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Driving a car at extreme speeds, road holding, and sustainability do not go together well. Formula 1 racing is exciting but is not an example of sustainability. The aim of this work was to use materials, suitable for the treads of high-performance racing tyres, that can favour both high performance and sustainability. In particular, the objective was to achieve high dynamic rigidity at high temperatures (>100 °C) and a stable crosslinking network. A copolymer from an industrial waste such as sulphur and a comonomer from a circular biosourced material were used as the crosslinking agent of an elastomer composite based on poly(styrene-co-butadiene) from solution anionic polymerization and a carbon black with a high surface area. The biosourced circular material was 1,6-bis(2,5-dimethyl-1H-pyrrol-1-yl)hexane (HMDP), the di-pyrrole derivative of hexamethylenediamine. Two poly(S-co-HMDP) copolymers, with different S/HMDP ratios (6 and 8.9, Copolymer 1 and Copolymer 2) were carefully characterized by means of 1H-, 13C-, 2D1H-1H-COSY and 2D 1H-13C HSQC NMR. The comparison of the spectra highlighted the substitution with sulphur of the β-position of the pyrrole ring: mono-substitution largely prevailed in Copolymer 1 and also bi-substitution in Copolymer 2. The copolymers were used as additives in the vulcanization system. Compared with a reference composite, they allowed us to achieve more efficient vulcanization, a higher density of the crosslinking network, higher dynamic rigidity, better ultimate tensile properties, and better stability of the crosslinking network at high temperatures. Compared with a traditional oil-based crosslinking agent for elastomer composites with high rigidity and a stable structure at high temperatures, such as the perthiocarbamate 6-((dibenzylcarbamothioyl)disulfaneyl)hexyl 1,3-diphenylpropane-2-sulfinodithioate, the poly(S-co-HMDP) copolymers led to higher dynamic rigidity and better ultimate tensile properties. These improvements occurring simultaneously are definitely unusual. This work paves the way for the upcycling of circular materials in a large-scale application such as in tyres. [ABSTRACT FROM AUTHOR]

Flying cars, or vertical takeoff and landing (VTOL) aircraft, are revolutionary devices that can address traffic congestion and help build a three-dimensional transportation system in the future. Flight safety could deteriorate in actual flight because ducted fans may suffer from significant thrust fluctuations induced by crosswinds. In this article, we propose a variable-speed control method to reduce thrust fluctuations by applying a speed waveform opposite to the fluctuation waveform. We choose a typical condition of a 5 m/s crosswind for validation. The results of unsteady Reynolds averaged Navier‒Stokes (URANS) calculations show that crosswinds lead to fluctuations in all thrusts with the frequency of the blade passing frequency. The total thrust fluctuation amplitude accounts for 6.2% of the total thrust. By applying the variable-speed control where the frequency is identical to the thrust fluctuation frequency, the phase difference is 180 degrees, and the amplitude is approximately the square root of the fluctuation amplitude, the standard deviation of the thrust fluctuation is reduced by 92% without affecting the mean total thrust. Fluctuations in other performance parameters, such as lateral force and pitching moment, are improved as well. This active control method can achieve accurate control without any auxiliary equipment and has good application prospects. It provides a promising idea for solving the problem of performance fluctuations in turbomachinery. [ABSTRACT FROM AUTHOR]

Fusing data from many sources helps to achieve improved analysis and results. In this work, we present a new algorithm to fuse data from multiple cameras with data from multiple lidars. This algorithm was developed to increase the sensitivity and specificity of autonomous vehicle perception systems, where the most accurate sensors measuring the vehicle's surroundings are cameras and lidar devices. Perception systems based on data from one type of sensor do not use complete information and have lower quality. The camera provides two-dimensional images; lidar produces three-dimensional point clouds. We developed a method for matching pixels on a pair of stereoscopic images using dynamic programming inspired by an algorithm to match sequences of amino acids used in bioinformatics. We improve the quality of the basic algorithm using additional data from edge detectors. Furthermore, we also improve the algorithm performance by reducing the size of matched pixels determined by available car speeds. We perform point cloud densification in the final step of our method, fusing lidar output data with stereo vision output. We implemented our algorithm in C++ with Python API, and we provided the open-source library named Stereo PCD. This library very efficiently fuses data from multiple cameras and multiple lidars. In the article, we present the results of our approach to benchmark databases in terms of quality and performance. We compare our algorithm with other popular methods. [ABSTRACT FROM AUTHOR]

