Search Results (803)

Peer-to-peer car-sharing systems are an evolving branch of urban mobility, aligning with global trends focused on sustainable development and reducing congestion in cities. A research gap has been identified concerning the specific vehicle attributes that would encourage the public to potentially use these services. Addressing this gap, and in the context of launching a new peer-to-peer car-sharing service in Katowice, Poland, this article investigates the technical features influencing the choice of vehicles in peer-to-peer car-sharing systems, particularly from the perspective of individuals who currently do not use such platforms. The study employs Social Network Analysis (SNA) to examine the interrelationships between vehicle attributes. The analysis reveals that key factors influencing users’ decisions include fuel/energy consumption, safety features, and technological advancement, with a particular emphasis on driver assistance systems, including autonomous driving capabilities. The network structure, characterized by a relatively low density (0.2536) and a short average path length (1.872), suggests that a few central vehicle features dominate user decisions, and improvements in these key areas can quickly propagate through the decision-making process, enhancing overall user satisfaction. To validate the findings, a Gradient Boosting Regression (GBR) analysis was conducted, confirming the significance of the key factors identified by the SNA, such as fuel efficiency, battery capacity, and safety systems, thus strengthening the reliability of the results. This study underscores the growing importance of sustainability and technological innovation in the automotive industry, particularly in the context of the sharing economy. It suggests that car-sharing platforms and vehicle manufacturers should prioritize these features to meet user expectations and preferences. These findings provide valuable insights for the strategic and operational management of peer-to-peer car-sharing services, emphasizing the importance of targeted vehicle selection and user-centered innovations to improve platform performance and scalability. Full article

The green vision rate of rural highway greening landscape is a key factor affecting the driver’s visual load. Based on this, this paper uses the eye tracking method to study the visual characteristics of drivers in different green vision environments on rural highways in Xianning County. Based on the HSV color space model, this paper obtains four sections of rural highway with a green vision rate of 10~20%, green vision rate of 20~30%, green vision rate of 30~40%, and green vision rate of 40~50%. Through the real car test, the pupil area, fixation time, saccade time, saccade angle, saccade speed, and other visual indicators of the driver’s green vision rate in each section were obtained. The visual load quantization model was combined with factor analysis to explore the influence degree of the green vision rate in each section on the driver’s visual load. The results show that the visual load of the driver in the four segments with different green vision rate is as follows: Z10~20% > Z20~30% > Z30~40% > Z40~50%. When the green vision rate is 10~20%, the driver’s fixation time becomes longer, the pupil area becomes larger, the visual load is the highest, and the driving is unstable. When the green vision rate is 40% to 50%, the driver’s fixation time and pupil area reach the minimum, the visual load is the lowest, and the driving stability is the highest. The research results can provide theoretical support for the design of rural highway landscape green vision rate and help to promote the theoretical research of traffic safety. Full article

Occasionally, four cars arrive at the four legs of an unsignalized intersection at the same time or almost at the same time. If each lane has a stop sign, all four cars are required to stop. In such instances, gestures are used to communicate approval for one vehicle to leave. Nevertheless, the autonomous vehicle lacks the ability to participate in gestural exchanges. A sophisticated in-vehicle traffic light system has therefore been developed to monitor and facilitate communication among autonomous vehicles and classic car drivers. The fuzzy logic-based system was implemented and evaluated on a self-organizing network comprising eight ESP32 microcontrollers, all operating under the same program. A single GPS sensor connects to each microcontroller that also manages three light-emitting diodes. The ESPNow broadcast feature is used. The system requires no internet service and no large-scale or long-term storage, such as the driving cloud platform, making it backward-compatible with classical vehicles. Simulations were conducted based on the order and arrival direction of vehicles at three junctions. Results have shown that autonomous vehicles at four-legged intersections can now communicate with human drivers at a much lower cost with precise position classification and lane dispersion under 30 s. Full article

CO₂ emissions from gas-powered cars have a large impact on global warming. The aim of this paper is to develop an accurate estimation method of CO₂ emissions from individual private passenger cars by using actual driving data obtained by telematics. CO₂ emissions from gas-powered cars vary depending on various factors such as car models and driving behavior. The developed approach uses actual monthly driving data from telematics and vehicle features based on drag force. Machine learning based on random forest regression enables better estimation performance of CO₂ emissions compared to conventional multiple linear regression. CO₂ emissions from individual private passenger cars in 24 car models are estimated by the machine learning model based on random forest regression using data from telematics, and the coefficient of determination for all 24 car models is R2=0.981. The estimation performance for interpolation and extrapolation of car models is also evaluated, and it keeps enough estimation accuracy with slight performance degradation. The case study with actual telematics data is conducted to analyze the relationship between driving behavior and monthly CO₂ emissions in similar driving record conditions. The result shows the possibility of reducing CO₂ emissions by eco-driving. The accurate estimation of the reduced amount of CO₂ estimated by the machine learning model enables valuing it as carbon credits to motivate the eco-driving of individual drivers. Full article

