Utility Vehicle Research Articles

Ride Sharing Mobile Application Optimised with Courier Service

The Cab Sharing Mobile Application with Integrated Courier Service merges passenger ride-sharing with parcel delivery to enhance urban transportation and streamline logistics. This dual-function platform maximizes vehicle utilization by enabling both commuter ride-sharing and parcel delivery within a single system. The application pairs passengers traveling in similar directions, facilitating shared rides that lower travel expenses and reduce emissions, which is beneficial for the environment. Additionally, it leverages available space in shared cabs to transport parcels, offering cost-effective, same-day delivery solutions. Through an advanced algorithm, the app efficiently plans routes to balance the needs of passengers and parcel drop-offs, minimizing delays. Key features like real-time tracking, secure payment options, and a feedback mechanism ensure a smooth, reliable experience for users. Index Terms— Cab sharing, Parcel delivery, Eco-friendly transport, Ride-sharing

CI-YOLO: A lightweight foreign object detection model for inspecting transmission line

Timely detection and elimination of foreign objects on transmission lines are of significance for the secure operation of power grids. With the widespread utilization of unmanned aerial vehicles (UAVs) for inspecting transmission lines, how to effectively balance the resource consumption and accuracy of detection model presents a challenge. To tackle this problem, this study proposes a foreign object detection model based on an improved YOLOv8n, realizing both lightweight and enhanced detection performance. Firstly, a SC Block structure is devised to substitute Bottleneck in C2f module of Backbone, minimizing the spatial and channel redundancy among features, enhancing the network performance, and reducing the consumption of computational resources. Subsequently, a novel dual attention mechanism, IEMA, is added after C2f module of Backbone, strengthening the crucial spatial and channel feature expression capability and significantly increasing the detection accuracy. Lastly, a new feature fusion network, CCFF_IEMA, is designed to replace Neck network, effectively integrating detailed features and reducing the parameter count and FLOPs. Experimental results indicate that in comparison to standard YOLOv8n, the parameters of our proposed CI-YOLO model are decreased by 40 %, FLOPs are reduced by 19.8 %, while achieving improvements of 0.7 % in Precision, 3.2 % in Recall, and 3.1 % in mAP0.5, respectively. The CI-YOLO model is beneficial to deployment on UAVs and provides a novel approach for detecting the foreign objects.

A Multi‐Trip Vehicle Routing Problem With Release Dates and Interrelated Periods

ABSTRACTThis article proposes a new multi‐trip vehicle routing problem variant, originally motivated by a practical application, namely a car components distribution to repair centers (warehouses). The problem is named the multi‐trip vehicle routing with release dates and interrelated periods (MTVRP‐RDIP). The routes may start at different pre‐defined periods, called departure periods, and may have different durations. Thus, two routes that start at different periods may be active at the same period, leading to the so‐called interrelated periods. Moreover, a route may only start at a period if a vehicle is available, and the availability of the vehicle depends not only on the existing vehicles but also on the active routes at that time period. The clients are classified according to their importance and represent warehouses that require car components that are made available throughout the time horizon, thus leading to the consideration of release dates. Delays in satisfying client orders and not serving orders in the time horizon result in penalty costs that must be minimized. The objective to minimize also includes routing costs and vehicle utilization costs. Two metrics are defined to compute an order's delay, each leading to a different mixed integer linear model. Computational results showed that one model clearly outperforms the other and even this one is only suited to address the smallest instances. A matheuristic based on a rolling‐horizon process that iteratively solves the better‐performing model with fewer periods was designed to tackle the largest instances. The matheuristic can provide feasible solutions for all test instances in an efficient manner that are, on average, better than the ones provided by the model in most of the compared key performance indicators.

A Low-Cost and Lightweight Real-Time Object-Detection Method Based on UAV Remote Sensing in Transportation Systems

Accurate detection of transportation objects is pivotal for enhancing driving safety and operational efficiency. In the rapidly evolving domain of transportation systems, the utilization of unmanned aerial vehicles (UAVs) for low-altitude detection, leveraging remotely-sensed images and videos, has become increasingly vital. Addressing the growing demands for robust, real-time object-detection capabilities, this study introduces a lightweight, memory-efficient model specifically engineered for the constrained computational and power resources of UAV-embedded platforms. Incorporating the FasterNet-16 backbone, the model significantly enhances feature-processing efficiency, which is essential for real-time applications across diverse UAV operations. A novel multi-scale feature-fusion technique is employed to improve feature utilization while maintaining a compact architecture through passive integration methods. Extensive performance evaluations across various embedded platforms have demonstrated the model’s superior capabilities and robustness in real-time operations, thereby markedly advancing UAV deployment in crucial remote-sensing tasks and improving productivity and safety across multiple domains.

