transport development in the Member States of the European Union. A key measure in this area is the introduction of low- and zero-emission propulsion systems in vehicles intended for passenger and freight transport. This article focuses on electric vehicles powered by battery energy storage systems. An essential component of these efforts is the development of alternative fuels infrastructure, which is expected to enable the operation of such vehicles by providing access to battery charging facilities. The development of infrastructure in the form of electric vehicle charging stations, initially concentrated in urban areas, has been extended to the network of European roads. The driving force behind this expansion is the European Parliament and the Council of the EU, which, on the basis of the Alternative Fuels Infrastructure Regulation (AFIR), stimulate the development of alternative fuels infrastructure along the TEN-T network. The aim of the article is to present selected challenges related to the electrification of road transport infrastructure in the context of the sustainable transport development concept and the construction of alternative fuels infrastructure along the TEN-T network. The research focuses on forecasting the demand for alternative fuels infrastructure along the A1 and A2 motorways, which form part of the TEN-T network within the territory of Poland. The research process stems from the implementation of the AFIR in the EU Member States. Full article

Current research and development in understanding road users’ driving behaviors play a key role in improving traffic safety. Recently, several driving simulators have been employed as a suitable approach to investigate several drivers’ responses in challenging traffic scenarios. Although professional drivers represent a particular category among driving populations, the body of literature about their comparative behavioral and psychological characteristics remains limited. This study examined the differences in driving performance and visual and physiological responses between civilian and professional drivers in a simulated environment. A total of 30 drivers, with an equal split between professional and civilian categories, took part in a series of driving simulations. The simulations incorporated various infrastructure types, including four cone avoidance tasks and a high-speed motorway task. This study collected comprehensive data on performance metrics, hand usage, heart rate, and eye movements. Eye-tracking technology was used to measure visual attention. The findings revealed that during cone avoidance scenarios, civilian drivers exhibited a similar performance, visual behavior, and physiological response, except for the speed, experiment duration, and throttle, to professional drivers. In the motorway scenario, all metrics showed no significant difference between the two driver groups. These results highlight the need for cautious interpretation, particularly given the limitations of the sample. Revalidation is needed in larger studies, especially for understanding the differences between drivers’ metrics, which is crucial to elevate drivers’ safety, and assessing training programs in Hungary. Full article

Road infrastructure elements like guardrails, bollards, delineators, and traffic signs are critical for traffic safety but are significantly underrepresented in existing driving datasets, which primarily focus on vehicles and pedestrians. To address this crucial gap, we introduce DORIE (Dataset of Road Infrastructure Elements), a novel, high-resolution dataset specifically curated for real-time patrol vehicle monitoring along the A2 motorway in Spain. DORIE features 938 manually annotated images containing over 6800 object instances across ten safety-critical categories, including both static infrastructure and dynamic traffic participants. To establish a robust performance benchmark, we conducted an extensive evaluation of the YOLO family of detectors (versions 8, 11, and 12) across multiple scales and input resolutions. The results show that larger YOLO models and higher-resolution inputs yield up to 40% improvement in mean Average Precision (mAP) compared to smaller architectures, particularly for small and visually diverse classes such as traffic signs and bollards. The inference latency ranged between 5.7 and 245.2 ms per frame, illustrating the trade-off between detection accuracy and processing speed relevant to real-time operation. By releasing DORIE with detailed annotations and quantitative YOLO-based baselines, we provide a verifiable and reproducible resource to advance research in infrastructure monitoring and support the development of intelligent road safety and maintenance systems. Full article

Driving safety hinges on the dynamic interplay between task demand and driving capability, yet these concepts lack a unified, quantifiable formulation. In this work, we present a framework based on a multimodal large language model that transforms heterogeneous driving signals—scene images, maneuver descriptions, control inputs, and surrounding traffic states—into low-dimensional embeddings of task demand and driving capability. By projecting both embeddings into a shared latent space, the framework yields an interpretable measurement of task difficulty that alerts to capability shortfalls before unsafe behavior arises. Built upon a customized BLIP 2 backbone and fine-tuned on diverse simulated driving scenarios, the model respects consistency within tasks, captures impairment-related capability degradation, and can transfer to real-world motorway data without additional training. These findings endorse the framework as a concise yet effective step toward proactive, explainable risk assessment in intelligent vehicles. Full article

Although particulate matter (PM) pollution from vehicles’ exhaust has decreased significantly over the years, the contribution from non-exhaust sources (brakes, tyres) has remained at the same levels. In the European Union (EU), Euro 7 regulation introduced PM limits for vehicles’ brake systems. Regenerative braking, i.e., recuperation of the deceleration kinetic and potential energy to the vehicle battery, is one of the strategies to reduce the brake emission levels and improve vehicle efficiency. According to the regulation, the shares of friction and regenerative braking can be determined with actual testing of the vehicle on a chassis dynamometer. In this study we tested the regenerative capabilities of a plug-in hybrid vehicle, both in the laboratory and on the road, under different protocols (including both smooth and aggressive braking) and covering a wide range of driving conditions (urban, rural, motorway) over 10,000 km of driving. Good agreement was obtained between laboratory and on-road tests, with the use of the friction brakes being on average 7% and 5.3%, respectively. However, at the same time it was demonstrated that the friction braking share can vary over a wide range (up to around 30%), depending on the driver’s behaviour. Full article

