Vehicle Type Research Articles

Fixed-time observation and predefined performance regulation for oxygen excess ratio of vehicular fuel cells under input saturation

The efficiency, health and lifetime of vehicle-mounted fuel cells highly rely on the oxygen excess ratio (OER). Faced with urgent research limitations, this article tackles the estimation and regulation for immeasurable OER, while suppressing the influences of dramatic stack current, uncertain system information, actuator saturation and performance constraint. Restricted by high cost and measurement difficulty, two fixed-time adaptive observers are proposed to rebuild unavailable OER and lumped disturbance respectively, which eliminate the dependence of estimation time on initial states and alleviate the peaking issue. Further, a fixed-time prescribed performance controller is developed to elevate the tracking performance for the estimated OER, in which the overshoot, regulation time and convergence region can be quantitatively constrained, regardless of initial conditions. Specially, to exclude undesired control singularity, an improved performance function is established to automatically adjust the predefined constraint boundaries depending on the captured variations of stack current and anti-saturation auxiliary signal. The hardware-in-loop validation results under custom working conditions and a typical vehicle driving cycle exhibit that the estimation and regulation of OER reach stability within 0.2s and 1s respectively, besides, the maximum absolute errors in observing and controlling the OER are limited to around 0.6 and 0.8 respectively, with great adaptability to different scenarios.

Enhanced Autonomous Speed Control System for Integrated Cars

The Automated Vehicle Speed Control System (AVSCS) is an embedded framework that utilizes specialized hardware and software components to automatically regulate a vehicle's speed. This system is designed to be implemented in a variety of vehicles, including cars, trucks, and autonomous vehicles, to enhance safe and efficient driving. The AVSCS comprises several essential elements: sensors, a processor or microcontroller, a control calculation algorithm, and a user interface. The sensors gather real-time data about the vehicle's current speed, weather conditions, and surrounding road environment, including wheel speed, GPS, radar, LIDAR, and cameras. The microcontroller or processor processes the sensor data and employs control algorithms, such as PID (Proportional-Integral-Derivative) controllers or advanced Model Predictive Control (MPC) techniques, to calculate the optimal vehicle speed. This information is then used to adjust the throttle or braking mechanisms accordingly. Additionally, the user interface allows drivers to customize the system or set their desired speed preferences, providing a seamless and personalized driving experience. The integration of the AVSCS into various vehicle types can significantly increase security, streamline traffic,, and reduce energy consumption, making it a crucial component of the connected and autonomous vehicle ecosystem. Keywords: vehicle speed regulator, GPS, radar, LIDAR, and cameras

Evaluation of bridge structure damage based on deep learning identification model

Safety monitoring is an important part of bridge engineering construction and operation. At present, there is room for promoting the health monitoring and evaluation of small and medium-sized concrete bridges. In view of this, the study first models the spatial model and physical parameters of the bridge, and then builds the data of vehicle load and vehicle type. To reduce the complexity of data mapping, wavelet packet decomposition is used to analyze the data structure. And the physical field effect analysis is abandoned to directly mine the data relationship at both ends by using deep neural network. The data decomposition results show that the method can discard the temperature-induced effect. And the local decomposition results of the data meet the input of the neural network. The data measured by the sensor is added to the depth learning model for fitting. The overall and local fitting rates are more than 92%. The loss function converges quickly, and there is no gradient explosion. The model predicts the bridge structural damage caused by vehicle stress of four load categories, and the results show that the average fitting rate is 89.72%. Therefore, the identification path of the proposed deep learning model has positive significance for the evaluation of bridge structural damage. The main contribution of the study is to propose a deep learning-based method for bridge structural damage assessment. By modeling the spatial model and physical parameters of the bridge and combining data from vehicle load and vehicle type, the data structure was analyzed using the wavelet packet decomposition method to eliminate temperature-induced effects and data from sensor measurements were added to the deep learning model for fitting. This finding has positive implications for bridge structural damage assessment and can provide effective pathways and methods to monitor and evaluate the health status of small and medium-sized concrete bridges. This has important practical application value for the construction and operation of bridge engineering.