EVs meet health: A complex-systems policy integration approach for electric vehicle adoption and its impact on respiratory and cardiovascular disease

The transition from gasoline-combustion to zero-emission vehicles represents one of the most ambitious public and private enterprises around the globe to mitigate air pollution and its subsequent effects on climate change. The present study uses the rapid transition in the state of California to analyze the connections between the increasing utilization of electric vehicles (EVs) and two critical health outcomes: Asthma and acute myocardial infarction (AMI) emergency department (ED) visits. We fit simultaneous quantile and interquartile-range regression models to health and sociodemographic data from the California Office of Health and Hazard Assessment and electric vehicles registration data, 2011–2017. Results show that changes in EV utilization, from 2011 to 2013 to 2015–2017, have larger and more favorable effects as asthma and AMI deteriorate across zip codes. Looking at zip codes located at the 75th and 25th percentiles of the change in the asthma- and AMI-related ED visits distribution, a 5-percentage points change in the EV-to-gasoline combustion utilization ratio is associated with a 25-percentage points (p<.01) and 12.5-percentage points (p<.01) decline in asthma- and AMI-related ED visits, respectively. Our findings also suggest that, should the U.S. meet the goals of Executive Order N-79-20 by 2035, we could observe about 37,000 fewer asthma-related ED visits (95% CI: 16,154–59,280) per year, with an overall possible annual savings of about $65.5 million dollars (95% CI: $29.1M – $106.7M), and a possible decline of about 19,292 AMI-related hospitalizations, with possible annual savings of about $425 million dollars (95% CI: $365M – $509M). Our findings are optimistic in that spillover effects from increasing EV utilization can reduce healthcare costs while improving population health. Research should point toward the development of public-private coalitional strategies to effectively bridge usually disparate sectors of the economy and policy-making like energy and health.

The role of battery electric vehicles in off-peak hour deliveries: Sustainability assessment of a case study in Stockholm

City logistics faces critical challenges in optimising resource management, utilising time effectively, and reducing costs, particularly in the context of growing environmental concerns. Battery Electric Vehicles (BEVs) and Off-Peak Hour Deliveries (OPHD) have emerged as promising solutions. Despite considerable research efforts, more research is needed to evaluate the impacts of using BEVs during OPHD. To address this need, this paper describes how data from a case study in Stockholm were used to develop a comprehensive overview of the sustainability impacts of OPHD carried out with BEV. A multi-method approach was applied, including an indicator framework, Life Cycle Costing, and a Social Life Cycle Assessment to consider all sustainability dimensions holistically. Our findings reveal that, while BEVs are not yet cost-competitive with Internal Combustion Engine vehicles for daytime operations, OPHD can mitigate these costs by increasing the vehicle utilisation and distances travelled. Furthermore, OPHD with BEV can offer substantial benefits by increasing accessibility and reducing emissions. However, challenges such as increased employee-related transport costs and infrastructure needs must be addressed. The main contributions of this paper are practical, as we add knowledge about the impacts of OPHD using BEV, and methodological, as we used a multi-method approach to holistically assess sustainability impacts. Future research should focus on long-term impacts and refine assessment methods to support the sustainable development of urban logistics. This study highlights the importance of using holistic sustainability assessment to inform and enable stakeholders and decision-makers to leverage the full potential of OPHD when shifting to more sustainable transport.

