Passenger Vehicles Research Articles

Methods Review for Mitigating Motion Sickness of Electric Vehicle Passengers

Under the background of the continuous development of electric vehicles and intelligent cockpit technology, the problem of motion sickness is becoming more and more prominent, which has become an important factor affecting the riding experience. Therefore, this study centers on the problem of motion sickness, discusses its mechanism, influencing factors and mitigation methods, and aims to provide theoretical and practical support for improving the comfort and safety of passengers, which is of great significance for improving the riding environment and enhancing the quality of passenger travel.

Comparative analysis of child injuries in passenger vehicles and school buses: a multicentre cross-sectional study

ObjectiveThis study investigated the differences in injury profiles and safety device effectiveness among children with road traffic injuries (RTIs) involving passenger vehicles and school buses.MethodsUsing data from the Emergency Department-based...

Spatial instability of crash prediction models: A case of scooter crashes

Scooters have gained widespread popularity in recent years due to their accessibility and affordability, but safety concerns persist due to the vulnerability of riders. Researchers are actively investigating the safety implications associated with scooters, given their relatively new status as transportation options. However, analyzing scooter safety presents a unique challenge due to the complexity of determining safe riding environments. This study presents a comprehensive analysis of scooter crash risk within various buffer zones, utilizing the Extreme Gradient Boosting (XGBoost) machine learning algorithm. The core objective was to unravel the multifaceted factors influencing scooter crashes and assess the predictive model’s performance across different buffers or spatial proximity to crash sites. After evaluating the model’s accuracy, sensitivity, and specificity across buffer distances ranging from 5 ft to 250 ft with the scooter crash as a reference point, a discernible trend emerged: as the buffer distance decreases, the model’s sensitivity increases, although at the expense of accuracy and specificity, which exhibit a gradual decline. Notably, at the widest buffer of 250 ft, the model achieved a high accuracy of 97% and specificity of 99%, but with a lower sensitivity of 31%. Contrastingly, at the closest buffer of 5 ft, sensitivity peaked at 95%, albeit with slightly reduced accuracy and specificity. Feature importance analysis highlighted the most significant predictor across all buffer distances, emphasizing the impact of vehicle interactions on scooter crash likelihood. Explainable Artificial Intelligence through SHAP value analysis provided deeper insights into each feature’s contribution to the predictive model, revealing passenger vehicle types of significantly escalated crash risks. Intriguingly, specific vehicular maneuvers, notably stopping in traffic lanes, alongside the absence of Traffic Control Devices (TCDs), were identified as the major contributors to increased crash occurrences. Road conditions, particularly wet and dry, also emerged as substantial risk factors. Furthermore, the study highlights the significance of road design, where elements like junction types and horizontal alignments – specifically 4 and 5-legged intersections and curves – are closely associated with heightened crash risks. These findings articulate a complex and spatially detailed framework of factors impacting scooter crashes, offering vital insights for urban planning and policymaking.

Mechanisms and risk factors associated with spinal cord injury, facet fracture, and level of dislocation, in occupants with cervical spine dislocations sustained in motor vehicle crashes

Objective Motor vehicle crashes (MVCs) are the leading cause of cervical spine dislocation. The mechanisms underlying this injury are unclear, limiting the development of injury prevention devices and strategies. MVC databases contain occupant, medical, vehicle, and crash details that are not routinely collected elsewhere, providing a unique resource for investigating injury mechanisms and risk factors. In this study, a comprehensive standalone analysis of cervical spine dislocations captured in MVC databases was performed. Methods Epidemiologic, biomechanical, and injury data were extracted from three MVC databases. Logistic regression models were developed to determine the occupant, vehicle, and crash characteristics, as well as the global (inertial or impact) and regional (flexion, compression, etc.) loading mechanisms associated with the level of cervical spine dislocation (axial or sub-axial), and the occurrence of spinal cord injury (SCI) or facet fracture concomitant to dislocation. Results There was no association between global or regional injury mechanisms and the level of cervical spine dislocation. Sub-axial dislocations were typically due to head/face impact with the airbag or upper interior components, or a result of seatbelt restraint of the torso. Higher occupant age, lower BMI, partial/no ejection, and frontal and side configuration crashes (compared to rollovers) were associated with a higher likelihood of sub-axial, versus axial, dislocation. Amongst all dislocations, an increased likelihood of SCI was associated with impact injuries, airbag non-deployment, and complete ejection, while concomitant facet fracture was associated with the presence of regional compression. Severe crashes, partial ejections, and “utility vehicles” and “vans and trucks” (compared with “passenger vehicles”) were associated with a higher risk of facet fracture concomitant to sub-axial dislocation. Conclusion The findings of this study may be used to inform the loading modes to be simulated in future ex vivo or computational models seeking a better understanding of cervical spine dislocations.