Highway exit ramps play a crucial role in ensuring the safe and efficient operation of road networks. As automated vehicles progressively integrate into highways, it is essential to investigate whether these exit ramps can accommodate the safe and efficient operation of heterogeneous traffic flows. This study constructed a basic simulation test using the SUMO simulation platform to analyze the adaptability of motorway exit ramps in a heterogeneous traffic environment. The simulation model incorporated the Krauss car-following model for the longitudinal dynamics of manual-driving vehicles, the ACC/CACC car-following model for automated vehicles, the LC2013 lane-changing model for manual-driving vehicles, and the game-theoretic lane-changing model for automated vehicles. The results reveal that in the absence of automated vehicles, the comprehensive cost is minimized with a deceleration lane length of 215 m, offering superior adaptability compared to the current standard of 180 m. As the proportion of automated vehicles gradually increases to surpass 40%, the rate of improvement in traffic flow, operational speed, and overall operational costs diminishes. Under these conditions, heterogeneous traffic flows exhibit limited adaptability to the existing road infrastructure. However, when the deceleration lane is extended to 200 m, the exit ramp shows optimal adaptability for heterogeneous traffic flows. Full article

This study aims to identify the factors that influence the occurrence of traffic accidents to improve motorway traffic safety. Various data, including the frequency of traffic accidents, traffic volume, geometric structure, and congestion level, were collected from individual sections of motorways in South Korea. Using the collected data, a traffic accident frequency prediction model was developed by applying an explainable artificial intelligence (AI)-based approach. The developed deep neural network model was combined with Shapley Additive Explanations to identify the variables that significantly affect the frequency of traffic accidents. The analysis identified five significant factors: segment length, total traffic volume, the proportion of truck traffic, the number of dangerous driving behaviors, and the duration of congestion. The results demonstrate the potential of using explainable AI in predicting traffic accident frequency. By identifying the factors that influence traffic accidents using this model, we can pinpoint areas for improvement, which may ultimately help reduce highway traffic accidents. Full article

The recently introduced Euro 7 emissions standard regulation foresees the addition of abrasion limits for tyres sold in the European Union. The measurement procedures for tyre abrasion are described in the newly introduced Annex 10 of the United Nations (UN) Regulation 117. However, the limits are not yet defined as there is no data available regarding the new procedure. For this reason, a market assessment campaign is ongoing under the auspices of the UN Task Force on Tyre Abrasion (TFTA). Recent reviews on the topic also concluded that there is a lack of studies measuring the abrasion rates of tyres. In this study, we measured the abrasion rate of one tyre model at two different locations (Spain and China) with the aim of deep diving into possible influencing factors. Additionally, wear rates were studied separately for urban, rural, and motorway routes to get more insight into the impact of the route characteristics. The abrasion rates varied from 22 mg/km to 123 mg/km per vehicle, depending on the route (urban, rural, motorway) and ambient temperature. The overall average trip abrasion rates were 75 mg/km and 45 mg/km per vehicle at the two locations, respectively. However, when corrected for the different ambient temperatures, the rates were 63 mg/km and 60 mg/km per vehicle, respectively. The impacts of other parameters, such as driving dynamics and road surface, on the final results are also discussed. The average tread depth reduction was estimated to be 0.8–1.4 mm every 10,000 km. Full article

Energy consumption in electric vehicles is a key element of their operation, determining energy efficiency and one of its main indicators, i.e., range. Therefore, in this article, mathematical models were developed to evaluate the impact of selected factors on energy consumption in electric vehicles. The phenomenon of energy recuperation was also examined. The study used data from mileage measurements of the electric vehicle (EV) driving on a motorway and in built-up areas. The results obtained showed a strong correlation between acceleration, vehicle speed, battery power, and energy consumption. In urban conditions, engine RPM and vehicle speed had an additional impact on energy consumption. Findings from this study can be used to optimize vehicle acceleration control modules to increase their range, develop eco-driving styles for EV drivers, and better understand the energy efficiency factors of EVs. Full article