Energy conservation and emission reduction is a common concern in various industries. The construction process of electric wheel loaders has the advantages of being zero-emission and having a high energy efficiency, and has been widely recognized by the industry. The frequent shift in wheel loader working processes poses a serious challenge to the operator. Automatic shift is an effective way to improve the operator’s comfort and safety. The driving intention is an important input judgment condition to achieve efficient automatic shift. However, the current methods of vehicle driving intention recognition mainly focus on passenger cars. The working condition of the wheel loader is significantly different from that of the passenger car, with a high shifting frequency and severe load fluctuation. The driving intention recognition method of passenger cars is difficult to transplant directly. In this paper, aiming at the characteristics of wheel loader working conditions, a fuzzy recognition method based on fuzzy control is applied to driving intention recognition for electric wheel loaders. The throttle, throttle change rate and braking signals are used as inputs for recognizing the driving intention at the current moment of the whole machine. Five types of driving intentions, namely, rapid acceleration, normal acceleration, acceleration maintenance, deceleration and braking, are defined and recognized. In order to verify the effectiveness of the proposed method, simulation and experimental research are carried out. The results show that the proposed driving intention recognition method can effectively identify the driver’s intention and provide effective shift signal input for the wheel loader. Full article

Car passengers suffer much more from motion sickness compared to car drivers, presumably because drivers can better anticipate the car’s motions. Visual and auditory cues that announce upcoming motions have been demonstrated to mitigate motion sickness. In automated vehicles, vibrotactile cues might be more desirable. However, prior studies provided mixed evidence regarding their effectiveness. In this study, we directly compared the effectiveness of anticipatory auditory and vibrotactile cues. We determined their effectiveness by examining self-reported motion sickness from anticipatory sessions with auditory or vibrotactile cues announcing the onset and direction of upcoming motion relative to a control session. Our preregistered analysis did not show a significant difference in mitigation between the cues but also no significant overall effect. As this lack of an effect may be due to limited statistical power, we performed an internal meta-analysis. This analysis demonstrated a small overall effect of anticipatory cues. We conclude that it is worthwhile to investigate how their effectiveness can be enhanced. Full article

The successful application of CAR-T cells in the treatment of hematologic malignancies has fundamentally changed cancer therapy. With increasing numbers of registered CAR-T cell clinical trials, efforts are being made to streamline and reduce the costs of CAR-T cell manufacturing while improving their safety. To date, all approved CAR-T cell products have relied on viral-based gene delivery and genomic integration methods. While viral vectors offer high transfection efficiencies, concerns regarding potential malignant transformation coupled with costly and time-consuming vector manufacturing are constant drivers in the search for cheaper, easier-to-use, safer, and more efficient alternatives. In this review, we examine different non-viral gene transfer methods as alternatives for CAR-T cell production, their advantages and disadvantages, and examples of their applications. Transposon-based gene transfer methods lead to stable but non-targeted gene integration, are easy to handle, and achieve high gene transfer rates. Programmable endonucleases allow targeted integration, reducing the potential risk of integration-mediated malignant transformation of CAR-T cells. Non-integrating CAR-encoding vectors avoid this risk completely and achieve only transient CAR expression. With these promising alternative techniques for gene transfer, all avenues are open to fully exploiting the potential of next-generation CAR-T cell therapy and applying it in a wide range of applications. Full article

As the aging population increases, elderly drivers face challenges due to physical and cognitive decline, in addition to the growing complexity of in-car technology and interaction features. This study aims to optimize the in-car control interface for elderly drivers. Using surveys, the KANO model, Analytic Hierarchy Process (AHP), and Quality Function Deployment (QFD), we first identify the core needs of elderly drivers. Based on these findings, we propose four key design principles: (1) Streamline functional tasks and simplify interaction logic; (2) Reasonable page layout and functional modularization; (3) Color matching for the elderly, creating a comfortable emotional experience; and (4) Choose familiar icons with less cognitive burden. The design was implemented based on these principles, followed by usability testing to validate the results. The findings show that the optimized interface improves the usability, ease of operation, and overall satisfaction of elderly drivers, enhancing both safety and the driving experience. This research provides a theoretical foundation and practical framework for age-friendly design, offering valuable insights for related fields. Full article

