Mobile Vehicle Research Articles

Predicting unsaturated soil strength of coarse-grained soils for mobility assessments

Accurately estimating surficial soil moisture and strength is integral to determining vehicle mobility and is especially important over large spatial extents at fine resolutions. While methods exist to estimate soil strength across landscapes, they are empirical and rely on class average soil behavior. The Strength of Surficial Soils (STRESS) model was developed to estimate moisture-variable soil strength with a physics-based approach. However, there is a lack of field data from a diverse landscape to evaluate the STRESS model. To test the STRESS model, a field study was conducted in northern Colorado. Soil moisture and strength were measured on 10 dates. Data from the surficial layer (0–5 cm) were used to test the STRESS model and determine if soil strength trends could be estimated from topographical and soil textural differences. High variability was observed in soil strength measurements stemming from fine-scale terrain features and user variability. Observations show lower soil strengths for greater soil moistures, steeper slopes, higher vegetation, and lower soil fines content. STRESS estimated rating cone index values with a mean relative error of 37.5 %, while a pre-existing model had a mean relative error of 47.4 %. The STRESS model outperforms the current RCI prediction method, but uncertainty remains large.

Developing a model for measuring the spatial disability level of cities within the framework of barrier-free pedestrian accessibility: the case of Kırklareli City centre

The problem of disability is directly related to the physical barriers encountered in urban spaces. Pedestrian accessibility performance is a notable urban quality indicator. Universal design principles and barrier-free design standards are important in making urban spaces usable for everyone. However, projects in this context generally cover specific areas of cities. Bringing an entire city into compliance with barrier-free design standards requires high cost and time. Therefore, determining the spatial disability level of the urban texture and planning project stages constitute a critical process for local governments. This study proposes a model to measure the pedestrian accessibility performance of medium-sized cities with relatively walkable distances within the framework of spatial barriers and to identify priority intervention points. In this context, Kırklareli city centre was selected as a case study area. According to the model developed with the support of GIS, the “ideal accessibility network” is determined for the citizens, and the performance level is calculated by identifying the spatial barriers on this network. The model was developed using components that can be applied to any city and tested in a sample urban environment. This conceptual model contributes to urban science and local government policy by providing a monitoring mechanism for spatial disability that can be constantly tracked by citizens, and by supplying an information base for projects to be developed by local governments. It is hoped that this study will popularise pedestrian-oriented spatial design and control in cities where walkability is postponed due to the focus on wheeled vehicle mobility and where spatial barriers are felt only by disabled individuals.

Attribute annotation and bias evaluation in visual datasets for autonomous driving

This paper addresses the often overlooked issue of fairness in the autonomous driving domain, particularly in vision-based perception and prediction systems, which play a pivotal role in the overall functioning of Autonomous Vehicles (AVs). We focus our analysis on biases present in some of the most commonly used visual datasets for training person and vehicle detection systems. We introduce an annotation methodology and a specialised annotation tool, both designed to annotate protected attributes of agents in visual datasets. We validate our methodology through an inter-rater agreement analysis and provide the distribution of attributes across all datasets. These include annotations for the attributes age, sex, skin tone, group, and means of transport for more than 90K people, as well as vehicle type, colour, and car type for over 50K vehicles. Generally, diversity is very low for most attributes, with some groups, such as children, wheelchair users, or personal mobility vehicle users, being extremely underrepresented in the analysed datasets. The study contributes significantly to efforts to consider fairness in the evaluation of perception and prediction systems for AVs. This paper follows reproducibility principles. The annotation tool, scripts and the annotated attributes can be accessed publicly at https://github.com/ec-jrc/humaint_annotator.

Optimizing Performance in Wireless Sensor Networks through a Multi-Objective Rendezvous Points Selection Algorithm

Wireless sensor networks (WSNs) play a vital role in modern research and applications due to their potential to gather data from various environments. Because sensor nodes (SNs) within WSNs have limited battery life, those in close proximity to the sink often experience rapid power depletion, leading to the emergence of hotspot issues. To address this, the concept of a mobile sink (MS) has emerged as a potential solution, effectively mitigating power usage in SNs and thereby extending the network’s overall lifespan. Furthermore, many sensor-based applications necessitate specific data collection timeframes, underscoring the necessity of effective strategies. Leveraging rendezvous points (RPs) to enhance network efficiency becomes imperative in enabling the MS to efficiently collect data from all SNs within designated time periods. A sophisticated cost function is employed to strategically determine RPs, considering multiple factors that influence the efficacy of each RP. This process culminates in the selection of RPs, optimizing for the longest path with minimal delays. Through the proposed hybrid mobile vehicle (HMV) method, compared against the prevailing MOOVor method, significant enhancements are observed in terms of sensor coverage and reduced hop count within the network.