Design of Dust Emission to Avoid Air Pollution

Amounts of Industries in India have been recognized a sharply increase from year to year with the increment reaching to 42 % per annum. Meanwhile Machineries produce particulate emissions in different sizes with high concentrations depending on type of vehicles, fuels, and engine capacity. Motor Particle emissions are not only to significantly contribute the atmospheric particles but also adverse to human health. In order to reduce the particle emission, it is needed a filter. This study was aimed to develop a thin filter using coconut fibre to reduce particulate emissions for Machines. The filter was made of coconut fibres that were grinded into power and mixed with glues The filter was tested by the measurements of particle concentrations coming out from the vehicle exhaust directly and the particle concentrations after passing through the filter. The efficiency of the filter was calculated by ratio of the particle concentrations before coming in the filter to the particle concentrations after passing through the filter. The results showed that the efficiency of the filter obtained more than 30%. The efficiency increases sharply when a number of the filters are arranged parallelly

Few-shot SAR vehicle target augmentation based on generative adversarial networks

Abstract. The study of few-shot SAR image generation is an effective way to expand the SAR dataset, which not only provides diversified data support for SAR target classification, but also provides a high-fidelity false image template for SAR deceptive jamming. In this paper, we have constructed a multi-frequency and multi-target type SAR vehicle imagery dataset that encompasses frequencies such as X, Ka, P, and S bands. The vehicle types are coaster, suv and cabin. Subsequently, we utilized various Generative Adversarial Networks for image generation from the SAR vehicle dataset. The experimental result indicates that the images generated by the DCGAN and the LSGAN models are of superior quality. Furthermore, we employed different recognition networks to evaluate the classification accuracy of the generated images. Of all the frequency bands, the Ka band generated images achieved the highest recognition rate, with an accuracy of up to 99%. Under conditions of a limited number of samples, the LSGAN model performed the best, reaching a classification recognition rate of 71.48% with a dataset of only 20 samples. Finally, we use a conditional network generation model to generate conditions based on target categories and frequency bands, providing high fidelity samples for SAR deception jamming.

Self-Diagnostic Opportunities for Battery Systems in Electric and Hybrid Vehicles

The number of battery systems is also growing significantly along with the rise in electric and hybrid car sales. Different vehicles use different types and numbers of batteries. Furthermore, the layout and operation of the control and protection electronics units may also differ. The research aims to develop an approach that can autonomously detect and localize the weakest cells. The method was validated by testing the battery systems of three different VW e-Golf electric vehicles. A wide-range discharge test was performed to examine the condition assessment and select the appropriate state of charge (SoC) for all three vehicles. On the one hand, the analysis investigated the cell voltage deviations from the average; the tests cover deviations of 0 mV, 12 mV, 60 mV, 120 mV, and 240 mV. On the other hand, the mean value calculation was used to filter out possible erroneous values. Another important aspect was examining the relationship between the state of charges (SoC) and the deviations. Therefore, the 10% step changes were tested to see which SoC level exhibited more significant voltage deviations. Based on the results, it was observed that there are differences between the cases, and the critical range is not necessarily at the lowest SoC level. Furthermore, the load rate (current) and time of its occurrence play an important role in the search for a faulty cell. An additional advantage of this approach is that the process currently being tested on the VW e-Golf can be relatively simply transferred to other types of vehicles. It can also be a very useful addition for autonomous vehicles, as it can self-test the cells in the system at low power consumption.

