Real Road Research Articles

Abrasion Behaviors of Silica-Reinforced Solution Styrene-Butadiene Rubber Compounds Using Different Abrasion Testers.

Solution styrene-butadiene rubber (SSBR) is widely used to improve the properties of tire tread compounds. Tire wear particles (TWPs), which are generated on real roads as vehicles traverse, represent one of significant sources of microplastics. In this study, four SSBR compounds were prepared using two SSBRs with high styrene (STY samples) and 1,2-unit (VIN samples) contents, along with dicyclopentadiene resin. The abrasion behaviors were investigated using four different abrasion testers: cut and chip (CC), Lambourn, DIN, and laboratory abrasion tester (LAT100). The abrasion rates observed in the Lambourn and LAT100 abrasion tests were consistent with each other, but the results of CC and DIN abrasion tests differed from them. The addition of the resin improved the abrasion rate and resulted in the generation of large wear particles. The abrasion rates of STY samples in the Lambourn and LAT100 abrasion tests were lower than those of VIN samples, whereas the values in the CC and DIN abrasion tests were higher than those of VIN samples. The wear particles were predominantly larger than 1000 μm, except for the VIN sample in the DIN abrasion test. However, TWPs > 1000 μm are rarely produced on real roads. The size distributions of wear particles > 1000 μm were 74.0-99.5%, 65.9-93.4%, 7.2-95.1%, and 37.5-83.0% in the CC, Lambourn, DIN, and LAT100 abrasion tests, respectively. The size distributions of wear particles in the Lambourn and LAT100 abrasion tests were broader than those in the other tests, whereas the distributions in the CC abrasion test were narrower. The abrasion patterns and the morphologies and size distributions of wear particles generated by the four abrasion tests varied significantly, attributable to differences in the bound rubber contents, crosslink densities, and tensile properties.

Dual-Slope Path Loss Model for Integrating Vehicular Sensing Applications in Urban and Suburban Environments.

The development of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs), and autonomous driving (AD) has progressed rapidly in recent years, driven by artificial intelligence (AI), the internet of things (IoT), and their integration with dedicated short-range communications (DSRC) systems and fifth-generation (5G) networks. This has led to improved mobility conditions in different road propagation environments: urban, suburban, rural, and highway. The use of these communication technologies has enabled drivers and pedestrians to be more aware of the need to improve their behavior and decision making in adverse traffic conditions by sharing information from cameras, radars, and sensors widely deployed in vehicles and road infrastructure. However, wireless data transmission in VANETs is affected by the specific conditions of the propagation environment, weather, terrain, traffic density, and frequency bands used. In this paper, we characterize the path loss based on the extensive measurement campaign carrier out in vehicular environments at 700 MHz and 5.9 GHz under realistic road traffic conditions. From a linear dual-slope path loss propagation model, the results of the path loss exponents and the standard deviations of the shadowing are reported. This study focused on three different environments, i.e., urban with high traffic density (U-HD), urban with moderate/low traffic density (U-LD), and suburban (SU). The results presented here can be easily incorporated into VANET simulators to develop, evaluate, and validate new protocols and system architecture configurations under more realistic propagation conditions.

Suitability of using carbon dioxide as a tracer gas for studying vehicle emission dispersion in a real street canyon

High-rise buildings form deep urban street canyons and restrict the dispersion of vehicle emissions, posing severe health risks to the public by aggravating roadside air quality. Field measurements are important for understanding the dispersion process of tailpipe emissions in street canyons, while a major challenge is the lack of a suitable tracer gas. Carbon dioxide (CO2), which is safe to the public and inexpensive to obtain, can be reliably measured by existing gas analysers. This study investigated the suitability of using CO2 as a tracer gas for characterising vehicle emission dispersion in a real-world street canyon. The tracer gas was released via a line or point source, whose dispersion was characterised by a sensors network comprising low-cost air quality sensors. The results showed that the CO2 contained in the exhaust gas of a test vehicle itself had unmeasurable effect at roadsides. Both the line and point sources produced obvious CO2 level elevations at approximately 30 s after the test vehicle passed by. In addition, for both line and point sources, the CO2 elevations were much more distinct at the roadside next to tailpipe exit than the opposite side, and were higher at 0.8 m than 1.6 m above the ground. The present study demonstrated that using CO2 as a tracer gas is feasible for investigating vehicle emission dispersion in real-world street canyons. Future studies are needed to improve the gas release rate of the developed tracer gas systems for more reliable measurements and larger street canyons.