Jointly Optimization of Delay and Energy Consumption for Multi-Device FDMA in WPT-MEC System.

With the rapid development of mobile edge computing (MEC) and wireless power transfer (WPT) technologies, the MEC-WPT system makes it possible to provide high-quality data processing services for end users. However, in a real-world WPT-MEC system, the channel gain decreases with the transmission distance, leading to "double near and far effect" in the joint transmission of wireless energy and data, which affects the quality of the data processing service for end users. Consequently, it is essential to design a reasonable system model to overcome the "double near and far effect" and make reasonable scheduling of multi-dimensional resources such as energy, communication and computing to guarantee high-quality data processing services. First, this paper designs a relay collaboration WPT-MEC resource scheduling model to improve wireless energy utilization efficiency. The optimization goal is to minimize the normalization of the total communication delay and total energy consumption while meeting multiple resource constraints. Second, this paper imports a BK-means algorithm to complete the end terminals cluster to guarantee effective energy reception and adapts the whale optimization algorithm with adaptive mechanism (AWOA) for mobile vehicle path-planning to reduce energy waste. Third, this paper proposes an immune differential enhanced deep deterministic policy gradient (IDDPG) algorithm to realize efficient resource scheduling of multiple resources and minimize the optimization goal. Finally, simulation experiments are carried out on different data, and the simulation results prove the validity of the designed scheduling model and proposed IDDPG.

Review Research paper on Highly Energy Efficient Aerodynamic Shape for Flying Cars

The reviewed research paper presents an analysis of the aerodynamic design for flying cars, focusing on optimizing shape to achieve high energy efficiency. Given the increasing interest in urban air mobility (UAM) and eVTOL (electric vertical take-off and landing) vehicles, energy efficiency is critical for extending range, minimizing power consumption, and ensuring safe operations. The paper explores how specific aerodynamic principles can be applied to flying cars to minimize drag, reduce energy consumption, and enhance flight stability.

Next-cell prediction with LSTM based on vehicle mobility for 5G mc-IoT slices

Next-cell prediction with LSTM based on vehicle mobility for 5G mc-IoT slices

Dependency-aware online task offloading based on deep reinforcement learning for IoV

The convergence of artificial intelligence and in-vehicle wireless communication technologies, promises to fulfill the pressing communication needs of the Internet of Vehicles (IoV) while promoting the development of vehicle applications. However, making real-time dependency-aware task offloading decisions is difficult due to the high mobility of vehicles and the dynamic nature of the network environment. This leads to additional application computation time and energy consumption, increasing the risk of offloading failures for computationally intensive and latency-sensitive applications. In this paper, an offloading strategy for vehicle applications that jointly considers latency and energy consumption in the base station cooperative computing model is proposed. Firstly, we establish a collaborative offloading model involving multiple vehicles, multiple base stations, and multiple edge servers. Transferring vehicular applications to the application queue of edge servers and prioritizing them based on their completion deadlines. Secondly, each vehicular application is modeled as a directed acyclic graph (DAG) task with data dependency relationships. Subsequently, we propose a task offloading method based on task dependency awareness in deep reinforcement learning (DAG-DQN). Tasks are assigned to edge servers at different base stations, and edge servers collaborate to process tasks, minimizing vehicle application completion time and reducing edge server energy consumption. Finally, simulation results show that compared with the heuristic method, our proposed DAG-DQN method reduces task completion time by 16%, reduces system energy consumption by 19%, and improves decision-making efficiency by 70%.

Vehicular edge cloud computing content caching optimization solution based on content prediction and deep reinforcement learning

In conventional studies on vehicular edge computing, researchers frequently overlook the high-speed mobility of vehicles and the dynamic nature of the vehicular edge environment. Moreover, when employing deep reinforcement learning to address vehicular edge challenges, insufficient attention is given to the potential issue of the algorithm converging to a local optimal solution. This paper presents a content caching solution tailored for vehicular edge cloud computing, integrating content prediction and deep reinforcement learning techniques. Given the swift mobility of vehicles and the ever-changing nature of the vehicular edge environment, the study proposes a content prediction model based on Informer. Leveraging the Informer prediction model, the system anticipates the vehicular edge environment dynamics, thereby informing the caching of vehicle task content. Acknowledging the diverse time scales involved in policy decisions such as content updating, vehicle scheduling, and bandwidth allocation, the paper advocates a dual time-scale Markov modeling approach. Moreover, to address the local optimality issue inherent in the A3C algorithm, an enhanced A3C algorithm is introduced, incorporating an ɛ-greedy strategy to promote exploration. Recognizing the potential limitations posed by a fixed exploration rate ɛ, a dynamic baseline mechanism is proposed for updating ɛ dynamically. Experimental findings demonstrate that compared to alternative content caching approaches, the proposed vehicle edge computing content caching solution substantially mitigates content access costs. To support research in this area, we have publicly released the source code and pre-trained models at https://github.com/JYAyyyyyy/Informer.git.