An enhanced method for evaluating the effectiveness of protective devices for road safety application

This paper presents an enhanced probabilistic approach to estimate the real-world safety performance of new device concepts for road safety applications from the perspective of Powered Two-Wheeler (PTW) riders who suffer multiple injuries in different body regions. The proposed method estimates the overall effectiveness of safety devices for PTW riders by correlating computer simulations with various levels of actual injuries collected worldwide from accident databases. The study further develops the methodology initially presented by Johnny Korner in 1989 by introducing a new indicator, Global Potential Damage (GPD), that overcomes the limitations of the original method, encompassing six biomechanical injury indices estimated in five body regions. A Weibull regression model was fit to the field data using the Maximum Likelihood Method with boundaries at the 90% confidence level for the construction of novel injury risk curves for PTW riders. The modified methodology was applied for the holistic evaluation of the effectiveness of a new safety system, the Belted Safety Jacket (BSJ), in head-on collisions across multiple injury indices, body regions, vehicle types, and speed pairs without sub-optimizing it at specific crash severities. A virtual multi-body environment was employed to reproduce a selected set of crashes. The BSJ is a device concept comprising a vest with safety belts to restrict the rider’s movements relative to the PTW during crashes. The BSJ exhibited 59% effectiveness, with an undoubted benefit to the head, neck, chest, and lower extremities. The results show that the proposed methodology enables an overall assessment of the injuries, thus improving the protection of PTW users. The novel indicator supports a robust evaluation of safety systems, specifically relevant in the context of PTW accidents.

Data-driven traffic signal adaptive control algorithm integrating vehicle perception and traffic flow data

Due to the equipment error, environmental interference and data transmission delay of vehicle flow detection, the accuracy and real-time performance of vehicle perception and traffic flow data will be affected to some extent, resulting in poor traffic signal control effect. Therefore, a data-driven traffic signal adaptive control algorithm is designed by integrating vehicle perception and traffic flow data. To complete the modeling of urban traffic, the discrete distribution and continuous distribution of traffic are obtained. Based on this research environment, the DV-hop localization algorithm is improved to sense the vehicle position. Based on the phase space reconstruction of traffic flow time series and vehicle location information, traffic flow data is predicted. Based on the driving of traffic data, the vehicle types are divided into small, medium and large three categories, and the impact weights are assigned respectively, and the weight values affecting the final allocation of green time are obtained to realize the allocation of green time. The experimental results show that: The research algorithm can not only predict the traffic flow intensity effectively, but also the predicted results are highly coincident with the actual traffic flow intensity. Vehicle arrival rates are higher, vehicle delays are shorter, and vehicles stop fewer times on average.

MVT: Multi-Vision Transformer for Event-Based Small Target Detection

MVT: Multi-Vision Transformer for Event-Based Small Target Detection

TARGET RECOGINITION AND DETECTION USING DEEP LEARING TECHNIQUE

Object detection is a fundamental computer vision function for identifying and locating things in images or videos. Unlike image classification, object detection not only classifies the items in an image but also locates them inside the picture by constructing a bounding box around each one. Defence objects include a wide range of key assets needed for military and security activities. Tanks, armoured personnel carriers (APCs), military trucks, planes, helicopters, naval boats, and unmanned ground vehicles (UGVs) are among the types of vehicles included. Detection of these vehicles is critical for tracking troop movements, analysing battlefield dynamics, and maintaining operational preparedness. The modern defence scenario demands improved surveillance and security measures to successfully counter growing threats. This work suggests using the You Only Look Once (YOLO) method for real-time identification of numerous defence items. The project's goal is to improve security by properly detecting a variety of items, including cars, individuals, equipment, and structures, with a single deep learning model. Methodologically, a broad dataset containing photos and videos of defence items in various locations is collected and preprocessed. The YOLO algorithm is then trained on this dataset, with parameters optimised for high accuracy and recall rates. Performance evaluation include thorough testing on various datasets to determine accuracy, speed, and resilience. KEYWORDS: Target recognition, Detection, YOLO algorithm, Military applications, Flying objects, Vehicles, Sensor integration, Real-time processing, Threat detection, Situational awareness.

