Vehicle Classification Research Articles

Least-cost light-duty vehicle fleet decarbonization and the electric vehicle conundrum

This paper evaluates the greenhouse gas (GHG) emissions and financial performance of the U.S. light-duty vehicle (LDV) fleet. It develops dynamic models for conventional (ICEV), hybrid (HEV), plugin hybrid (PHEV) and battery electric (BEV) LDVs in four vehicle classes (compact, sedan, SUV and pickup truck), estimating their component sizes, purchase prices, embodied emissions, fuel consumption, life cycle GHG emissions and total cost of ownership (TCO) as a function of vehicle miles travelled (VMT). These dynamic models are applied to the US LDV fleet with its wide range of VMT per vehicle, revealing that ICEVs have the lowest TCO at lower VMT, but HEVs and BEVs become more cost-effective as VMT increases. Financially optimal powertrain choice can then reduce fleet-wide GHG emissions by one-third with current technologies, without any subsidies or increased costs. Technological advances, particularly in battery costs and electricity emissions, are then powerful drivers of further fleet decarbonization.

Allowable stress rating compared to load and resistance factor rating for timber bridges with and without steel-beam retrofit

This paper presents the relevancy of the Allowable Stress Rating (ASR) and the Load and Resistance Factor Rating (LRFR) methods for timber bridges. Benchmark bridges constructed in the 1930′s are upgraded with hollow structural steel (HSS) beams and three-dimensional finite element models provide technical information that is necessary for examining their behavior and rating evaluations. Complying with published manuals, a total of 17 rating vehicles are considered across three categories (Design, Legal, and Permit) so as to generate the maximum responses of the bridges. The position of the vehicles dominates the deflection profiles of the unrepaired bridges. After installing the HSS beams, the live loads are redistributed and stiffness enhancement is noticed in the transverse direction of the bridges. The rating factors calculated with ASR exceed those with LRFR for the unrepaired bridges, regardless of vehicle configurations, and the level of disparity between these rating approaches increases when the bridges are repaired owing to differences in load factors. In terms of sensitivity to average daily truck traffic, the LRFR factors of the bridges under the Legal vehicles are more responsive than the factors subjected to other vehicle types. From a probability perspective, the compatibility of these rating methodologies varies contingent upon vehicle categories and the presence of the HSS beams. Practice guidelines are proposed to facilitate the conversion of rating factors between ASR and LRFR as a function of the present condition of constructed timber bridges.

When does Reckless Driving Occurs? Case Study in Nampula, Mozambique

This study investigates the influence of traffic conditions on reckless driving behavior in Nampula, Mozambique, where road accidents claim several lives daily. Previous research has largely focused on individual driving behaviors, neglecting the impact of traffic environments on such behaviors. This study addresses this gap by categorizing vehicles into four groups: passenger cars, minibuses, trucks, and motorcycles, and classifying traffic conditions using k-means clustering. Three distinct traffic patterns were identified: Motorcycle and Car Dominant Traffic (MCDT), High-Density Traffic Flow (HDTf), and Low-Density Traffic Flow (LDTf). Results show that reckless driving is significantly influenced by traffic conditions, with minibuses, motorcycles, and trucks being key contributors. Pattern 2 (HDTf) demonstrated the highest occurrence of reckless driving, driven by competition among minibus drivers, the obstructive presence of trucks, and risky motorcycle maneuvers. Pattern 1 also exhibited elevated risk, particularly performed among motorcycle riders, while Pattern 3 (LDTf) showed the lowest risk due to reduced traffic density. Poisson regression analysis revealed that increased traffic flow in any vehicle category is associated with higher rates of reckless driving. Minibuses and motorcycles, functioning as demand-oriented public transport, engage in competitive and unsafe behaviors, while trucks, though not engaging in reckless driving directly, act as barriers that exacerbate risky maneuvers by other vehicles. These findings highlight the need for dedicated lanes for minibuses and motorcycles and tailored traffic safety policies to mitigate reckless driving.