Assessment of Sustainable Transportation Model Using Energy-Efficient Algorithm

The proliferation and extensive utilization of vehicles have escalated energy use and increased environmental damage. Utilizing large amounts of information on metropolitan traffic patterns can enhance vehicle transportation's environmental and financial aspects. This can be achieved through an efficient decrease in fuel consumption and contaminants. This study aims to present a novel framework for evaluating the Sustainable Transportation Model with Energy-Efficient Algorithm (STM-EE) by utilizing Route Planning Algorithms (RPAs) in a simulated environment. The CARLA simulator was used to compare the widely used conventional Dijkstra technique and an Integrated Genetic Algorithm (IGA) that integrates Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) in different driving scenarios. This study aims to quantify the effects of RPA decisions on the energy consumption of vehicles. The efficiency of comparing the two RPAs has been enhanced by employing an offline energy estimate methodology. The proposed architecture is assessed through EE simulations to demonstrate its efficacy and adaptability. The results support the development of energy-efficient RPA solutions, which contribute to advancing the STM field.

Impact of Vehicle Steering Strategy on the Severity of Pedestrian Head Injury.

In response to the sudden violation of pedestrians crossing the road, intelligent vehicles take into account factors such as the road conditions in the accident zone, traffic rules, and surrounding vehicles' driving status to make emergency evasive decisions. Thus, the collision simulation models for pedestrians and three types of vehicles, i.e., sedans, Sport Utility Vehicles (SUVs), and Multi-Purpose Vehicle (MPVs), are built to investigate the impact of vehicle types, vehicle steering angles, collision speeds, collision positions, and pedestrian orientations on head injuries of pedestrians. The results indicate that the Head Injury Criterion (HIC) value of the head increases with the increase in collision speed. Regarding the steering angles, when a vehicle's steering direction aligns with a pedestrian's position, the pedestrian remains on top of the vehicle's hood for a longer period and moves together with the vehicle after the collision. This effectively reduces head injuries to pedestrians. However, when the vehicle's steering direction is opposite to the pedestrian's position, the pedestrian directly collides with the ground, resulting in higher head injuries. Among them, MPVs cause the most severe injuries, followed by SUVs, and sedans have the least impact. Overall, intelligent vehicles have great potential to reduce head injuries of pedestrians in the event of sudden pedestrian-vehicle collisions by combining with Automatic Emergency Steering (AES) measures. In the future, efforts need to be made to establish an optimized steering strategy and optimize the handling of situations where steering is ineffective or even harmful.

Comparative assessment of global warming potential of gasoline, battery, and hybrid vehicles in India

This study reports a comparative analysis of life cycle greenhouse gas (GHG, CO2-eq.) emissions of three fuel-type vehicles, viz. internal combustion engine (ICEVs), battery electric (BEVs), and hybrid electric vehicles (HEVs), from an Indian perspective. Vehicles in hatchback and compact sports utility vehicles (SUVs) categories were analyzed. GHG emissions in fuel life cycle (well-to-tank and tank-to-wheel) and vehicle life cycle were estimated using data from websites of OEMs and ministries of Government of India, and the GREET software. The vehicle life cycle constitutes a smaller fraction (15–20 %) of total life cycle GHG emissions for ICEVs and HEVs compared to BEVs (∼35 %). The vehicle cycle emissions of BEVs are higher than ICEV and HEV in both categories (52–63 % for hatchback and approx. 45 % for compact SUV). The fuel cycle emissions of BEVs are smaller than ICEV and HEV in both categories (29–34 % for hatchback and 32–40 % for compact SUV). The total life cycle GHG emissions of BEVs were lower than ICEV and HEV (15–17 % in hatchback and 17–25 % in compact SUV category) for the 2023 energy mix in India with a significant share of thermal route. With anticipated doubling of renewable energy sources in India by 2030, the total life cycle GHG emission of BEV will decrease by approximately 20 %. However, sensitivity analysis revealed that BEVs produce higher GHG emissions than ICEVs and HEVs until a minimum lifetime travel distance. Thermal power plant efficiency and battery lifespan also influence BEVs lifetime GHG emissions.

Experimental Evaluation of Damping and Stiffness in Optimized Active Sport Utility Vehicle Suspension Systems