Assessing the accuracy of two steady‐state temperature models for onboard passenger vehicle photovoltaics applications

AbstractWe assess the accuracy of two steady‐state temperature models, namely, Ross and Faiman, in the context of photovoltaics (PV) systems integrated in vehicles. Therefore, we present an analysis of irradiance and temperature data monitored on a PV system on top of a vehicle. Next, we have modeled PV cell temperatures in this PV system, representing onboard vehicle PV systems using the Ross and Faiman model. These models could predict temperatures with a coefficient of determination (R2) in the range of 0.61–0.88 for the Ross model and 0.63–0.93 for the Faiman model. It was observed that the Ross and Faiman model have high errors when instantaneous data are used but become more accurate when averaged to timesteps of greater than 1000–1500 s. The Faiman model's instantaneous response was independent of the variations in the weather conditions, especially wind speed, due to a lack of thermal capacitance term in the model. This study found that the power and energy yield calculations were minimally affected by the errors in temperature predictions. However, a transient model, which includes the thermal mass of the vehicle and PV modules, is necessary for an accurate instantaneous temperature prediction of PV modules in vehicle‐integrated (VIPV) applications.

Robust pedestrian multi-object tracking in the intelligent bus environment

Abstract Pedestrian multi-object tracking algorithms aim to maintain identity information of pedestrians by comparing the similarity between trajectories and detections, predicting pedestrian motion trajectories. However, within the context of intelligent bus, challenges arise due to factors such as passenger growth and vehicle vibrations, rendering existing pedestrian multi-object tracking algorithms less accurate. Therefore, this paper proposes an intelligent bus terminal robust pedestrian multi-object tracking algorithm named pure motion (PM), which can consistently and stably track pedestrians. The proposed algorithm employs several key strategies. Firstly, it optimizes trajectory prediction by adapting the aspect ratio of the prediction box based on pedestrian movement, automatically adjusting its shape, and selecting velocity weight coefficients according to different tracking targets. Secondly, it decomposes the homography matrix to acquire motion components and correct predicted results under motion conditions. Subsequently, the algorithm leverages the similarity between detection results and trajectories to retain high-confidence detections, eliminating low-confidence ones associated with background, thereby reducing false negatives and enhancing trajectory coherence. Futhermore, the introduction of detection confidence into trajectory updates to enhances the precision of measurement noise. The proposed algorithm underwent testing in intelligent bus driving scenarios, including turns, waiting for traffic lights, emergency braking, and approaching bus stops. The tracking accuracy on the MOT17-13-val dataset reaches 81.8. The results demonstrate that PM significantly improves the robustness of pedestrian multi-object tracking algorithms in the environment of intelligent bus.

Large-Scale Side By Side Stereo-PIV Measurements In An Automotive Wind Tunnel: Aerodynamic Influence Of Ride Height Variations.