In this article, we propose an assessment framework for zero-emission vehicles (ZEVs) in Germany using economic and customer-relevant criteria, with a focus on the mobility needs of individuals. Developing this framework required data obtained from four different sources: (1) literature, (2) semi-structured interviews, (3) a survey, and (4) market research. First, we derived the criteria relevant to assessing ZEVs from the literature and from semi-structured interviews. These interviews were conducted with individuals who have driving experience with both battery and fuel cell electric vehicles. Seven criteria were found to be particularly relevant for assessing ZEVs: greenhouse gas emissions, infrastructure availability, charging/refueling time, range, spaciousness, total costs, and driving dynamics (in descending order of importance). Second, we conducted a survey among 569 ZEV drivers and ZEV-interested individuals in order to weight these seven criteria. This survey was based on the Analytic Hierarchy Process approach. We then used market research to assign value scores to each criterion, representing the extent to which a particular ZEV meets a given criterion. Finally, we combined the value scores with the criteria weights to create the assessment framework. This framework allows for a transparent assessment of different ZEVs from the perspective of (potential) customers, without the need to repeatedly involve the surveyed participants. Our study is primarily useful for mobility planners, policymakers, and car manufacturers to improve ZEV infrastructure and support transportation systems’ transition towards low-carbon mobility. Full article

The safety of construction and maintenance work zones has been highlighted as a crucial aspect of construction management that requires special attention due to the increasing number of fatal and non-fatal injuries in recent years. Temporary traffic control (TTC) is required by the Occupational Safety and Health Administration (OSHA) to improve overall safety performance during road construction and maintenance projects. The fact that speeding and distracted drivers may overlook TTC warning signs and directions has been reported as one of the leading causes of work zone incidents. This study aimed to examine both the impact of temporary portable rumble strips (TPRSs) on traffic speeds and the response of different vehicle types in road work zones, including trucks and cars. Accordingly, field experiments were conducted in collaboration with the Road Commission for Oakland County (RCOC) in Michigan. The findings indicate that TPRSs have a statistically significant impact on the driving speed of light vehicle drivers but not on medium and heavy vehicles, such as trucks. This study contributes to the existing literature by quantifying the safety benefits of TPRS use, providing valuable data for policymakers and construction professionals. By demonstrating the effectiveness of TPRSs in reducing the speed of light vehicles, this research supports the implementation of these systems as a practical measure for enhancing safety within road construction work zones. Additionally, this study highlights the need for tailored approaches to address the limited impact on larger vehicles, underscoring the importance of developing complementary strategies to ensure comprehensive safety improvements across all vehicle types. Full article

Background: Choline is an essential nutrient required for proper cell functioning. Due to its status as a precursor to acetylcholine, an important neurotransmitter connected to cognition and neuromuscular function, maintaining or enhancing choline levels is of interest. Supplementation with alpha-glycerylphosphorycholine (A-GPC) can maintain choline levels, but its ability to offer support towards cognition remains an area of ongoing research. Methods: Using a randomized, double-blind, placebo-controlled, crossover approach, 20 resistance-trained males (31.3 ± 11.0 years, 178.6 ± 7.3 cm, 84.6 ± 11.4 kg, 15.4 ± 5.6% body fat) consumed either a placebo (PL), 630 mg A-GPC (HD), or 315 mg (LD) A-GPC (GeniusPure^®, NNB Nutrition, Nanjing, China). After resting hemodynamic assessments, participants took their assigned dose and had cognitive assessments (Stroop, N-Back, and Flanker), visual analog scales, and hemodynamics evaluated 60 min after ingestion. All participants then warmed up and completed vertical jumps and bench press throws before completing a bout of lower-body resistance exercise (6 × 10 repetitions using the Smith squat at a load of 70% 1RM). Venous blood was collected 5, 15, 30, and 60 min after completion of the squat protocol to evaluate changes in growth hormones, and follow-up visual analog scales and cognitive measurements were evaluated 30 min after completing the exercise bout. Results: When compared to PL, changes in Stroop total score were statistically greater after HD (13.0 ± 8.2 vs. 5.2 ± 9.0, p = 0.013, d = 0.61) and LD (10.8 ± 7.7 vs. 5.2 ± 9.0, p = 0.046, d = 0.48) administration, in addition to significantly faster times to complete the Stroop test in the HD group when compared to PL (−0.12 ± 0.09 s vs. −0.05 ± 0.09 s, p = 0.021, d = 0.56). No significant differences between groups were found for the Flanker and N-Back assessments, while a tendency was observed for HD to have faster reaction times when compared to PL during the Flanker test. No group differences were realized for visual analog scales, physical performance, or growth hormone. Statistically significant changes in heart rate and blood pressure were observed in all groups, with all recorded values aligning with clinically accepted normative values. Conclusions: HD and LD A-GPC supplementation significantly increased cognitive performance in a group of young, healthy males as measured by changes in the Stroop Total Score and completion time of the Stroop test. These results offer unique insight into the potential for A-GPC to acutely increase cognition in a group of young, healthy males. While previous research has indicated potential for A-GPC to acutely improve cognition in clinical populations, extending these outcomes to healthy individuals can be potentially meaningful for a wide variety of populations such as athletes, race car drivers, military operators, and other non-athletic populations who desire and have a need to improve their mental performance. This study was retrospectively registered as NCT06690619 on clinicaltrials.gov. Full article