This paper introduces an anchorless deep learning model designed for efficient analysis and processing of large-scale 3D synthetic traffic sign board datasets. With an ever-increasing emphasis on autonomous driving systems and their reliance on precise environmental perception, the ability to accurately interpret traffic sign information is crucial. Our model seamlessly integrates object detection, depth estimation, deformable parts, and text character extraction functionalities, facilitating a comprehensive understanding of road signs in simulated environments that mimic the real world. The dataset used has a large number of artificially generated traffic signs for 183 different classes. The signs include place names in Japanese and English, expressway names in Japanese and English, distances and motorway numbers, and direction arrow marks with different lighting, occlusion, viewing angles, camera distortion, day and night cycles, and bad weather like rain, snow, and fog. This was done so that the model could be tested thoroughly in a wide range of difficult conditions. We developed a convolutional neural network with a modified lightweight hourglass backbone using depthwise spatial and pointwise convolutions, along with spatial and channel attention modules that produce resilient feature maps. We conducted experiments to benchmark our model against the baseline model, showing improved accuracy and efficiency in both depth estimation and text extraction tasks, crucial for real-time applications in autonomous navigation systems. With its model efficiency and partwise decoded predictions, along with Optical Character Recognition (OCR), our approach suggests its potential as a valuable tool for developers of Advanced Driver-Assistance Systems (ADAS), Autonomous Vehicle (AV) technologies, and transportation safety applications, ensuring reliable navigation solutions. Full article

Electric vehicles (EVs) encounter substantial obstacles in effectively managing energy, particularly when faced with varied driving circumstances and surrounding factors. This study aims to evaluate the performance of three different control systems in a fully operational hybrid energy storage system (HESS) installed in the Nissan Leaf. The objective is to improve the performance of EVs by focusing on optimising energy management in response to different global environmental and driving circumstances. This study utilises an analytical strategy by developing a distinct energy management system model using MATLAB/Simulink. This model is specifically designed for optimising the integration and control of batteries and supercapacitors (SCs) in a fully active HESS. This model mimics the performance of the controllers under three different driving cycles—Artemis rural, Artemis motorway, and US06. The findings demonstrate notable progress in managing the battery state of charge (SOC) and the system’s responsiveness, especially when employing the radial basis function (RBF) controller. This study emphasises the capacity of HESSs to enhance the effectiveness and durability of EVs, therefore promoting wider acceptance and progress in electric transportation technology. Full article

The lack of real-world data on Connected and Autonomous Vehicles (CAVs) has prompted researchers to rely on simulations to assess their societal impacts. However, few studies address the operational and technological challenges of integrating CAVs into existing transport systems. This paper introduces a new CAV driving model featuring a constant time gap longitudinal control algorithm that accounts for sensor errors and platoon formations of varying sizes. Additionally, it develops a high-level route-based decision-making algorithm for CAV path choice. These algorithms were tested in a calibrated motorway corridor simulation, examining different market penetration rates, platoon sizes, and sensor error scenarios. Traffic conflicts were used as a primary safety performance indicator. The findings indicate that CAV sensors are generally adequate, but optimal platoon sizes vary with market penetration rates. To further explore factors influencing traffic conflicts, a hierarchical Bayesian negative binomial regression model was used. This model revealed that in addition to unobserved heterogeneity and spatial autocorrelation, the standard deviation of speeds between lanes and the CAV market penetration rate significantly affect conflict occurrences. These results corroborate the simulation outcomes, enhancing our understanding of CAV deployment impacts on traffic safety. Full article

In this work, an intelligent hybrid model is proposed to identify hazardous or inattentive driving manoeuvres on roads, with the final goal being to increase and ensure travellers’ safety and comfort. The estimation is based on the effects that road geometry may have on vehicle accelerations, displacements and dynamics. The outputs of the intelligent systems proposed are how the type of driving can be characterized as normal, careless or distracted. The intelligent system consists of an LSTM (Long Short-Term Memory) neural network in a first step that distinguishes between normal and abnormal driving behaviour and then a second module that classifies abnormal forms of driving as aggressive or inattentive, with the latter implemented with another LSTM, a CNN (convolutional neural network) or the Hotelling transform. They are applied to some of the characteristics of vehicle dynamics to estimate the driving behaviour. Smartphone inertial sensors such as GPS, accelerometers and gyroscopes are used to measure these vehicle characteristics and to identify driving events in manoeuvres. Specifically, the critical acceleration due to the influence of the road geometry can be measured with inertial sensors, and then, this road acceleration with the lateral acceleration allows us to estimate the driver’s perceived acceleration. This perceived acceleration affects the driving style and, consequently, the estimation of the appropriate speed to travel on that road. There is use of both a traditional two-lane and a motorway route located in the Madrid region of Spain. Driving behaviour is determined by considering how changes in road geometry may affect one’s driving style and, consequently, the estimation of the proper speed. The results obtained with some of the proposed configurations of the intelligent hybrid system reach an accuracy of 97.21% in detecting dangerous driving or driving with a certain risk. This could allow generating real-time alerts for potentially dangerous or inattentive manoeuvres, leading to safer and more appropriate driving. Full article