Online optimal scheduling for battery swapping charging systems with partial delivery

Battery swapping–charging system (BSCS) is a promising operating paradigm to provide centering charging and battery swapping service for electric vehicles in transportation electrification. Facing real-time information on battery demand under the limited transporting trucks, flexible online optimization of battery delivery and transportation routing is essential for meeting practical requirements. This paper investigates the real-time scheduling problem in BSCS, considering the battery partial delivery, energy demand, delivery deadline, and vehicle routing. Considering the non-deterministic polynomia hardness of battery transportation, the offline BSCS is a time-consuming task and is unsuitable for the online setting. A Lagrangian relaxation-based Benders decomposition is proposed for parallel and real-time implementation, improving the scheduling efficiency. To tackle future information such as battery demands and delivery deadlines, by introducing the dummy copy, the offline algorithm is embedded within a rolling horizon framework to solve in real-time repeatedly. Finally, case studies using real road maps in Shanghai and Belgium have verified the validity of the proposed online framework and confirmed the necessity of considering partial delivery in enhancing the operation flexibility of BSCS. The computational efficiency of the proposed algorithm is studied under different scales of the road network, and the profit from partial delivery and online implementation are highlighted.

Reliable lifelong planning A*: Technique for re-optimizing reliable shortest paths when travel time distribution updating

Re-optimization technique is an efficient approach for solving the shortest path problem in dynamic deterministic networks, where link travel times are updated in real-time. However, existing re-optimization techniques, built on the assumption that link travel times are deterministic, cannot be used to solve reliable shortest path problems in real road networks with noticeable levels of travel time uncertainties. This study proposes a novel re-optimization technique, named reliable lifelong planning A* (RLPA*), for re-optimizing reliable shortest path finding results in dynamic stochastic networks, where link travel time distributions are updated in real-time. The proposed RLPA* technique can efficiently determine the optimal solution in dynamic stochastic networks by reusing path search results produced in the previous time instance. The proposed RLPA* technique is further utilized to solve the K reliable shortest paths problem, which is regarded as a series of reliable shortest path searches in a dynamic stochastic network. To validate the proposed algorithms, a comprehensive case study using real traffic data is conducted. The case study results demonstrated that the proposed algorithms significantly outperform the corresponding state-of-the-art algorithms on all testing networks.

Automatic extraction and 3D modeling of real road scenes using UAV imagery and deep learning semantic segmentation

ABSTRACT The extraction of roads from UAV images is challenged by lighting, noise, occlusions, and similar non-road objects, making high-quality road extraction difficult. To addressing these issues, this study proposes an enhanced U-Net network to automate the extraction and 3D modeling of real road scenes using UAV imagery. Initially, a cascaded atrous spatial pyramid module was integrated into the encoder to capitalize on global context information, thereby refining the fuzzy segmentation outcomes. Subsequently, a module for augmenting road feature extraction was added within the channel, and a spatial attention mechanism was introduced in the decoder to enhance edge clarity. Experimental results demonstrated that this model captures more road information compared to mainstream networks and effectively incorporates topological structure perception for road extraction in complex scenarios, thus improving road connectivity. The model achieved an F1 score and mean Intersection over Union (mIoU) of 85.6% and 81.2%, respectively, on UAV images of road scenes – marking improvements of 3.9% and 3.4% over the traditional U-Net model, thereby exhibiting superior automatic road extraction capabilities. Ultimately, the model facilitated refined modeling and visual analysis of road scenes, achieving high overall accuracy and detailed local restoration of the actual scene.