On a flat city road, the specificity of the suspension poses a challenge. Sport utility vehicles (SUVs) have less flexibility compared to regular cars, despite providing a firm grip on asphalt and concrete. Constant adjustments to the center of gravity of SUVs are necessary. Surprisingly, they are less reliable and stable in urban settings due to this characteristic. In this study, a mathematical model of the suspension system of SUVs based on the Newtonian approach is introduced and validated with data from experiments carried out by the MTS machine system on the mono-tube (oil/air) mixed damper element. The model accurately predicts the performance of this damper, commonly used in SUVs, across various operating conditions, including different frequencies. A coil spring element, serving as a passive suspension unit with this damper, is also tested experimentally under similar conditions. By integrating passive suspension elements with the active actuator, the proposed modified design reduces the power consumption needed for the actuator to function and ensures a certain level of reliability. The effectiveness and performance of the modified active suspension system in comparison to the traditional passive suspension system are assessed using three different strategies: hybrid PID-LQR, linear quadratic regulator (LQR), and proportional-integral-derivative (PID). The genetic algorithm is utilized to determine the optimal parameter values for each controller by minimizing a cost function, maximizing performance, and minimizing energy consumption. Simulation results demonstrate that the active suspension system controlled by the PID-LQR controller offers significantly improved ride comfort and vehicle stability compared to other systems. This suggests that the performance of the active suspension system is greatly enhanced by the combined application of the hybrid PID-LQR controller compared to other systems.

Validation of a generic finite element vehicle buck model for near-side crashes

Objective Finite element (FE) reconstructions of motor vehicle crashes using human body models are effective tools for developing a better understanding of occupant kinematics and injuries in real-world lateral crash conditions, but current near-side reconstruction methods are limited by the paucity of full-scale FE vehicle models. The objective of this study was to validate a generic vehicle model equipped with left-side airbags and intrusion capability by simulating a series of near-side crash tests for a range of vehicles and assessing model accuracy using objective evaluation methods. Methods Moving deformable barrier crash tests were reconstructed for five common vehicle classifications (compact passenger, mid-size passenger, sport utility vehicle, pickup truck, and van) using an updated version of a previously developed simplified vehicle model. Unknown vehicle and intrusion properties (pretensioner force, seatback airbag pressure, curtain airbag pressure, door panel stiffness, ratio of dynamic-to-static intrusion, intrusion velocity, and intrusion scaling factor) were estimated by parameterizing them across 224 simulations per crash test using a Latin hypercube design of experiments. Model accuracy was assessed for 13 anthropomorphic test device signals using the Correlation and Analysis (CORA) objective rating method and injury metric comparisons. Results Maximum ratings of 0.69, 0.67, 0.52, 0.52, and 0.62 were achieved for the compact passenger, midsize passenger, sport utility vehicle, pickup truck, and van classifications, respectively. On average, the abdomen displayed the most accurate behavior (0.51 ± 0.12), followed by the thorax (0.50 ± 0.10) and head (0.50 ± 0.07). The pelvis displayed the least accurate behavior (0.46 ± 0.18) of any region. Reconstructions overpraedicted injury metrics in all cases. Conclusions All vehicles achieved “fair” biofidelity ratings and the compact passenger and midsize passenger vehicles achieved “good” biofidelity ratings, validating them for kinematic evaluations with vehicle-to-vehicle nearside crash reconstructions. Regression models were developed for injuries and CORA ratings and can be used to optimize vehicle parameters in future studies.

Optimization of Operating Conditions for Battery Thermal Management System

With the rapid increase in the number of vehicles worldwide, we are currently experiencing a scarcity of nonrenewable energy, such as fuel, and the resulting environmental risks associated with vehicle utilization [...]

Adaptive dynamic programming–based fault-tolerant control of multi-UAV formation system

With the increasing utilization of unmanned aerial vehicle (UAV) swarms in civilian and military field applications, it is crucial to eliminate the potential impacts of UAV faults on task completion and efficiency. To address this issue, the present paper investigates a fault-tolerant control scheme based on the adaptive dynamic programming (ADP) method and fault observer. The whole closed-loop system is composed of a position loop and an attitude loop. An ADP-based position-tracking controller is established with an improved cost function that can simultaneously represent actuator faults, control inputs, and tracking errors. In addition, a single-layer critic neural network is constructed to estimate the cost function and approximate control inputs. In the process of adjusting the weights of the neural network, a unique adjustment method combining policy gradient and prioritized experience replay is adopted to improve the data usage efficiency and speed up the weight convergence. Furthermore, a sliding mode–based attitude controller is utilized for the inner-loop attitude subsystem. The significant feature of this scheme is that it has the ability to self-learn and self-adapt. A Lyapunov stability analysis is performed to verify whether the signals are uniformly ultimately bounded. Finally, simulation experiments are conducted to demonstrate the effectiveness of the proposed scheme.