In this investigation, we conducted large-scale Stereo PIV experiments in the flow around a Volkswagen ID.4. within the Aerodynamic and Aeroacoustic Wind Tunnel (AAK) at Volkswagen AG, Wolfsburg. The primary objective of these experiments was to investigate the effects of ride height variation on the near wake of the passenger vehicle using Stereo PIV, while minimizing wind tunnel occupancy. The measurements aimed to create datasets for CFD validation to improve virtual aerodynamic development capabilities of passenger vehicles. The experiment was performed on a 1:1 scale vehicle and multiple cross-sectional planes were scanned along the longitudinal axis of the vehicle. The impact of ride height alterations on the wake flow topology were evaluated. Average velocity vector fields were computed per case, providing comprehensive insights into the aerodynamic effects of diverse vehicle setups. A small change in the ride height has shown to have a significant impact on the wake topology. The results show that the electric SUV ID.4 can yield a wake topology similar to the estate back and fast/notchback type of vehicle, depending on the ride height. The normal ride height of the ID.4 shows to have a broader but lower wake region, with a significant downwash. The PIV results of this configuration show significant similarities to the wake topology of a fast back vehicle. Lowering the ride height by 15 mm has shown already to be enough to cause a global effect on the wake topology and to exhibit a narrower wake and recirculation region, like an estate back vehicle. Additionally, the association between the drag/lift coefficients and the wake topology of both configurations was discussed. Results have shown that sole measurements in the wake are not sufficient to fully explain changes in the determined drag or front lift coefficients. The rear lift coefficients however, indicate a stronger relationship with the near wake topology of the vehicle.

Model of a 100 % renewable energy system of self-sufficient wider urban area based on a short-term scale and the integration of the transport and thermal sector

The mutual integration of different energy sectors and their conversion to electricity could promote the integration of renewable energy sources. This paper analyses 100 % renewable electrical power system of the Dubrovnik wider urban area. The Calliope energy system model was developed and compared based on 10-min and hourly data. The energy scenario is modelled up to the year 2050. It includes the replacement of all conventional passenger vehicles with electric vehicles. The vehicle-to-grid model was developed using the Calliope modelling tool. All fossil fuels used in the heating sector are replaced by seawater heat pump systems. Additional storage is added to the system and the system is connected to the adjacent electricity market. The 10-min model led to a reduction in cross-border transmission capacity of around 60 % and a 7-fold reduction in the difference between import and export compared to the hourly model. This led to a more stable system operation, as the import and export capacities were equalised. The vehicle-to-grid technology in the regulated system led to a reduction in import and export transmission capacities of up to 17 % and 42 % respectively compared to the unregulated system, including extremely fast charging and discharging in a short-term step model.

Scenarios for New Mobility Policies and Automated Mobility in Beijing

In this study, we consider the introduction of new mobility services and technologies into the megacity of Beijing, China, as per developed strategy and action plans, in order to investigate their potential contribution to sustainable mobility. This includes population relocation (decentralization), the construction of new rail lines, the introduction of shared bike services as a feeder to subway stations, the electrification of passenger vehicles and the adoption of automated and shared vehicles. The well-established, system dynamics-based MARS model is adapted to Beijing and further improved via the inclusion of these new services, technologies and policies. We find that decentralization can have a profound effect on overall sustainability if not considered in conjunction with other policies and that new rail lines and shared bikes may only have benefits in specific zones. Shared and automated vehicles could increase VKT by 60% and reduce active and public transport trips by a quarter. As such, nuanced integrated policy approaches will be required that are similar to those currently in place, such as imposed car shedding and taxi fleet control.

Carbon Footprint Reduction Associated With Multidisciplinary Pediatric Airway Clinics: A Program Evaluation Study.

Health care is a significant contributor to the climate crisis. Multidisciplinary clinics (MDC) may reduce carbon emissions by combining multiple appointments into one. This is the first program evaluation study to quantify the carbon footprint associated with multidisciplinary pediatric airway clinics. Retrospective. Children's Hospital at London Health Sciences Center, London, Canada. Pediatric airway MDC allows patients to see otolaryngology and respirology in one appointment. The carbon and financial savings (Canadian Dollars) of all patients attending the MDC from January 1, 2018 to December 31, 2022 were calculated. Patient postal codes and institutional parking rates were inputted into the CASCADES carbon accounting tool. Total distance was divided into unsustainable (vehicles) and sustainable (transit, walking, cycling) transportation to calculate carbon emissions. Travel costs included cost/kilometer for vehicles (maintenance, license/registration, insurance, fuel) and costs/ride for transit. A total of 560 MDC appointments for 300 patients saved 77,785 km. Total carbon emissions saved from travel averted was 16.21 tonnes. The total carbon emissions saved, minus public transit, was 15.60 tonnes. Using the Natural Resources Canada Greenhouse Gas Equivalencies Calculator, 16.21 tonnes are approximately equivalent to 5 passenger vehicles, 6906 L of gasoline, 3.8 homes' energy, and 10.8 homes' electricity use for one year, 36.6 barrels of oil consumed, and 675 propane cylinders. Travel costs of $28,891.83 (no parking), $30,519.40 ($4 minimum parking fee), or $33,774.55 ($12 maximum parking fee) were saved. MDC effectively reduced carbon emissions and offered patients financial savings. Similar models can be adapted across institutions to help mitigate climate change.