Copyright of Smart Rail Transit is the property of Smart Rail Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nearly 3700 people are killed in broadside collisions in the U.S. every year. To reduce broadside collisions, we created and tested the CornerGuard, a prototype system that senses around a corner to alert a car driver of an impending collision with a pedestrian or automobile that is not in the line of sight (LOS). The CornerGuard leverages a microwave-transceiving radar sensor mounted on the car and a curved radio wave reflector installed at the corner to sense around the corner and detect a broadside collision threat. The car's speed is constantly read by an onboard diagnostics (OBD) system to allow the sensor to differentiate between static objects and objects approaching around the corner. Field testing demonstrated that the CornerGuard can effectively and consistently detect threats at a consistent range without blind spots under broad weather conditions. Our proof of concept study shows that the CornerGuard can be enhanced to be readily integrated into automobile construction and street infrastructure. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Coach car is a commonly used mode of transportation, due to its large passenger capacity, in case of accidents, it will cause enormous human casualties and economic losses. To improve the braking performance of coach car, an aerodynamic plate is installed on the top of coach car to assist braking. Based on RANS (Reynolds average Navier-Stokes) equations and SST (Shear-Stress Transport) k-ω turbulence model, the aerodynamic performance of coach car with different aerodynamic parameters is analyzed by computational fluid dynamics method. The numerical algorithm and setting are verified by wind tunnel tests. In this paper, the influence of four parameters, including car speed, plate configuration of maximum opening angle, installation position, and plate height, on the aerodynamic drag and lift coefficient of coach car is discussed by using control variable method and orthogonal test method. The conclusions of the two methods are consistent, that is, the optimal combination of plate configuration is that installation position, maximum opening angle, and height are 10 m, 80°, and 1 m, respectively. According to the results of range and variance analysis, the order of influence of aerodynamic parameters on the aerodynamic drag and lift coefficient is installation position, maximum opening angle, height, and car speed. [ABSTRACT FROM AUTHOR]

This paper presents an Event-B model of a speed control system, a part of the case study provided in the ABZ2020 conference. The case study describes how the system regulates the current speed of a car according to a set of criteria like the driver's desired speed, the position of a possible preceding vehicle, but also a given speed limit that the driver must not exceed. For that purpose, this controller reads different information from the available sensors (key state, desired speed) and takes adequate actions by acting on the actuators of the car's speed according to the information read. To formally model this system, we adopt a stepwise refinement approach with the Event-B method. We consider most of the features of the case study. All proof obligations of the invariant properties have been discharged using the Rodin provers. Our model has been validated using ProB by applying the different provided scenarios. This validation has permitted us to point out and correct some mistakes, ambiguities and oversights contained in the first versions of the case study. [ABSTRACT FROM AUTHOR]

Our primary goal is to develop a fully automated car parking system that requires minimal human intervention. With the increasing global population, time efficiency has become crucial, necessitating the reduction of time spent on trivial activities like searching for parking spaces in busy areas and alleviating traffic congestion. Existing systems have demonstrated that accidents can occur in parking situations due to cars speeding or frustrated drivers unable to find parking for an extended period. In our project, we propose a smart and automated car parking model that allows users to pre-book their parking spaces. Once in the designated parking zone, the vehicle will be able to park automatically. What sets our automated car parking system apart is our emphasis on minimizing human involvement. Both the vehicle and the parking area will be equipped with sensors, enabling a safe and efficient parking process. Our aim is to provide a completely secure and automated experience that is reliable and can be implemented in real-time, with the hope of establishing it as the standard for parking systems in the future. [ABSTRACT FROM AUTHOR]

To determine the speed of a vehicle in a collision with body deformation, the kinetic energy input of the vehicle to cause body damage must be estimated. This paper analyzes the methods for estimating the energy equivalent of vehicle damage. A finite element model of a Toyota Yaris developed by the National Crash Analysis Center (NCAC) for use in the LS DYNA R.11.0.0 software environment is used for the simulation. The simulations include tests of the vehicle hitting a non-deformable wall, an object simulating a pole or a tree. The residual deformations obtained are used to determine the energy equivalent speed (EES) values using the "Crash 3—EBS Calculation 12.0" software and a visual comparison with the EES catalog database, where the EES parameter value is recalculated to take into account the difference in the mass of the vehicles. [ABSTRACT FROM AUTHOR]

Aiming at the poor transient convergence and real-time performance of the horizontal vibration state variables of the high-speed elevator car system, and the low control accuracy and stability, this paper proposes an adaptive control strategy to ensure the transient response of the elevator car system. First, a prescribed preset transient performance function is introduced into the controller design to control the variation range of the state variables and ensure the steady transient performance of the elevator car; Second, representing the information of observation error/control error through algebraic operations, designing an adaptive law based on e-correction, estimating unknown parameters in the elevator car system, and achieving online parameter updates; Then, using neural networks to learn and compensate for unknown dynamics in the elevator car system, and solving the online estimation problem of neural network weights through adaptive laws, so that the tracking error and weight estimation error converge to a tight set near zero; Finally, using MATLAB/SIMULINK to compare and analyze the four control algorithms of passive control, PID control, adaptive control based on gradient descent method and transient response adaptive control proposed in this paper under two different rail excitations: Random excitation and pulse excitation. The simulation results show that the adaptive control strategy proposed in this paper effectively suppresses the horizontal vibration of the elevator car, makes the state variables have faster convergence speed and smaller convergence error, and ensures the stable and transient performance of the elevator car system. [ABSTRACT FROM AUTHOR]