Investigating factors affecting injury severity of single-vehicle run-off-road crashes

This study aimed to identify and investigate the contributing factors influencing injury severity in single-vehicle run-off-road (ROR) crashes, which are known for their high severity. The primary objective was to analyze and compare the impact of these factors across three distinct vehicle classes: passenger cars, sport utility vehicles (SUVs), and pickups. A mixed logit model with heterogeneity in mean and variance was developed to analyze the injury severity outcomes in ROR crashes for the three vehicle classes. The model accounted for the potential variations in the impact of contributing factors across different vehicle types. The study revealed several significant variables consistently influencing injury severity across all three vehicle classes. These included driver age, alcohol or drug usage, seatbelt utilization, airbag deployment, higher travel speeds, and the vehicle model year post-2010. Notably, as driver age increased, the impact on changes in injury severity outcomes was more pronounced for pickup drivers compared to those operating passenger cars and SUVs. Among the common findings was the highly effective role of seatbelt usage in mitigating injury severity in ROR crashes. Additionally, passenger cars were associated with increased injury severity, particularly at relatively higher travel speeds exceeding 75 mph when contrasted with SUVs and pickups traveling between 61 and 75 mph. The study highlights the importance of considering vehicle class-specific factors in analyzing injury severity in ROR crashes. Recommendations include further in-depth investigations into distinct factors contributing to injury severity within each vehicle class and utilizing more extensive crash datasets to gain additional insights for enhancing road safety.

Learning to enhance areal video captioning with visual question answering

ABSTRACT The utilization of Unmanned Aerial Vehicles (UAV) in remote sensing (RS) has witnessed a significant surge, offering valuable insights into Earth dynamics and human activities. However, this has led to a substantial increase in the volume of video data, rendering manual screening and analysis impractical. Consequently, there is a pressing need for the development of automated interpretation models for these aerial videos. In this paper, we propose a novel approach that leverages visual dialogue to enhance aerial video captioning. Our model adopts an encoder-decoder architecture, integrating a Visual Question Answering (VQA) task before the captioning task. The VQA task aims to enrich the captioning process by soliciting additional information about the image content. Specifically, our video encoder utilizes ViT-L/16, while the decoder employs Generative Pre-trained Transformer-2 (Distill-GPT-2). To validate our model, we introduce a novel benchmark dataset named CapERA-VQA, comprising videos accompanied by sets of questions, answers, and captions. Through experimental validation, we demonstrate the effectiveness of our proposed approach in enhancing the automated captioning of aerial videos.

A mixed-methods examination of fixed-object crashes among electric utility company fleet vehicles

IntroductionOne of the most common and serious types of crashes among all motor-vehicle users involves collisions with fixed objects. This type of crash occurs frequently among utility vehicle workers while driving for work. The overarching objective of this research was to improve the safety of electric utility company vehicle operators by determining the circumstances under which utility vehicles are involved in crashes with fixed objects and to provide recommendations to help drivers and utility fleet company management prevent these types of crashes. MethodsThe study incorporated a mixed methods approach and gathered information from structured interviews with safety managers at electric power companies and an analysis of utility vehicle crashes using data supplied by electric power companies and a statewide database in Michigan. ResultsMany factors were found to contribute to fixed-object crashes including engagement in secondary tasks and job productivity pressure. Information was organized and evaluated to develop 11 recommendations for countermeasure strategies that are directed at various levels within the system in which the drivers operate. DiscussionThis study was the first to explore fixed-object crashes among electric utility fleet vehicles. The findings are unique in that they provide insight into the safety practices in an industry that has received limited academic attention and highlight the need to place greater emphasis on road safety practices in the workplace. Practical applicationCountermeasures generated for this study are applicable to companies in which fleet driver safety is a primary concern.