This paper proposes a solution based on the particle swarm optimization algorithm to address the issue of Proportional Integral Derivative parameter selection in the motion control of a two-wheel differential car. The mathematical motion model is established based on the driving principle of a two-wheel differential car. The transfer function of the DC motor is derived in detail, based on Kirchhoff’s law and the Laplace transform. The pose renewal equation and error renewal equation of the car are based on the mathematical motion model. Finally, a numerical simulation and experimental analysis were conducted using MATLAB R2022a, Simulink 9.1 (part of R2018a), VOFA 1.3.10 software, an STM32 microcontroller, an L298N driver chip, and other hardware components. The results indicate that the particle swarm optimization algorithm enables the rapid acquisition of optimal Proportional Integral Derivative parameters. The optimized parameter of the motor speed convergence time is set to 10 ms, with an overshoot of 1 r/min and an enhanced anti-interference ability. The optimized parameters effectively regulate the car’s motion, ensuring a maximum error control of approximately 0.003 m. Full article

In auxiliary transportation within mines, accurately assessing the cognitive and response states of drivers is vital for ensuring safety and operational efficiency. This study investigates the effects of various vehicle interaction stimuli on the electroencephalography (EEG) signals of mine transport vehicle drivers, analyzing the cognitive and response states of drivers under different conditions to evaluate their impact on safety performance. Through experimental design, we simulate multiple scenarios encountered in real operations, including interactions with dynamic and static vehicles, personnel, and warning signs. EEG technology records brain signals during these scenarios, and data analysis reveals changes in the cognitive states and responses of drivers to different stimuli. The results indicate significant variations in EEG signals with interactions involving dynamic and static vehicles, personnel, and warning signs, reflecting shifts in the cognitive and response states of drivers. Additionally, the study examines the overall impact of different interaction objects and environments. The detailed analysis of EEG signals in different scenarios sheds light on changes in perception, attention, and responses related to drivers, which is critical for advancing safety and sustainability in mining operations. Full article

Taking into account the requirements to reduce the release of harmful emissions into the environment, the EU’s environmental standards when transitioning to the Euro 7 standard in 2025 will actually lead vehicles having to operate without producing emissions in all driving situations. Carmakers believe that the new, much stricter regulations will mark the end of the internal combustion engine era. For example, in 2030, the manufacturer SEAT will cease its activities, leaving behind the Cupra brand, which will be exclusively electric in the future. This trend will apply not only to private vehicles (passenger cars), but also to utility vehicles, which is the subject of our research, namely the spatial tubular frame in the Bogdan ERCV27 garbage truck, presented in the form of a solid model. The peculiarity of the studied model is the installation of a battery block behind the driver’s cabin, causing an additional load to be placed on the spatial frame of the garbage truck, which in terms of its architecture is more like the body of a bus. During the conditions involving various modes of operation of a full-scale Bogdan ERCV27 garbage truck sample, questions about the strength and uniformity of its load-bearing spatial frame inevitably arise, which are decisive, even at the stage of designing and preparing the technical documentation. The main static load mode, which, despite its name, also covers dynamic conditions, was modeled using the appropriate coefficient k_d = 2.0. The maximum stresses on the model during the “bending” mode were 381.13 MPa before structure optimization and 270.5 MPa as a result of the improvement measures. The spatial frame mass was reduced by 4.13%. During the “torsion” mode, the maximum deformation values were 12.1–14.5 mm, which guarantees the normal operation of the aggregates and units of the truck. Full article

This paper presents the results of a study on the effects of low car speeds and the elastic-damping properties of tires on steering effort. “Steering effort” is a measure of the demand/energy consumption of the power steering system that limits the force applied by the driver to the steering wheel. Low driving speeds, on the other hand, are characteristic of urban traffic, where we would like to see as many electric cars moving as possible. An increase in “driver effort” means a higher electricity consumption and shorter car range. In this study, energy intensity was evaluated for a typical maneuver such as a double lane change. For this purpose, measurements were made of the torque on the steering wheel, the speed of the car, and the lateral accelerations acting on the car. A torque wheel, an optoelectronic sensor for measuring the components of the car’s motion, and an acceleration sensor were used for the study. The test subjects were two passenger cars with hydraulic power steering systems. The tests were carried out for four values of air pressure in the tires. This made it possible to determine four work charts for each wheel. The work charts made it possible to identify the stiffness and damping coefficients of the tires for the tested cars. The values of the coefficients were used to determine the correlation between the directional coefficient of the regression lines of the skeletal axes of the elastic and damping characteristics and the index determining steering effort. Full article