Mathematical formulations for the multi-period alternative fuel refueling station location problem with routing under decision-dependent flow dynamics

The refueling station location problem with routing considers vehicles’ ranges and drivers’ preferences about their routes to improve the alternative fuel station infrastructure. Comprehensive planning is necessary for developing a mature infrastructure to overcome budgetary constraints and spatial limitations. Hence, adopting a multi-period planning approach becomes crucial when taking into account the evolving demand for alternative fuel vehicles over time. The evolution of demand can be dependent on exogenous and endogenous factors. Although it is typical to account for exogenous demand growth in multi-period planning, a few studies also take into account an endogenous factor which is the refueling opportunity of drivers on their paths. In this study, in addition to the refueling opportunities, we consider the proximity of each individual station to the flow-based demands. We draw attention to the significance of considering the effects of individual station locations on demand evolution, as these strategic locations can play an important role in reducing the drivers’ range anxiety and increasing their acceptance of the technology. Hence, we introduce a multi-period alternative fuel refueling station location problem with routing under different vehicle flow evolution dynamics that employ various weights for the factors where the natural growth rate is exogenous and the decisions of station locations and flow coverage are endogenous to the problem. We propose three mixed integer linear programming formulations for different evolution dynamics. We carry out computational experiments on the real road networks of Belgium, California, and Turkey and present our findings on the performances of the proposed mathematical models and the gains that can be obtained by considering multi-period planning and incorporating the effects of decisions into the vehicle flow evolution.

The 3D localization of subsurface pipes from Ground Penetrating Radar images using edge detection and point cloud segmentation

This research introduces an innovative algorithm using Ground Penetrating Radar (GPR) for inspecting underground infrastructure non-destructively, surpassing R-CNN methods by combining signal processing with computer vision techniques including hyperbola correlation, point cloud segmentation and noise reduction based on linearity, density and echo patterns. Utilizing on-vehicle multi-channel GPR and without training, it maps pipes in 3D with high precision on both experimental fields and real roads. The method demonstrated remarkable accuracy, with an average error of 0.1175 m in locating pipes, outperforming deep learning methods requiring numerous training data. The ablation study and sensitivity analysis on hyperparameters further prove the improvement from the proposed method and its robustness. These outcomes offer notable improvements in infrastructure monitoring, highlighting the potential for automated underground utility detection.

Characteristics of tire-road wear particles (TRWPs) and road pavement wear particles (RPWPs) generated through a novel tire abrasion simulator based on real road pavement conditions

Microparticles such as tire-road wear particles (TRWPs) and road pavement wear particles (RPWPs) are generated by the friction between tire tread and road surface. TRWPs and RPWPs on roads are dispersed through traffic and transferred to rivers and seas via runoff to accumulate in sediments. However, research on the generation of both TRWP and RPWP has rarely been conducted. In this study, the generation of both TRWP and RPWP was investigated using a novel tire abrasion simulator equipped with paved road and bus tire, and their contributions to the generation of microparticles were examined. Two types of model paved roads, asphalt and concrete pavements (AP and CP, respectively), were applied. TRWPs generated from the simulator exhibited morphologies very similar to those on real roads. The abrasion rate for the CP was 2.8 times higher than that for the AP. The wear particle size distributions peaked at the size ranges of 63–106 μm and 212–500 μm for the AP and CP, respectively. Totals of 84 wt% and 89 wt% of the wear particles were distributed in size ranges of 38–212 μm for the AP and 106–1000 μm for the CP. The tire wear particle (TWP) contents in the total wear particles of 38–500 μm were 21.7 wt% and 30.0 wt% for the AP and CP, respectively, and decreased as the particle size decreased. The weight of RPWP was higher than that of TWP in TRWP. Contributions from road pavement to the generation of wear particles of 38–500 μm were 3.6 and 2.3 times higher than those from tire tread for the AP and CP, respectively, and the contribution increased as the wear particle size decreased.