Scheduling of Collaborative Vegetable Harvesters and Harvest-Aid Vehicles on Farms

Transporting harvested vegetables in the field or greenhouse is labor-intensive. The utilization of small harvest-aid vehicles can reduce non-productive time for farmers and improve harvest efficiency. This paper models the process of harvesting vegetables in response to non-productive waiting delays caused by the scheduling of harvest-aid vehicles. Taking into consideration harvesting speed, harvest-aid vehicle capacity, and scheduling conflicts, a harvest-aid vehicle scheduling model is constructed to minimize non-production waiting time and coordination costs. Subsequently, to meet the collaborative needs of harvesters, this paper develops a discrete multi-objective Jaya optimization algorithm (DMO-Jaya), which combines an opposition-based learning mechanism and a long-term memory library to obtain scheduling schemes suitable for agricultural environments. Experiments show that the studied model can schedule harvest-aid vehicles without conflicts. Compared to the NSGA-II algorithm and the MMOPSO, the DMO-Jaya algorithm demonstrates a better diversity of solutions, resulting in a shorter non-productive waiting time for harvesters. This research provides a reference model for improving the efficiency of vegetable harvesting and transportation.

Utilization of Unmanned Aerial Vehicle for Monitoring and Surveillance of Aquaculture Farms: A Proposed Framework

Abstract: In this paper, unmanned aerial vehicles (UAVs) for monitoring and surveillance within aquaculture farms situated in Iloilo, Philippines are investigated. The principal aim is to create a framework that can provide guidance on how to use UAVs for effective monitoring, data collection, and regulatory control within the aquaculture sector under the supervision of BFAR – Department of Agriculture. The study examines the performance of UAVs in real-time surveillance mapping as well as geotagging and geo-referencing that would help in observing fish behavior and environmental settings. The research assesses the precision of UAVs in executing these assignments and compares various models based on their effectiveness in monitoring perimeters environmental data collecting and following changes in fish behavior. Findings reveal that UAVs especially those with high detection precision levels and advanced mapping capabilities may be good tools for improving management practices on aquaculture farms. The paper suggests some ways we may adopt UAV technology within aquaculture emphasizing its potential to minimize operational costs while at the same time increasing productivity. With the help of these findings, it is possible to develop initial guidelines for UAV use in the aquaculture industry that will serve as a basis for a regulatory framework ensuring the sustainable and efficient functioning of aquaculture farms. This framework seeks to support local government initiatives in enhancing farm monitoring, protecting the environment and managing resources by using advanced UAV technology.

Forensic entomology in homicide cases: study of a corpse found inside a buried vehicle.

Forensic entomology plays a crucial role in death investigations, particularly in estimating the postmortem interval (PMI). This study presents a forensic entomology case involving a corpse found in a buried utility vehicle. The victim was in an advanced state of decomposition, with autopsy findings revealing gunshot wounds. Cadaveric fauna was collected at the scene and during the autopsy. The analysis revealed a diverse insect community, with predominance of Compsomyiops fulvicrura and Piophila casei. The time of development of species like Dermestes maculatus and Necrobia rufipes was used to estimate the minimum PMI. The presence and low abundance of Calliphora vicina, a species preferring lower temperatures, shed light on the seasonal conditions at the time of death and suggested possible body concealment shortly after death. This research is the first to report insects as evidence in a corpse found in a buried vehicle and contributes to the body of knowledge in forensic entomology. The study also suggests that the use of entomological evidence can provide additional information about the season in which the body was concealed, making it a valuable tool in death investigation and crime scene reconstruction. Finally, it emphasizes the need for proper sampling, expert identification, and close collaboration between forensic entomologists and pathologists.

Selection of drive system and chassis structure basic design and analysis for medium-sized urban electric bus

One of the alternative solutions to address environmental and energy challenges is the utilization of electric vehicles, particularly in the domain of public transportation. The focus was on the development of medium-sized electric buses to expand the reach of electric-powered public transport. The design process encompasses the selection of vehicle components, such as the drive system, and the configuration of a space frame chassis structure. The drive system was meticulously chosen and proved to exceed the design requirements, as it achieved a gradability of 12.44%, surpassing the targeted capability of 10% slope. Moreover, it boasts a maximum speed of 108 km/h, exceeding the design requirement of a maximum speed of 50 km/h, and can accelerate at a rate of 1-2 m/s². The chassis design adheres to regulations and standards and is grounded in the strength, stiffness, and natural frequency criteria. The initial chassis design did not meet the design requirement, however, through several iterations, a chassis structure was achieved with a vertical bending stiffness of 8.57 kN/mm and a torsional stiffness of 9.63 kNm/°. Based on the outcomes of this research, a drive system and chassis structure design that fully satisfies all the existing design requirements has been successfully attained.