Bivariate ordered probit modelling of motorcycle riders and pillion passengers’ injury severities relationship and associated risk factors

This study simultaneously modelled the injury severity of motorcycle riders and their pillion passengers and determine the associated risk factors. The analysis is based on motorcycle crashes data in Ashanti region of Ghana spanning from 2017 to 2019. The study implemented bivariate ordered probit model to identify the possible risk factors under the premise that the injury severity of pillion passenger is endogenously related to that of the rider in the event of crash. The model provides more efficient estimates by considered the common unobserved factors shared between rider and pillion passenger. The result shows a significant positive relationship between the two injury severities with a correlation coefficient of 0.63. Thus, the unobservable factors that increase the probability of the rider to sustain more severe injury in the event of crash also increase that of their corresponding pillion passenger. The rider and their pillion passenger injury severities have different propensity to some of the risk factors including passengers’ gender, day of week, road width and light condition. In addition, the study found that time of day, weather condition, collision type, and number of vehicles involved in the crash jointly influence the injury severity of both rider and pillion passenger significantly.

Electrifying the Road: Navigating the Transition to Electric Vehicles in Connecticut Through Hybrid Insights and Fleet Evolution

The adoption of light duty electric vehicles (EVs) in the past 5 years has accelerated dramatically. As EVs continue penetrating the U.S. market, it is critical to understand what a sustainable transition within the transportation sector will look like in relation to mitigating impacts of climate change. This work aims to fill two existing gaps in the current literature: 1) investigate the positive and negative influences of the emerging EV transition within the U.S. market and 2) investigate the types of vehicles EVs are replacing. We fulfill these objectives by utilizing a unique attribute dataset of all vehicles in Connecticut (USA) on the road between 2013-2020. Our findings suggest that the penetration rate of traditional hybrid vehicles (e.g., Toyota Prius) is an important predictor of EV adoption. Furthermore, our results show that in Connecticut most of that transition is occurring among hatchbacks (e.g., Chevrolet Bolt), a body style where internal combustion engine (ICE) versions have relatively high fuel efficiencies compared with other major vehicle types (e.g., pickup trucks or SUVs). Our results offer important implications for policymakers in terms of maximizing the deployment of EVs and EV infrastructure and the path in which the automotive sector is progressing towards decarbonization.

A causal discovery approach to study key mixed traffic‐related factors and age of highway affecting raveling

AbstractThe relationship between real‐world traffic and pavement raveling is unclear and subject to ongoing debates. This research proposes a novel approach that extends beyond traditional correlation analyses to explore causal mechanisms between mixed traffic and raveling. This approach incorporates the causal discovery method, and is applied to five Dutch porous asphalt (PA) highway sites that have substantial data sets. Findings indicate a nonlinear relationship between traffic volume and raveling, with road age emerging as a shared contributor. The results also suggest that the degree to which different vehicle types contribute as a causal factor for raveling varies with carriageway configurations and lane characteristics. This underlines the need for targeted maintenance strategies. Challenges remain due to confounding correlations among traffic variables, necessitating further development of causal discovery models. This study may not conclusively resolve the debate on to what extent traffic contributes to raveling, but we argue we provide sufficient evidence against rejecting this hypothesis.