An integrated navigation algorithm assisted by CNN-Informer during short-time GNSS outages

Abstract Inertial Navigation System (INS) and Global Navigation Satellite System (GNSSs) are commonly used in integrated navigation systems. The GNSS navigation system, however, is not autonomous, and its signals are susceptible to environmental influences leading to poor navigation results. In order to improve the positioning accuracy of the INS/GNSS integrated navigation system during GNSS outages, a hybrid algorithm of Convolutional Neural Network (CNN)-Informer model-assisted INS is proposed. The CNN-Informer model combines the advantages of each of the convolutional neural network and the Informer network. It utilizes CNN to rapidly extract spatial features from input data, employs the Informer network to establish relationships between inputs and outputs, and acquires pseudo-GNSS signals to compensate for position errors in the INS. Lastly, we evaluate the proposed algorithm through six sets of experiments conducted on public datasets and real road scenarios. The comparative results demonstrate that the proposed method offers a more accurate and reliable solution during GNSS outages. Specifically, CNN-Informer can improve navigation accuracy by 81.46% over MLP and by 58.91% over LSTM during 20-second GNSS outages.

Development of sleepiness in professional truck drivers: Real-road testing for driver drowsiness and attention warning (DDAW) system evaluation.

All new vehicle types within the European Union must now be equipped with a driver drowsiness and attention warning system starting from 2022. The specific requirements for the test procedure necessary for type approval are defined in the Annex of EU Regulation C/2021/2639. The objectives of this study were to: (i) investigate how sleepiness develops in professional truck drivers under real-road driving conditions; and (ii) assess the feasibility of a test procedure for validating driver drowsiness and attention warning systems according to the EU regulation. Twenty-four professional truck drivers participated in the test. They drove for 180 km on a dual-lane motorway, first during daytime after a normal night's sleep and then at nighttime after being awake since early morning. The results showed higher sleepiness levels during nighttime driving compared with daytime, with a faster increase in sleepiness with distance driven, especially during the night. Psychomotor vigilance task results corroborated these findings. From a driver drowsiness and attention warning testing perspective, the study design with sleep-deprived drivers at night was successful in inducing the targeted sleepiness level of a Karolinska Sleepiness Scale score of ≥ 8. Many drivers who reported a Karolinska Sleepiness Scale ≥ 8 during the drives also acknowledged feeling sleepy in the post-drive questionnaire. Reaching high levels of sleepiness on real roads during daytime is more problematic, not the least from legal and ethical perspectives as higher traffic densities during the daytime lead to increased risks.

Low-vacuum SEM imaging and viability test of L929 cells exposed to a Euro 6 diesel exhaust gas mixture in a BAT-CELL chamber in comparison with hydrocarbons emission

Exhaust emissions, which count among the most common causes of premature death worldwide, can cause irreversible changes in cells, leading to their damage or degeneration. In this research, L929 line cells were observed after exposure in the BAT-CELL chamber to exhaust gases emitted from a Euro 6 compression-ignition engine. Real road traffic conditions were simulated, taking into account air resistance while driving at speeds of 50 km/h, 120 km/h and idling engine. Morphological analysis of the cells was performed using an environmental scanning electron microscope. It has been observed that diesel exhaust fumes can cause inflammation, which can induce apoptosis or leads to necrotic cell death. The impact of the vehicle exhaust gases can inhibit cell proliferation by almost three times. Moreover, a correlation has been observed between the speed of the inflammatory reaction in cells and the presence of specific hydrocarbon compounds that determine the toxicity of exhaust gases. Research has shown that the toxicity of the emitted exhaust gases has been the highest at the driving speed of 120 km/h. In order to reduce the harmful effects of exhaust emissions, ecological alternatives and the supplementation of legal provisions regarding the compounds subject to limitation are necessary.