Development of a condition monitoring of heat exchangers for corrosion progress and coolant condition

The transport sector, and thus also rail transport, needs to be completely decarbonised by 2050 at the latest. Considering the long service life of rail vehicles, this goal means enormous innovation and growth potential for the vehicle and supplier industry of railway technology in the coming years. This contribution presents the first results of an R&D project to reduce the total cost of ownership of passenger and freight rail vehicles. The reduced life cycle costs can be achieved over the operating life, among other things, with condition monitoring approaches for critical components of cooling systems to enable diagnosis-based maintenance. Based on guided elastic waves, an integrated system is being developed to analyse the depth and position of corrosion damage to heat exchangers in rail vehicles. This method is to be supplemented by a refractive index sensor integrated in the coolant circuit which can be used to derive influences of the coolant condition on corrosion. First, an acoustic measuring technique was put into operation. Suitable piezoelectric transducers were selected and test measurements, including the introduction of artificial damages, along different paths on a representative heat exchanger were carried out and evaluated. Measurement signals can be received over different distances, and a monitoring of larger areas of the heat exchanger seems possible. For the development of the monitoring of the coolant, plasmonically active sensor substrates with a gold nanostructure were produced and installed in a measuring flow cell. In laboratory tests, concentrations of water-coolant mixtures could be distinguished with the refractive index sensor. In the future, the refractive index sensing will be carried out by means of bypass flow measurement. Since the coolant is pumped in a circuit, the sensor system can be attached to one of the inlet/outlet lines.

An adapted two-step approach to simulate nonlinear vibrations of patterned tires rolling on a smooth surface

The tire/road noise is one of the major problems facing the tire industry due to the nuisance felt by the vehicle's driver and passengers. Moreover, it is expected in the coming years that this sound nuisance will be one of the main sources of vehicle noise due to the transition from combustion engine driven vehicles to electric vehicles. Tire manufacturers have therefore refined the design of their tire structures to find technological solutions to reduce traffic noise. Tire/road noise is also generated by various mechanisms which depend on different parameters such as the properties of the tire, road texture and driving conditions. This noise is partly caused by the acoustic radiation induced by the tire vibrations due to contacts with the road. The simulation and analysis of the tire's vibrations remains a challenge for the tire industry. Indeed, the simulation of the full dynamic response of a rolling patterned tire requires not only taking onto account various nonlinearities but also the multi-scale nature of the generated dynamic response. Contrary to a straightforward strategy that consists in using a time integrator to predict the multi-scale dynamic response, the strategy proposed in this study is based on a two-step approach to separate the dynamics occurring at different scales. The mathematical formulation of the proposed method is detailed as are the different modeling choices to simulate tire rolling under imposed load. The sensitivity of the tire's vibrations response is analyzed under different rolling conditions and with respect to certain key tire tread pattern parameters.

Suitability of Hybrid Passenger Vehicles in Metropolitan Cities: A Study of Paris and Cairo Using Toyota Prius

Abstract Hybrid electric vehicles (HEVs) reduce metropolitan city air pollution and traffic congestion. This paper evaluates the performance of the Toyota Prius, a passenger HEV model, in Paris and Cairo, using driving cycle data incorporated in the validated digital tool (Advanced Vehicle Simulator - ADVISOR ). The paper compares exhaust gas emissions and fuel consumption of the selected HEV in those two cities. The results show a reduction in exhaust gas emissions and fuel consumption in Paris than in Cairo and a significant reduction in greenhouse gas emissions. The paper suggests that the HEV Toyota Prius is a suitable and sustainable solution for urban transportation in metropolitan cities.