Carbon Reduction Impact from Synchronising PrescriptionS (CRISPS): A pilot study

Abstract Introduction Synchronising the dates of patients’ repeat prescriptions can reduce monthly community pharmacy visits or home deliveries, which may improve patients’ adherence to medicines.[1] It should also reduce carbon dioxide (CO2) emissions associated with avoidable travel, but previous research does not appear to have determined the potential size of this reduction. This pilot study therefore attempted to do so using routinely collected data. Aim To estimate potential carbon savings from synchronising repeat medicines for a sample of patients from one community pharmacy. Methods All patients who had prescriptions dispensed four times per month or more from a single medium-sized (approximately 2,500 items dispensed per month) urban community pharmacy in England were identified from the pharmacy’s home delivery application. The threshold of four deliveries per month was selected to increase the likelihood of this frequency being due to asynchronised prescriptions. Data were collected (August 2023) for deliveries in June 2023 as there were no public holidays or events to affect prescription ordering. Data included patients’ postcode, delivery dates, and using the Patient Medication Record, the pharmacist collecting the data determined acute or one-off prescription items, that neither the patients nor the repeat medicines were new, and where deliveries were made to supply medicines owing. The pharmacy was selected because it efficiently managed stock to minimise items owing. The distance that would have been travelled was calculated as if the medicines had been collected from the pharmacy as a dedicated journey to and from the patient’s home address instead of being delivered. The saving in miles and CO2 emitted for different vehicle types was modelled by assuming that for each patient, all deliveries that were not for acute items or medicines owing could have been reduced to a single delivery if prescriptions had been synchronised. UK Government conversion factors were used to calculate CO2 emissions.[2] Results Data were collected for 59 patients, of whom 7 patients had 8 deliveries, 4 had 7 deliveries, 6 had 6 deliveries, 16 had 5 deliveries and 26 had 4 deliveries. The total number of deliveries to these patients was 269 but would have been 80 deliveries if the repeat prescriptions had been synchronised (70% reduction). The distance travelled would have been reduced from 842 miles to 241 miles (71% reduction). This would have resulted in a 71% reduction in CO2 emissions, although the reduction in volume in kilograms of CO2 emitted would have varied (by an estimated 137 Kg CO2 or 51%) depending on whether e.g., a small diesel car was used or a large petrol car (reduction of 133kg versus 270 kg CO2). An average diesel car was estimated to have reduced CO2 emissions by 163 kg. Conclusion The findings demonstrate that potential carbon savings from medicines synchronisation can be modelled from routinely collected data, but with limited accuracy, especially as patients receiving home deliveries may not be representative of all pharmacy users. Challenges remain in how to measure actual carbon savings of prescription synchronisation and overcoming barriers to widespread implementation of clinically appropriate synchronisation.

Review of Road Traffic Accident Burdens in Transportation Sector of Ethiopia

The transportation sector has a very immense role in the economic development of a given country. Because human beings' day-to-day activities re highly depends on the transport service. But this sector has its own drawbacks, like road traffic crashes and environmental pollution. In Ethiopia, the road transport sector is poor in terms of quality and service delivery systems. Due to this, the frequent occurrence of road traffic crashes has been recorded in the country continuously. To that end, this paper was mainly intended to review road traffic accident burdens in the transportation sector of Ethiopia. In detail, reviewing the costs of road traffic accidents, the causing factors of road traffic accidents, the types of vehicles highly involved in road traffic accidents, and the victim groups of communities affected by road traffic accidents at the national level were the specific objectives of this paper. Meanwhile, all road traffic accident data found in Ethiopia from the federal police commission report papers, journal articles, and dissertation papers were used for analysis purposes. Besides, the collected data is presented in tables and figures. Furthermore, the data was analyzed and interpreted based on a descriptive research design. Finally, the result of this review shows that trends of road traffic accidents in Ethiopia have been increasing over time by killing, injuring people and damaging resources. Drivers’ bad behaviors, low know-how of road users, poor road infrastructure, old vehicle conditions, and environmental factors are aggravating the occurrence of road traffic accidents in the country. Thus, to tackle this problem, educating people, enforcing traffic laws, enhancing engineering measures, and giving emergency responses were the recommendations given in this paper.

Intelligent Network-Connected New Energy Vehicle Technology and Application Under the Dual-Carbon Strategy

In responding to the “dual carbon” strategy, intelligent networked new energy vehicle technology plays a crucial role. This type of vehicle combines the advantages of new energy technology and intelligent network technology, effectively reduces carbon emissions in the transportation sector, improves energy utilization efficiency, and contributes to the green transportation system through intelligent transportation management and collaborative work between vehicles, making significant contributions. This article aims to explore the development of intelligent network-connected new energy vehicle technology and applications under the dual-carbon strategy and lay the foundation for the future development direction of the automotive industry.