Behavioral Modeling of Drivers near Speed Control Cameras: A Dual Perspective from Micro and Macro Data

The present study investigates the efficacy of speed cameras in managing vehicle speeds and their influence on various driver demographics within the operational context of the cameras. The research, conducted as a case study in Iran, comprises both macroscopic and detailed analyses, quantifying vehicle speeds at different spatial points relative to the speed camera location—upstream, adjacent to, and downstream of the camera—using a manual laser speedometer. Furthermore, the study examines diverse driver behavior metrics, including vehicle type, speeds at different locations, lane changes, braking actions, and measures such as obscuring license plates. Statistical analyses show that speed cameras effectively reduce speeds across different user groups and lead to increased braking and lane-changing behaviors, particularly among light vehicle categories. Moreover, the study reveals distinct behavioral patterns between indigenous and non-indigenous drivers. In addition, a multi-layer perceptron neural network model successfully approximates user speed selection behaviors within the operational range of speed cameras. Overall, this research provides valuable insights into the effectiveness of speed cameras and their impact on diverse driver demographics, contributing to a deeper understanding of advanced speed control mechanisms.

Diversity-Representativeness Replay and Knowledge Alignment for Lifelong Vehicle Re-identification

Lifelong vehicle re-identification (LVReID) aims to match a target vehicle across multiple cameras, considering non-stationary and continuous data streams, which fits the needs of the practical application better than traditional vehicle re-identification. Nonetheless, this area has received relatively little attention. Recently, methods for lifelong person re-identification (LPReID) have been emerging, with replay-based methods achieving the best results by storing a small number of instances from previous tasks for retraining, thus effectively reducing catastrophic forgetting. However, these methods cannot be directly applied to LVReID because they fail to simultaneously consider the diversity and representativeness of replayed data, resulting in biases between the subset stored in the memory buffer and the original data. They randomly sample classes, which may not adequately represent the distribution of the original data. Additionally, these methods fail to consider the rich variation in instances of the same vehicle class due to factors such as vehicle orientation and lighting conditions. Therefore, preserving more informative classes and instances for replay helps maintain information from previous tasks and may mitigate the model’s forgetting of old knowledge. In view of this, we propose a novel Diversity-Representativeness Dual-Stage Sampling Replay (DDSR) strategy for LVReID that constructs an effective memory buffer through two stages, i.e. , Cluster-Centric Class Selection and Diverse Instance Mining. Specifically, we first perform class-level sampling based on density in the clustered class-centered feature space and then further mine the diverse, high-quality instances within the selected classes. In addition, we introduce Maximum Mean Discrepancy loss to align the feature distribution between replay data and the new arrivals and apply L2 regularisation in the parameter space to facilitate knowledge transfer, thus enhancing the model’s generalization ability to new tasks. Extensive experiments demonstrate effective improvements of our method compared to current state-of-the-art lifelong ReID methods on the VeRi-776, VehicleID, and VERI-Wild datasets.

Robust evaluation of big data-driven winter weather traffic models using six weigh-in-motion sites as testbeds in Alberta's highway network

ABSTRACT This study assesses the temporal transferability of winter traffic models using dummy variable regression at six Weigh-in-Motion (WIM) sites on Alberta's highway network. Models for two vehicle classes were developed using five years of traffic and weather data. To evaluate transferability, an additional year of data was collected, and two model structures—dummy variable and naive—were tested. The models’ estimation accuracy was measured using R² and five error metrics. Results indicate successful transferability of the models to a different year, with performance varying by road function and vehicle class. The study highlights that different model types might be needed for each vehicle class to ensure high temporal transferability. Additionally, the quality of the test data is crucial for obtaining reliable transfer results. This research addressed gaps in previous studies by collectively testing the temporal transferability of winter traffic models across an entire highway network.