Enhanced real-time road-vehicles’ detection and tracking for driving assistance

Road-object detection, recognition, and tracking are vital tasks that must be performed reliably and accurately by self-driving car systems in order to achieve the automation/autonomy goal. Other vehicles are one of the main objects that the egocar must accurately detect and track on the road. However, deep-learning approaches proved their effectiveness at the expense of very demanding computational power and low throughput. They must be deployed on expensive CPUs and GPUs. Thus, in this work, a lightweight vehicle detection and tracking technique (LWVDT) is suggested to fit low-cost CPUs without sacrificing robustness, speed, or comprehension. The LWVDT is suitable for deployment in both advanced driving assistance systems (ADAS) functions and autonomous-car subsystems. The implementation is a sequence of computer-vision techniques fused together and merged with machine-learning procedures to strengthen each other and streamline execution. The algorithm details and their execution are revealed in detail. The LWVDT processes raw RGB camera pictures to generate vehicle boundary boxes and tracks them from frame to frame. The performance of the proposed pipeline is assessed using real road camera images and video recordings under different circumstances and lighting/shading conditions. Moreover, it is also tested against the well-known KITTI database, achieving an average accuracy of 87%.

River valley urban network and morphology: A study on the urban morphology evolution of Lanzhou.

The present study investigates the dynamic evolution characteristics of urban spatial morphology by analyzing real road network data from 2000, 2010, and 2020, along with nighttime lighting data employing spatial analysis methods and spatial syntax models. Accordingly, two separate dimensions of urban morphology: internal and external, are covered. First, the integration and synergy of interior morphology features are analyzed using spatial syntactic modeling. Subsequently, the spatial compactness, fractal dimension, and level of center of gravity shift of the city are assessed by combining the nighttime lighting data with the earlier dataset. This analysis facilitated the deep exploration of the spatiotemporal evolution of the city's external morphology. Building upon this foundation, the interaction between the "internal and external" domains was analyzed further. The main findings of the study reveal a synchronous pattern of urban expansion throughout the evolution of urban spatial morphology. Furthermore, the urban form was observed to undergo a progressive transformation, transitioning from a "single core" morphology to a "primary and secondary double core" morphology. Over time, this development progressed and evolved into a "belt-like multi-core" structure. Additionally, the coupling characteristics further validate the relationship between the structure of the road network and the urban morphology in river valley-type cities. In particular, accessibility of dense and horizontally distributed transportation network was found to significantly influence the spatial development of these cities. As observed, the findings provides valuable insights into understanding the characteristics of internal and external associations regarding urban spatial patterns.

Investigating the feasibility of dynamic speed limit on Indian highways

Speed limits that change as per real time traffic, road, and weather conditions are termed as Dynamic Speed Limits (DSL). Road users are informed of speed limit changes in DSL scheme by electronic signs or Variable Message Signs (VMS) placed above the lanes or side-mounted along highways. In India, most of the highways have static speed limits. The speed limits are kept lower at locations or stretches adjacent to roadside schools, market areas, bus stops and intersections, for enhancing safety of Vulnerable Road Users (VRUs). This causes higher travel time to through traffic while travelling through these stretches. DSL has the potential in reducing the travel time and the variability of speed of vehicles, thus helps in enhancing road safety. The present work investigates the feasibility of DSL on various segments of a typical Indian highway passing through various roadside developments and human activities using cost-benefit analysis. The travel time benefits due to DSL are estimated by comparing the travel times with static and dynamic speed limit conditions. The results indicate that there is a significant amount of saving in aggregate travel time of all vehicles passing through the stretch due to the application of DSL. The benefit cost ratios greater than unity indicated that it is beneficial to deploy DSL on several segments of the highway passing through roadside schools, market areas, bus stops and intersections. The Benefit-Cost Ratio is also used advantageously to prioritize viable locations for deployment of DSL. The findings from the present work are likely to be of interest to policy makers in different developing countries where infrastructure/facilities for VRUs are deficient, road safety is a major concern and speeding is identified as a major problem.