Exploring the role of configurational accessibility of alleyways on facilitating wayfinding transportation within the organic street network systems

Despite the significant role of alleyways in urban spatial configuration and accessibility as critical structures of urban infrastructure, there remains a noticeable gap in understanding their specific role in urban transportation policies. The main aim of the present study is to investigate the role of configurational accessibility of alleyways in facilitating wayfinding within organic street network systems. A mixed method was applied using Space Syntax and empirical observations. Moreover, Pearson correlations and t-tests were used to discern the association between variables and compare independent groups using SPSS software. The findings from the syntactical analysis strongly indicate that alleyways significantly impact configurational accessibility within urban network systems. Furthermore, the complex and labyrinthine structure of organic alleyways was found to significantly hinder the intelligibility of urban areas, making navigation more challenging. The results revealed a significant correlation between vehicle mobility and connectivity values, with higher levels of mobility consistently linked to increased connectivity values. The outcomes also indicated that alleys connected to main streets play a crucial role in facilitating wayfinding within urban network systems. This study contributes to a more profound understanding of the role of alleyways in urban network systems for urban planners and transportation engineers, paving the way for more targeted sustainable urban planning strategies.

Numerical study on the forward and inverse problems of the mobile pump truck frame

Aiming at the requirements of strong mobility and high flexibility of rescue and relief mobile pump trucks, this paper designs a new type of mobile pump truck frame based on existing mobile vehicle frame models. The materials used for the frame are 40Cr and Q235, and the finite element method is utilized to carry out static mechanical analysis and dynamic characteristic analysis. Simultaneously utilizing topology optimization and multi-objective genetic algorithm to optimize the design of the frame structure. The results show that the optimized pump truck frame can meet the strength design requirements of four typical working conditions: full load bending, full load torsion, emergency turning and emergency braking, while avoiding resonance phenomena caused by road surface and diesel engine vibration. Compared with the original frame model, the weight of the optimized frame is reduced by 87.88 kg, with a weight reduction rate of 10.89%, realizing the lightweight design requirements.

Stratified analysis of risk factors affecting vasovagal reactions in first-time whole blood donors: A regional multi-center donor hemovigilance data study

Background and objectivesVasovagal reactions (VVR) are the most common adverse reactions in blood donation. This study aimed to provide and analyze the data of the regional hemovigilance system in Chongqing, China from 2020 to 2022, report the prevalence and explore the risk factors of VVR. Materials and methodsR software (version 4.2.3) was used for all statistical analyses. Frequency and composition rates were used to describe the data of total donation, as well as data on the different types of ADR. The Chi-square test was used to analyze risk factors for VVR and inter-group comparisons of VVR stratified by gender (female/male), age (18–22; 23–29; 30–39; 40–49; 50–60) and season(Spring (Mar.-May.); Summer (Jun.-Aug.); Autumn (sep.-Nov.); Winter (Dec.-Feb.)). ResultsThe reported incidence rate of VVR was 8.69‰ during whole blood donations and 1.02‰ during platelet-apheresis donations.The stratified analysis revealed that female donors aged 18–22 years old and 30–39 years old were reported to have lower VVR rates than male donors, oppositely, higher in females in 50–60 years old. Statistically significant differences in the incidence rate of VVR were observed between winter and summer, and between winter and spring in 18–49 years old. No seasonal variation was found in 50–60 years old. ConclusionsThe reported incidence rate of VVR related to blood donation was very low and varied from those calculated by other haemovigilance systems. The higher prevalence of VVR in young, first-time donors, college students, donating in mobile vehicles, males and in spring. Among first-time donors, the prevalence of VVR was higher in males than in females; and stratified analysis revealed there were seasonal variation and gender differences within a same age group.