Geometric Design Optimization of Speed Tables at Urban Arterials using UAV Assistance

One of the primary risk factors at junctions on urban roads is vehicle speed. To curb over-speeding and road crashes at intersections, traffic calming measures are introduced. Current research aims at studying the impact of different geometries of recently constructed speed tables on the operational speed over such intersections. Since these treatments have been often used to regulate speed in other countries, an evaluation of their efficacy in the Indian context was required. This study utilises 6,000 vehicle samples of four different vehicle classes (two-wheeler, three-wheeler, cars, and buses) from 12 speed tables in total. The speed and acceleration kinematics in addition to the high-quality trajectory data over long road segments were extracted from the video recordings of an Unmanned Aerial Vehicle (UAV). Multi-factor Response Surface Methodology (RSM) was utilized to optimize the geometric parameters (variables) of the speed tables to achieve the requisite operational speed (predictor) at the considered measure. The box plots are provided to indicate descriptives of the parameters regarding the 85th percentile speed. Multiple linear regression and ANOVA identified the variables that were significant and fit to devise the required optimization model. This study can help in identifying the influence zone, concerning physical characteristics of speed tables and their effect on the design speed at the approaches of intersections at urban arterials. The outcomes of the study will cater to enhance the current guidelines and standards in India regarding speed table geometry for urban road sections.

Impact of Driving Timing and Road Types on the Operating Regions of Electric Two-wheeler Powertrain Components under Indian Road Conditions

Among the different classes of electric vehicles (EVs), electric two-wheelers (E2Ws) are securing more enticement towards the commutator for regular usage due to their higher suitability in the heterogeneous traffic conditions of India. However, the driving range of the E2Ws is the major concern in increasing its penetration rate in road transportation. To eradicate the range anxiety of commutators by redesigning the current E2W powertrain configuration, this study strives to insights into the operational limits of powertrain elements such as the battery and motor in E2W under multiple operational circumstances such as driving style, pavement, and ambient conditions. Regarding this, the E2W with data acquisition system (DAC) connected with an onboard diagnosis (OBD) port is utilized to acquire the data related to powertrain components and vehicles for four driving trips (trips 1–4) under three major road segments (Rural, expressway, and urban) of India. The operating and computed performance metrics, comprising battery voltage and current, state-of-charge (SOC), energy consumption, motor current, speed, efficiency, and so on, are then compared throughout driving trips and road segments. The mean velocity of E2W for trips 1 to 4 is 31.91, 33.68, 39.03, and 35.58 km/h, respectively. The results disclosed that total energy consumption and energy consumption per km. are higher during trip 3 due to higher average vehicle speed. Also, it has a higher depth-of-discharge (DOD) of 83.91 % than other driving trips. Further, a higher rate of SOC drop, energy consumption and battery temperature rise are found at highway road segments.

A two-lane bidirectional nonlocal traffic model

We propose and study a nonlocal system of balance laws, which models the traffic dynamics on a two-lane and two-way road where drivers have a preferred lane (the lane on their right) and the other one is used only for overtaking. In this model, the convective part is intended to describe the intralane dynamics of vehicles: the flux function includes local and nonlocal terms, namely, the velocity function in each lane depends locally on the density of the class of vehicles traveling on their preferred lane and in a nonlocal form on the density of the class of vehicles overtaking in the opposite direction. The source terms are intended to describe the coupling between the two lanes: the overtaking and return criteria depend on weighted means of the downstream traffic density of the class of vehicles traveling in their preferred lane and of the class of vehicles traveling in the opposite direction on the same lane. We construct approximate solutions using a finite volume scheme and we prove existence of weak solutions by means of compactness estimates. We also show some numerical simulations to describe the behavior of the numerical solutions in different situations and to illustrate some features of model.