Comparison of Tire Rolling Resistance Measuring Methods for Different Surfaces

The rolling resistance of car tires is one of the most important parameters characterizing tires today. This resistance has a very significant contribution to the energy consumption of wheeled vehicles. The climate crisis has forced tire and car manufacturers to place great emphasis on the environmental impact of their products. Paradoxically, the development of electric vehicles has led to an even greater importance of rolling resistance, because in electric vehicles, a large part of the influence of grade resistance and inertial resistance has been eliminated due to re-generative braking, which resulted in rolling resistance and air resistance remain as the most important factors. What is more, electric and hybrid vehicles are usually heavier, so the rolling resistance is increased accordingly. To optimize tires for rolling resistance, representative test methods must exist. Unfortunately, the current standards for measuring rolling resistance assume that tests are carried out in conditions that are far from real road conditions. This article compares the results of rolling resistance tests conducted in road conditions with the results of laboratory tests conducted on roadwheel facilities. The overview of results shows that the results of tests conducted in accordance with ISO and SAE standards on steel drums are very poorly correlated with more objective results of road tests. Significant differences occur both in the Coefficients of Rolling Resistance (CRR) and in the tire ranking. Only covering the drums with replicas of road surfaces leads to a significant improvement in the results obtained. For investigations of rolling resistance in non-steady-state conditions, the flat track testing machine (TTF), equipped with asphalt cassettes, is shown to provide measurement data in agreement with the road test data.

Traffic trajectory data analysis technology based on HMM model map matching algorithm.

The rapid growth of traffic trajectory data and the development of positioning technology have driven the demand for its analysis. However, in the current application scenarios, there are some problems such as the deviation between positioning data and real roads and low accuracy of existing trajectory data traffic prediction models. Therefore, a map matching algorithm based on hidden Markov models is proposed in this study. The algorithm starts from the global route, selects K nearest candidate paths, and identifies candidate points through the candidate paths. It uses changes in speed, angle, and other information to generate a state transition matrix that match trajectory points to the actual route. When processing trajectory data in the experiment, K = 5 is selected as the optimal value, the algorithm takes 51 ms and the accuracy is 95.3%. The algorithm performed well in a variety of road conditions, especially in parallel and mixed road sections, with an accuracy rate of more than 96%. Although the time loss of this algorithm is slightly increased compared with the traditional algorithm, its accuracy is stable. Under different road conditions, the accuracy of the algorithm is 98.3%, 97.5%, 94.8% and 96%, respectively. The accuracy of the algorithm based on traditional hidden Markov models is 95.9%, 95.7%, 95.4% and 94.6%, respectively. It can be seen that the accuracy of the algorithm designed has higher precision. The experiment proves that the map matching algorithms based on hidden Markov models is superior to other algorithms in terms of matching accuracy. This study makes the processing of traffic trajectory data more accurate.

Emission characteristic, spatial distribution, and health risk of polycyclic aromatic compounds (PAHs, NPAHs, and OPAHs) from light-duty gasoline and diesel vehicles based on on-road measurements