Comparative safety performance of autonomous- and human drivers: A real-world case study of the Waymo Driver

After several years of public road testing, the commercial deployment of fully autonomous vehicles—or Automated Driving Systems (ADS)—is poised to scale substantially following significant technological advancements and recent regulatory approvals. However, the fundamental question of whether an ADS is safer than its human counterparts remain largely unsolved due to several challenges in establishing an appropriate real-world safety comparison method. As scaling ensues, the lack of an established method can contribute to misinterpretations or uncertainties regarding ADS safety and impede the continuous and consistent assessment of ADS performance. This study introduces three research developments to define a robust and replicable safety comparison method to address this critical methodological gap. First, we introduce the use of liability insurance claims data to measure the comparative safety between ADS and human drivers. Second, we use Swiss Re insurance claims data to establish the first zip code- and responsibility-calibrated human performance benchmark, composed of over 600,000 private passenger vehicle claims and 125 billion miles of driving exposure. Third, we perform a case study by applying the developed baseline to evaluate the safety impact of the Waymo Driver. We find that when benchmarked against zip code-calibrated human baselines, the Waymo Driver significantly improves safety towards other road users. The comparison method established in this study can be replicated for other regions or ADS deployments to aid the decision-making of ADS safety stakeholders such as regulators, and instill trust in the general public.

CORRELATION STUDIES OF PROCESSES: VEHICLE VELOCITY, POLLUTANT EMISSIONS, AND VEHICLE FUEL CONSUMPTION IN THE WORLD-WIDE LIGHT-DUTY TEST CYCLE

The article presents the worldwide harmonized light-duty test cycle results of a diesel engine passenger vehicle carried out on a chassis dynamometer. Pollutant emissions from exhaust and fuel consumption were measured. Vehicle velocity was treated as a variable determining pollutant emissions and fuel consumption because the engine operating states depend on engine velocity and load during engine operation, and these states in turn affect pollutant emissions and fuel consumption. Actions taken to reduce pollutant emissions and consumption of fuel are often in opposition to each other. Therefore, there is a need to optimize these activities. There is also a need to know the relationship between the effects of taking specific actions that serve to achieve individual goals in order to rationalize actions aimed at reducing pollutant emissions and fuel use. The originality of the article lies in the use of the correlation theory between pollutant emission and fuel consumption as well as the processes that determine them, primarily vehicle velocity, to rationalize these activities. Pearson's linear correlation theory was used to assess the relationships between individual variables. Significant differences were found in the correlation coefficients between individual variables, which confirmed the need to take integrated actions to reduce pollutant emissions and fuel consumption.

Quantifying the Impact of Risk Factors on Direct Compensation Property Damage in Canadian Automobile Insurance

This study presents a statistical analysis assessing the impact of various risk factors on direct compensation property damage (DCPD) claims in private passenger vehicle accidents. Using automobile insurance data in Ontario, Canada for the decade years period between 2003 and 2012, a statistical model of property damage was explored via a generalized linear binary logit mixed model and considered the imbalance between the classes of insureds. The results indicate that several risk factors have a significant impact on the likelihood of DCPD claims, including usage, training, outstanding loss, and incurred loss. The effects of these risk factors were observed under the weights — the number of trials used to generate each success proportion — in the different classes of insureds. The generalized linear mixed models (GLMMs) analysis provides a powerful tool for quantifying the impact of risk factors on binary outcomes, which are called DCPD claims and property damage (PD) claims covered by third-party liability (TPL) insurance. These models can also inform insurance underwriting and policy design, focusing on identifying the most significant risk factors. The performance metrics calculated by considering the class imbalance in binary outcomes verify the resulting model’s ability to accurately predict classes. The F1 score, an evaluation metric to measure the performance of classification, was calculated as 0.934. In addition, PR AUC, which is the area under the Precision-Recall (PR) curve, was computed as 0.953. These high scores indicate that the resulting model performs well in the classification. The other metrics also support the classification accuracy of this model. The findings of the analysis can help insurers better understand the underlying drivers of property damages and develop more accurate and effective strategies for risk mitigation. Furthermore, this study highlights the importance of developing class-specific risk assessment models to account for the imbalance across different classes.