Unmanned Aerial Vehicle Classification Using Neural Neworks and Radar Digital Twins: UAV Classification Using Neural Networks and Radar Digital Twins

Unmanned Aerial Vehicle Classification Using Neural Neworks and Radar Digital Twins: UAV Classification Using Neural Networks and Radar Digital Twins

Modelling Severe Weather Impact on Traffic Mobility and Evaluating Cross-Regional Applicability to Functionally Distinct Segments in Cold Region Highway Networks

This study in Alberta, Canada, focuses on winter traffic modeling, crucial for transportation agencies in cold regions. By gathering data from six Weigh-in-motion (WIM) sites, models were developed and tested for three vehicle types. Notably, the research assessed the transferability of models between different highway segments, indicating successful transfer of coefficients to varied road functionalities. This suggests that agencies can optimize model structures for specific vehicle classes, enhancing usability without additional monitoring sites. Overall, the study highlights the feasibility of improving winter traffic models and their applicability across diverse road segments, benefiting transportation planning and management in cold climates.

Exploiting Remote Sensing Imagery for Vehicle Detection and Classification using Robust Competitive Algorithm with Deep Convolutional Neural Network

Exploiting Remote Sensing Imagery for Vehicle Detection and Classification using Robust Competitive Algorithm with Deep Convolutional Neural Network

New Trends in the Procurement and Use of Unmanned Aerial Vehicles

Based on the presented classification of unmanned aerial vehicles (UAVs), the specifics of their use in modern conflicts are examined. The main trends in the procurement of UAVs are analyzed. It is indicated that the use of UAVs in combat is not limited to the functions of reconnaissance, target designation and strikes, as well as the detection and destruction of terrorist groups, as was the case in Afghanistan and Iraq conflicts. Thе UAVs are increasingly used within the framework of reconnaissance and strike complexes on the battlefield. The RD and production of small and mini-drones, as well as of unmanned aerial vehicles capable of remaining invulnerable to air defense systems, are actively developing. Other modern technologies of unmanned systems are being advanced. Due to the fact that in recent years the trend of massive use of small and mini-UAVs in conflicts has prevailed, supplies to the armed forces of various countries of heavy and a number of medium (tactical) high-altitude and medium-altitude drones are decreasing, while the purchases of small drones, as well as light civilian UAVs with reconfigured software, are increasing.

The Correlation of the Smart City Concept with the Costs of Toxic Exhaust Gas Emissions Based on the Analysis of a Selected Population of Motor Vehicles in Urban Traffic

The intensive development of road transport has resulted in a significant increase in air pollution. This phenomenon is particularly noticeable in urban areas. This creates the need for analyses and forecasts of the scale and extent of future emissions of harmful substances into the environment. The aim of this study was to estimate the costs of the emission of toxic components of exhaust gases generated by all users of conventionally propelled vehicles travelling on a section of urban road in the next 25 years. The traffic study was carried out on an urban traffic route, playing a key role for road transport in the dimension of a given urban agglomeration. The traffic forecast for the analysed road section was based on the results of our own measurements carried out in April 2023 and external data from the General Directorate for Roads and Motorways. The results of the observations concerned six categories of vehicles for the morning and afternoon rush hours. Based on the data obtained, the generic structure of the vehicle population on the analysed section and the average daily traffic were determined. Using the methodology contained in the Blue Book of Road Infrastructure, parameters were calculated in the form of annual indicators of traffic growth on the analysed section, travel speed, and annual air pollution costs for selected vehicle categories, remembering at the same time that the Blue Book-based methodology does not distinguish between unit costs in relation to the type of emissions. The results of the study confirmed that there was an increase in the cost of toxic emissions for each vehicle category over the projected 25-year period. The largest increases were seen for trucks with trailers and passenger cars. In total, for all vehicle categories, emission costs nearly doubled from 2024 to 2046, from EUR 3,745,229 to EUR 7,443,384, due to the doubling of the number of vehicles resulting from the traffic forecast. The analyses presented here provide an answer to the question of what pollution costs may be faced by cities in which road transport will continue to be based on conventional types of propulsion. In addition, the research presented can be used to develop urban mobility transformation plans for the coming years, within the scope of the widely promoted smart city concept and the idea of electromobility, by pointing out to local authorities the direct economic benefits of these changes.