Agent-Based Modeling for Sustainable Urban Passenger Vehicle Mobility: A Case of Tehran

In response to escalating congestion and deteriorating air quality in urban centers worldwide, exacerbated by overburdened transportation systems, there is an urgent need for accurate traffic forecasting and effective sustainable urban development strategies. This study employs agent-based modeling through four distinctive scenarios for Tehran, I. R. Iran. A synthetic population is meticulously crafted using simulated annealing, enabling the emulation of daily commuting patterns. Results show that by bolstering cycling infrastructure and enhancing public transportation services, reliance on private cars is reduced up to 46%. The introduction of flexible working hours reduces the traffic volumes during peak traffic hours by 47% and significantly altering the daily distances traveled by personal cars, as evidenced by a 1:6 ratio in car volume increase between scenarios emphasizing flexible working hours and those with more conventional traffic patterns. The results provide powerful insights for decisionmakers to manage the traffic especially in high polluted air conditions.

A comparison of tonal-broadband decomposition algorithms for propeller noise

The usage of propellers is increasing as an alternative to traditional aircraft and has become the primary option for new urban mobility vehicles. However, noise emissions from rotating blades can be a challenge for the certification of these new vehicles. The acoustic sources from a propeller can be decomposed into deterministic and random components. Therefore, academic research has focused on developing methods to separate these components. This study compares the performance of several methods available in the literature to decompose propeller noise signals into tonal and broadband components. Initially, the methods were applied to synthetic data that represents the main characteristics of rotating blade noise. Later, the methods were tested on realistic acoustic data collected from an isolated propeller test rig. The results showed that methods based on phase-averaging recorded signals satisfactorily detected harmonic components. The method based on cross-correlation between data blocks effectively extracted the broadband spectra, but it performed poorly on harmonic detection. Additionally, the wavelet transform and Proper Orthogonal Decomposition methods were capable of reconstructing time series related to both deterministic and random parts. However, further investigation is required regarding the separation of POD modes, as unexpected trends were observed with the wavelet-based method.

Modeling and Performance Study of Vehicle-to-Infrastructure Visible Light Communication System for Mountain Roads.

Visible light communication (VLC) is considered to be a promising technology for realizing intelligent transportation systems (ITSs) and solving traffic safety problems. Due to the complex and changing environment and the influence of weather and other aspects, there are many problems in channel modeling and performance analysis of vehicular VLC. Unlike existing studies, this study proposes a practical vehicle-to-infrastructure (V2I) VLC propagation model for a typical mountain road. The model consists of both line-of-sight (LOS) and non-line-of-sight (NLOS) links. In the proposed model, the effects of vehicle mobility and weather conditions are considered. To analyze the impact of the considered propagation characteristics on the system, closed-form expressions for several performance metrics were derived, including average path loss, received power, channel capacity, and outage probability. Furthermore, to verify the accuracy of the derived theoretical expressions, simulation results were presented and analyzed in detail. The results indicate that, considering the LOS link and when the vehicle is 50 m away from the infrastructure, the difference in channel gain between moderate fog and dense fog versus clear weather conditions is 1.8 dB and 3 dB, respectively. In addition, the maximum difference in total received optical power between dense fog conditions and clear weather conditions can reach 76.2%. Moreover, under clear weather conditions, the channel capacity when vehicles are 40 m away from infrastructure is about 98.9% lower than when they are 10 m away. Additionally, the outage probability shows a high correlation with the threshold data transmission rate. Therefore, the considered propagation characteristics have a significant impact on the performance of V2I-VLC.

Mobility-aware parallel offloading and resource allocation scheme for vehicular edge computing

Vehicle edge computing (VEC) enhances the distributed task processing capability within intelligent vehicle-infrastructure cooperative systems (i-VICS) by deploying servers at the network edge. However, the proliferation of onboard sensors and the continual emergence of new applications have exacerbated the inadequacy of wireless spectrum resources and edge server resources, while the high mobility of vehicles reduces reliability in task processing, resulting in increased communication and task processing delays. To address these challenges, we propose a mobile-aware Many-to-Many Parallel (MTMP) offloading scheme that integrates: a) millimeter-wave (mmWave) and cellular vehicle-to-everything (C-V2X) to mitigate excessive communication delays; and b) leveraging the underutilized resources of surrounding vehicles and parallel offloading to mitigate excessive task processing delays. To minimize the average completion delay of all tasks, this paper formulates the objective as a min-max optimization problem and solves it using the maximum entropy method (MEM), the Lagrange multiplier method, and an iterative algorithm. Extensive experimental results demonstrate the superior performance of the proposed scheme in comparison with other baseline algorithms. Specifically, our proposal achieves a 47 % reduction in task completion delay under optimal conditions, a 31.3 % increase in task completion rate, and a 30 % decrease in program runtime compared to the worst-performing algorithm.