Smart Driver Monitoring System using Deep Learning

This research introduces a monitoring system that leverages deep learning methodologies, to analyze and interpret facial features to assess the driver’s alertness. With the rapid advancement of technology especially in automobiles this type of monitoring can be employed in all types of vehicles. The proposed system employs deep learning models like YOLOv8 to detect the state of alertness of the driver. This project aims to achieve this by training the deep learning model on a custom dataset with varying background noise to train the model as accurately as possible. Various performance metrics and evaluations will be done to evaluate the accuracy of the model to integrate it into automobiles. This holds great promise for revolutionizing intelligent transportation systems, automotive technologies, regulatory frameworks, public policies, and societal initiatives aimed at improving road safety, driver well-being, and operational excellence on a global scale, ushering in a safer, more efficient, and sustainable transportation ecosystem.

Vehicle Detection and Tracking Techniques Based on Deep Learning in Road Traffic Surveillance

This article discusses the application of deep learning in vehicle detection and tracking technology, elaborating on the basic concepts of deep learning and its advantages in vehicle target detection. Deep learning models such as Convolutional Neural Networks (CNNs) overcome the reliance on manual feature engineering by automatically learning image features. The article focuses on two deep learning detection frameworks, Faster R-CNN and YOLO. The former combines region proposal networks with region classification networks to achieve end-to-end optimization, while the latter transforms the detection task into a regression problem, enabling real-time detection in a single forward pass. Regarding vehicle tracking, the article explores the challenges of multi-object tracking such as occlusion, cross-movement, and the tracking requirements of different vehicle types. Deep learning applications in this field, such as the DeepSORT and Tracktor algorithms, combine CNNs, RNNs, and traditional tracking methods to achieve feature learning, historical state modeling, and probabilistic reasoning. Performance evaluation is discussed in terms of metrics like IoU, precision, recall, and F1 Score, comparing and analyzing the performance of different algorithms in vehicle detection and tracking tasks. Lastly, the article discusses the balance between real-time and accuracy in deep learning-based vehicle detection and tracking technology in road traffic monitoring, as well as its significant role in traffic accident warning and management.

The Importance of an Unmanned Surface Vehicle: A Point of View

In the world there are various types of unmanned vehicles: Unmanned ground vehicle, unmanned aerial vehicle, unmanned surface vehicle and unmanned underwater vehicle. The objective of this paper, however, is to present the importance of an unmanned surface vehicle (USV) in the Black Sea. The safe operations of all USVs are carried out by experienced and qualified operators. These operators can control a USV from a ship or from land. An USV is equipped with various devices (eg. video camera, long range antennae or autopilot box) depending on the mission it has to carry out at sea. These boats are launched through launch and recovery system (LARS). Furthermore, the USVs do not pollute and do not make noise during their activity on sea. Finally, it is expected that the unmanned surface vehicles will further develop, depending on new requirements present in the Black Sea.

Evaluating Road Crash Severity Prediction with Balanced Ensemble Models

This study evaluates the performance of an ensemble of five ML models (Random Forest, XGBoost, AdaBoost, LightGBM and CatBoost) on crash data from New South Wales, Australia. The model is evaluated based on ROC-AUC score, with a result of 0.68, indicating a moderate level of predictive accuracy. Feature importance analysis reveals the key predictors being the vehicle type involved, with sedans/hatchbacks and motorcycles being the most common in fatal crashes, and the collision type, with vehicle-to-object impacts often leading to fatalities. Furthermore, fatal crashes occur more on Saturdays, in country non-urban LGAs and speed limits of 100 km/h as the most usual settings for fatal accidents.