Enhancing Situational Awareness with VAS-Compass Net for the Recognition of Directional Vehicle Alert Sounds.

People with hearing impairments often face increased risks related to traffic accidents due to their reduced ability to perceive surrounding sounds. Given the cost and usage limitations of traditional hearing aids and cochlear implants, this study aims to develop a sound alert assistance system (SAAS) to enhance situational awareness and improve travel safety for people with hearing impairments. We proposed the VAS-Compass Net (Vehicle Alert Sound-Compass Net), which integrates three lightweight convolutional neural networks: EfficientNet-lite0, MobileNetV3-Small, and GhostNet. Through employing a fuzzy ranking ensemble technique, our proposed model can identify different categories of vehicle alert sounds and directions of sound sources on an edge computing device. The experimental dataset consisted of images derived from the sounds of approaching police cars, ambulances, fire trucks, and car horns from various directions. The audio signals were converted into spectrogram images and Mel-frequency cepstral coefficient images, and they were fused into a complete image using image stitching techniques. We successfully deployed our proposed model on a Raspberry Pi 5 microcomputer, paired with a customized smartwatch to realize an SAAS. Our experimental results demonstrated that VAS-Compass Net achieved an accuracy of 84.38% based on server-based computing and an accuracy of 83.01% based on edge computing. Our proposed SAAS has the potential to significantly enhance the situational awareness, alertness, and safety of people with hearing impairments on the road.

Evaluation of potential electric vehicles load-induced damage on flexible pavements

The adoption of Electric Vehicles (EVs), driven by policies like the Zero Emission Vehicle (ZEV) mandate, is rapidly transforming transportation infrastructure. In 2021, over 1.25-million light-duty EVs were registered in the United States, signaling a shift that extends to heavy vehicle categories. This study evaluates the implications of this transition on road networks, focusing on the increased gross weight of EVs compared to traditional Internal Combustion Engine (ICE) vehicles and its impact on flexible pavement structures. The study investigates the deterioration patterns and potential damage to road infrastructures resulting from integrating EVs into the traffic mix, utilizing both the AASHTO 1993 and Mechanistic-Empirical (ME) pavement evaluation methodologies. The analyses reveal a significant acceleration in road degradation, necessitating urgent consideration of EV-induced stresses by transportation authorities. Recommendations for future research and practical strategies for mitigating these impacts are provided to guide policymakers and engineers in adapting to this evolving vehicular landscape.

Analysis of Energy Efficiency Parameters of a Hybrid Vehicle Powered by Fuel with a Liquid Catalyst

A notable trend in the modern automotive market is the increased interest in hybrid cars. Hybrid cars combine a standard internal combustion engine with an electric motor solution. Research into increasing the energy efficiency of a conventional unit while meeting increasingly stringent exhaust emission standards is becoming a key postulate in this matter. This article discusses an analysis of modifying the fuel used by hybrid vehicles using the example of a selected drive unit equipped with a spark-ignition engine. This effect was tested after the Eco Fuel Shot liquid catalyst was added to the fuel. The research process was carried out in two stages, as follows: in road conditions using the Dynomet road dynamometer; and on the V-tech VT4/B2 chassis dynamometer. Tests were carried out to replicate road tests with a catalytic additive in the fuel. A mathematical model was created and the following energy efficiency parameters of the hybrid vehicle were calculated: the torque of the internal combustion engine, electric motor, and generator; the rotational speeds of the internal combustion engine, electric motor, and generator; the power of the internal combustion engine, electric motor, and generator; the equivalent fuel consumption of the electric motor and generator; the fuel consumption of the internal combustion engine, electric motor, and generator; and the mileage fuel consumption of the internal combustion engine, electric motor, and generator. The results of the tests made it possible to identify the benefits of using the tested liquid catalyst on the operation of the drive system of the analyzed hybrid vehicle. This research will be of benefit to both the demand side in the form of users of this category of vehicles, and the supply side represented by the manufacturers of power units.