Pedestrian’s Avoidance Behavior Characteristics Against Autonomous Personal Mobility Vehicles for Smooth Avoidance

Autonomous personal mobility vehicles (PMVs), such as electric wheelchairs, are meant to drive through pedestrian spaces. Cooperative pedestrian avoidance by PMVs is necessary in these spaces to maintain smooth traffic. Previous studies suggested that PMVs can avoid pedestrians on a shorter path without compromising each other’s acceptability. This avoidance can be realized by understanding how pedestrians react to the behavior of PMVs and considering those characteristics in the autonomous navigation of PMVs. In this study, the characteristics of pedestrian’s avoidance behavior were investigated. Experiments were conducted to understand the influence of the parameters of the PMV’s avoidance behavior on pedestrians. Results showed that the angular velocity of the PMV during avoidance affects the pedestrian’s avoidance width and tolerance against the PMV’s behavior. These results suggest that it is possible to avoid pedestrians in smaller avoidance spaces by controlling the angular velocity of the PMV and maintaining smooth traffic.

Proposed Supercluster-Based UMBBFS Routing Protocol for Emergency Message Dissemination in Edge-RSU for 5G VANET

Vehicular ad hoc networks (VANETs) can bolster road safety through the proactive dissemination of emergency messages (EMs) among vehicles, effectively reducing the occurrence of traffic-related accidents. It is difficult to transmit EMs quickly and reliably due to the high-speed mobility of VANET and the attenuation of the wireless signal. However, poor network design and high vehicle mobility are the two most difficult problems that affect VANET’s network performance. The real-time traffic situation and network dependability will also be significantly impacted by route selection and message delivery. Many of the current works have undergone studies focused on forwarder selection and message transmission to address these problems. However, these earlier approaches, while effective in forwarder selection and routing, have overlooked the critical aspects of communication overhead and excessive energy consumption, resulting in transmission delays. To address the prevailing challenges, the proposed solutions use edge computing to process and analyze data locally from surrounding cars and infrastructure. EDGE-RSUs are positioned by the side of the road. In intelligent transportation systems, this lowers latency and enhances real-time decision-making by employing proficient forwarder selection techniques and optimizing the dissemination of EMs. In the context of 5G-enabled VANET, this paper introduces a novel routing protocol, namely, the supercluster-based urban multi-hop broadcast and best forwarder selection protocol (UMB-BFS). The improved twin delay deep deterministic policy gradient (IT3DPG) method is used to select the target region for emergency message distribution after route selection. Clustering is conducted using modified density peak clustering (MDPC). Improved firefly optimization (IFO) is used for optimal path selection. In this way, all emergency messages are quickly disseminated to multiple directions and also manage the traffic in VANET. Finally, we plotted graphs for the following metrics: throughput (3.9 kbps), end-to-end delay (70), coverage (90%), packet delivery ratio (98%), packet received (12.75 k), and transmission delay (57 ms). Our approach’s performance is examined using numerical analysis, demonstrating that it performs better than the current methodologies across all measures.

Health benefits of air pollution control policies targeting mobile vehicles and electricity generating units

Health benefits of air pollution control policies targeting mobile vehicles and electricity generating units

Prelithiated Silicon Anodes for High-Energy and High-Power Lithium-Ion Batteries

Lithium-ion batteries (LIBs) are pivotal in powering modern electronic devices and next-generation land and air electric vehicles. Silicon-based anodes are considered the next technology to replace traditional graphite anodes due to their high theoretical capacity. However, the inherent challenges associated with their large volume expansion and high irreversible capacity loss (IRCL) limit their practical application. This abstract explores our patented strategy of prelithiation as a viable approach to mitigate the IRCL limitations of SiOx dominant anodes. Prelithiation involves the precise addition of supplemental lithium into the anode, mitigating the adverse effects of initial lithiation and enhancing overall cyclability, energy, power, and extreme fast charge capability for silicon oxide dominant LIBs. Various prelithiation techniques, including Li powder, Li slurry, Li-foil, and the chemical doping of SiOx are examined to evaluate their effectiveness in providing supplemental lithium. The effectiveness of the prelithiation process is assessed by examining the impact on the structural stability, cycling performance, power capability, and rate capability of SiOx dominant anodes integrated into high-capacity (~30 Ah) pouch cells. Our focus is to emphasize prelithiation as a practical and scalable solution to enhance the electrochemical performance of SiOx dominant anodes, contributing to the development of high-energy and high-power LIBs enabling advanced air mobility and high-performance electric vehicles. The findings presented in this abstract contribute to the growing body of knowledge aimed at advancing the fundamental understanding and practical implementation of prelithiation as an enabler for SiOx-based anodes in LIBs. Figure 1