Vehicle exhaust is the primary source of polycyclic aromatic compounds (PACs). Real road tests using a portable vehicle measurement system on light-duty gasoline vehicles and light-duty diesel trucks were conducted to investigate gas- and particle-phase polycyclic aromatic hydrocarbons (PAHs), nitro-PAHs (NPAHs), and oxy-PAHs (OPAHs) in vehicle exhaust with different emission standards, fuel types, and altitudes. The results showed that with the tightening of emission standards, the overall emission factors (EFs) of PACs decreased. Compared with China V diesel vehicles, the emissions of PAHs, OPAHs, and NPAHs from China VI diesel vehicles were 75.1 %, 84.4 %, and 61.2 % lower, respectively. With a ∼100 m increase in altitude, the EFs of PAHs, OPAHs, and NPAHs of diesel vehicles increased 1.88, 1.92, and 1.59 times due to incomplete combustion. In addition, the EFs of PAHs and OPAHs in gasoline vehicles were lower than those in diesel vehicles. In contrast, the proportion of PAHs with highly toxic components, such as dibenzo[a,h]anthracene (DahA) and benzo[a]pyrene (BaP), and the EFs of gas-phase NPAHs in gasoline vehicles were higher than those in diesel vehicles. Furthermore, the emissions of 1,8-DNP from diesel vehicles cannot be disregarded. 1,8-DNP was the main gas-phase NPAHs emitted by China VI and China V diesel vehicles, accounting for 49.3 % and 26.0 %, respectively. Moreover, gas-phase PACs contributed more to the EFs than particle-phase PACs, whereas particle-phase PACs have greater toxic effects. Although the EFs of PAHs are more than 100 times those of NPAHs, the toxic equivalent concentrations (TEQBaP) of PAHs in diesel and gasoline vehicles were approximately 6.5 times and 35 times those of NPAHs. The spatial distribution characteristics revealed that PACs emissions were mainly concentrated in urban areas and highways, and the differences in the toxicity of PACs emissions between different cities depended on the proportion of diesel vehicles. The average TEQBaP of PAHs and NPAHs in Haidong, Haibei, Huangnan, Hainan, Guoluo, and Yushu was 8.42 μg/m3 and 0.36 μg/m3, respectively, while those of Xining and Haixi were 0.24–0.29 μg/m3 and 0.09–0.108 μg/m3 higher, respectively. This study provides a comprehensive understanding of the emission characteristics, health risks, and spatial distribution of PACs from diesel and gasoline vehicle PACs in urban areas.

Adaptation and Validation of Injection Rate Predictive Model for Solenoid Type Injectors with Different Nozzle Geometry

The present research analyses the injection rate of a direct rail injection diesel engine, focusing specifically on the influence of the nozzles and various operating conditions from real road tests on the rate of injection. A diesel injector test bench was used for feedback with real data from the test vehicle under real road conditions. An analysis of the behaviour of the injection rate was carried out using the zero-dimensional model. This model generated a predictive model that incorporated the five variables identified through a developed multivariate analysis of variance, showing a high correlation of dependence between variations in injection pressure, the diameter of the holes, and the number of holes with greater representativeness. The results obtained showed that the nozzle geometry and the physical properties of the fuel had a direct effect on the injection rate. This analysis enriches the understanding of fuel injection and its effects on diesel engine performance by providing an analysis of the system components that influence the injection rate and generating a simple tool to feed thermodynamic diagnostic models. The proposal model may be used as an input in thermodynamics predictive models and reduce the simulation load in computational fluid dynamics predictive models.

A Feedback Active Control Approach to Road Noise Based on a Single Microphone Sensor to Improve Automotive Cabin Sound Comfort.

Tire-road noise deteriorates the sound quality of a vehicle's interior and affects the driving safety and comfort. Obtaining low interior noise is a challenge for passenger car manufacturers. Traditional passive noise control (PNC) is efficient for canceling high frequency noise but not useful for low frequency noise, while active noise control (ANC), according to the residual error signal, can generate an anti-noise signal to reduce the original noise. Most research has focused on improving the control effect for a feedforward ANC system. However, this paper emphasizes a feedback ANC system based on a signal microphone sensor. There are two main contributions in this study to improve automotive cabin sound comfort. One is that the algorithm of the feedback ANC system using a single microphone sensor without a reference noise signal is proposed based on the Filtered-x Least Mean Square method. The other is that the algorithm applies additive random noise online to estimate the secondary path model. A simulation was implemented based on measured real road noise data, and the simulation results indicate that the proposed feedback ANC system with the single microphone sensor can effectively attenuate road noise. This study shows the feasibility of applying a feedback ANC system in automobiles to increase the cabin sound quality.