Enhanced Vehicle Logo Detection Method Based on Self-Attention Mechanism for Electric Vehicle Application

Vehicle logo detection plays a crucial role in various computer vision applications, such as vehicle classification and detection. In this research, we propose an improved vehicle logo detection method leveraging the self-attention mechanism. Our feature-sampling structure integrates multiple attention mechanisms and bidirectional feature aggregation to enhance the discriminative power of the detection model. Specifically, we introduce the multi-head attention for multi-scale feature fusion module to capture multi-scale contextual information effectively. Moreover, we incorporate the bidirectional aggregation mechanism to facilitate information exchange between different layers of the detection network. Experimental results on a benchmark dataset (VLD-45 dataset) demonstrate that our proposed method outperforms baseline models in terms of both detection accuracy and efficiency. Our experimental evaluation using the VLD-45 dataset achieves a state-of-the-art result of 90.3% mAP. Our method has also improved AP by 10% for difficult samples, such as HAVAL and LAND ROVER. Our method provides a new detection framework for small-size objects, with potential applications in various fields.

FBrake, a Method to Simulate the Brake Efficiency of Laden Light Passenger Vehicles in PTIs While Measuring the Braking Forces of Their Unladen Configurations

This study introduces fBrake, a novel simulation method now designed for use in periodic technical inspections of M1 and N1 vehicle categories, addressing challenges posed by Directive 2014/45/EU. The directive mandates that braking efficiency must be measured relative to the vehicle’s maximum mass, which often results in underperformance during inspections due to vehicles typically being unladen. This discrepancy arises because the maximum braking forces are proportional to the vertical load on the wheels, causing empty vehicles to lock their wheels prematurely compared to laden ones. fBrake simulates the braking forces of unladen vehicles to reflect a laden state by employing an optimal brake-force distribution curve that aligns with the vehicle’s inherent braking behavior, whether through proportioning valves or through electronic brake distribution systems in anti-lock-braking-system-equipped vehicles. Our methodology, previously applied to heavy vehicles, involved extensive experimentation with a roller brake tester, comparing the actual braking performances of dozens of vehicles to those of their simulated counterparts using fBrake. The results demonstrate that fBrake reliably replicates the braking efficiency of laden vehicles, validating its use as an accurate and effective tool for braking system assessments in periodic inspections, irrespective of the vehicle’s load condition during the test. This approach ensures compliance with regulatory requirements while enhancing the reliability and safety of vehicle inspections.

Transformation and Development of New Energy Vehicle Industry Based on Social Policy Inquiry

The new energy vehicle industry has drawn the interest of international attention with the growing global demand for sustainability and reduction of carbon footprint. The growth of the New Energy Vehicle (NEV) industry is profoundly influenced by global policies aimed at driving technological innovation, meeting environmental requirements, and facilitating economic transformation. Multiple stakeholders, from policymakers to companies, from consumers to technology developers, are working together to drive the rapid growth of this industry. By analyzing research and practices from various nations and areas, insights can be gained on strategies for successfully advancing the growth of the new energy vehicle sector. From a social point of view more environmentally friendly, reducing the pollution of the traditional class of oil vehicles on the environment; from the consumer point of view, due to the fierce competition in the growth of the automotive new energy sector, the businessman will provide more tempting products to attract consumers, to promote the consumer to pick the price is more suitable for the quality of the product better, and further increase customer satisfaction. The future development of the new energy vehicle industry relies on progress on multiple fronts: policy, technology, and market. The healthy development and environmental sustainability of the industry can be more effectively promoted through international cooperation, technological innovation, and a deeper understanding of consumer needs. Policymakers need to take these factors into account to ensure that policies are both relevant to current market and technological realities and adaptable to future changes.