Dynamic Position Accuracy of Low-Cost Global Navigation Satellite System Sensors Applied in Road Transport for Precision and Measurement Reliability

Low-cost Global Navigation Satellite System (GNSS) sensors have been successfully applied in commercial vehicles’ position monitoring, and they continually raise interest among research audiences both in theoretical and practical aspects. While numerous studies have applied simulations and numerical methods to evaluate the accuracy of the sensors, this paper presents an analysis, supported by actual measurements collected under diversified conditions. The measurements were collected under a variety of conditions, including urban and suburban routes of considerable length, and in accordance with the position in lane applied in most European countries, which is considerably related to the sustainability of road transport. The measurements were collected during driving of three different passenger vehicles, and the response of the measurements to correct, partially correct and incorrect vehicle positions was recorded. Differentiated kinematic conditions and actual dynamic performance during driving were analyzed. This research compared the position accuracy of a low-cost GNSS sensor and a dual-antenna GNSS/INS sensor for vehicle dynamics monitoring. Both types of sensors were operated on all the passenger vehicles and with the same measurement conditions. Statistical hypothesis tests have been considered to compare the results, in accordance with the latest guidelines for carrying out such tests. Studies have indicated that a low-cost GNSS sensor also has satisfactory accuracy. However, this paper points out additional considerations and conclusions. Both the positive and negative results are described and commented on in the paper, including research limitations and suggestions for future measurement and future research agendas, both by the authors and as an inspiration for other researchers.

Integrating system dynamics and agent-based modeling: A data-driven framework for predicting electric vehicle market penetration and GHG emissions reduction under various incentives scenarios

As the growing deployment towards transportation electrification, a critical focus has emerged on quantifying the reduction contribution of greenhouse gas emissions from electric vehicles towards achieving carbon neutrality under diverse policy scenarios in the future. This necessitates a dynamic model that captures the evolving composition of the vehicle fleet and accurately forecasts the penetration and developmental trajectory of the electric vehicles in the car market. However, previous studies have largely overlooked the heterogeneity in user usage attributes, rendering them less effective in evaluating the impact of usage-based incentives on electric vehicle market penetration. To bridge this research gap, this study introduces an innovative, data-driven framework that integrates system dynamics and agent-based model. The proposed model can predict levels of electric vehicle penetration and corresponding greenhouse gas emission reductions within the private passenger vehicle sector, under a variety of policy scenarios. Our findings indicate that usage-based incentives, when implemented with optimal intensity, yield more significant emission reduction impacts and long-term economic benefits compared to conventional purchase-based subsidy. These insights not only furnish actionable policy suggestions to expedite the electric vehicle industry's growth in China but also offer valuable implications for other countries seeking to implement effective strategies for combating climate change and fostering sustainable transportation initiatives.

Analytical Prediction of Crushing Thin-Walled Square Tube with Static Axial Loads Using Elliptical Holes as Crush Initiators

In the structure of passenger vehicles there are components that absorb the impact load, which components are commonly thin-walled square tube. The design of the impact energy absorber model can be carry-out by experiment, simulation, and analysis. In the design of this impact energy absorbing component, an elliptical hole was used as a crush initiator to reduce peak loads and set the start of the bending pattern. Analytical predictions were made to reduce costs and speed up time to obtain mean load and peak loads values from the same shape model. In this study, a thin-walled square tubes model is used with an elliptical hole that have ratio of horizontal axis and vertical axis is from 3:7 until 7:3. The result of this study showed that the prediction of the peak load and average load by analytical means has a good conformity with the simulation. Keywords: Impact energy absorber, analytical, square tube, elliptical hole.

A Bibliometric Analysis of Suspension System Development for Enhancing Comfort in Family Passenger Vehicles

In daily-used family passenger cars, passenger comfort is crucial. Vehicle suspension is a major factor in passenger comfort. A detailed suspension domain knowledge evaluation is necessary. Despite continual comfort technology advances, studies have yet to use bibliometric analysis to track suspension system history and expansion. This study analyzes keywords, citations, h-index, publishing years, journals, affiliations, nations, authors, and review articles in a novel way. Based on Scopus, the analysis is performed in CSV format and incorporated into bibliometric.org's Biblioshiny, VOSviewer, and R-studio apps. The US leads this study, followed by India and Canada. Indian publications are the most prolific. For suspension technology makers, this research may help them discover rivals and predict future trends. This study promotes suspending technology development through educated decision-making, strategic planning, and innovation.