The hydrodynamic model can be used to describe traffic problems in transport. When the speed of the first car is less than the speed behind it, it leads to traffic jams. When the first car's speed is faster than the cars behind it, it leads to traffic evacuation. If we consider the first car to be a piston, then the speed of the piston will cause traffic jams and traffic evacuation. In this paper, we study the piston problem for the hydrodynamic model. The formation and propagation of shock wave, rarefaction wave, delta-shock wave, and vacuum can describe the phenomena of traffic jams, traffic evacuation, severe traffic jams, and traffic evacuation with traffic volume of zero, respectively. Therefore, for different traffic phenomena, we prove the existence of shock solution, rarefaction solution, delta shock solution, and vacuum solution. In addition, we perform some representative numerical simulations. [ABSTRACT FROM AUTHOR]

Mobile and handheld electronic products such as smartphones and dashboard cameras are frequently used to record occurrences of traffic accidents nowadays. Most existing studies require at least one entire lane marking as a reference to calculate a certain amount of driving distances and are not adapted to a targeted vehicle with a rapidly changing speed in a short time. Therefore, existing approaches cannot provide the desired accuracy of speed estimation in real-life scenarios. In this study, we obtain dynamic time and space data from recorded footage using dashboard cameras and then apply photogrammetry and cross-ratio methods to estimate the vehicle speed. In addition, the proposed method is applied to other cars' speed estimation and ego-speed estimation even though both cars are moving. The experimental results show that given the frame rate on the recorded footage, our proposed method only needs one object in each frame to estimate the vehicle speed even at a rapidly changing speed. Our proposed method shows that the difference between the estimated speed and the reference speed by the Global Positioning System (GPS) is smaller than 1 km/h when only one car is on the move, and smaller than 3 km/h when both cars are on the move. Nevertheless, the difference between the estimated speed and the reference speed is between 1.46 km/h and 5.28 km/h when one car moves by AUPD method. To our knowledge, there is no existing method that estimated the front vehicle speed when both cars are on the move using dashboard cameras. [ABSTRACT FROM AUTHOR]

The object of this study is the cross-country ability of four-wheel drive vehicles under off-road conditions. Based on the results of analysis of known studies, it was determined that the procedures of experimental assessment of the load-bearing capacity of bearing surfaces (BS), based on the use of the cone index, are used in NATO member countries. They take into account the characteristics of the surface of the theater of operations and make it possible to determine the approximate speeds of all-wheel drive vehicles with the help of standardized computer software. In the procedures for assessing the load-bearing capacity of BSs, which use the soil deformation module E, there is no possibility for calculating the speed of the car with known axle loads. In addition, they provide for the determination of a number of parameters of the bearing surface, such as the coefficient of adhesion, the angle of internal friction in the soil, and the shear modulus during the formation of a track. This makes them much more difficult compared to the procedures used by NATO. Therefore, the problem that is solved in this work is to improve the methodology for assessing the reference cross-country ability of four-wheel drive vehicles. Based on the results of scientific research, motion modeling was carried out in the MATLAB Simulink environment to determine parameters of reference cross-country ability of four-wheel drive vehicles. In order to determine the cross-country ability of samples of wheeled military vehicles (WMV) based on the indicator of the maximum pressure value (mean measure pressure – MMP), the conducted experimental studies were analyzed. They showed a difference in the speed modes of vehicles of the same gross weight but different layout schemes, within 11–12 %. Therefore, the methodology for assessing the cross-country ability based on MMP has been improved to take into account the features of the layout of WMV samples. Specifically, taking into account the different values of loads on the front axle of vehicles with hoodless and hood layouts at the same gross weight. In general, the improved procedure makes it possible to give a quantitative assessment of the movement mobility of WMV samples with various layout schemes and an assessment of their potential cross-country ability on the basis of the proposed refinement of MMP calculation [ABSTRACT FROM AUTHOR]

We consider the car following problem for a set of autonomous vehicles following each other on either an infinite or circular road. The behavior of each car is specified by its "speed regulator", a device that decides to increase or decrease the speed of the car as a function of the head-tail distance to its predecessor and the speed of both cars. A collective behavior emerges that corresponds to previously proposed cellular automata traffic models. We further analyze the traffic patterns of the system in the long term, as governed by the speed regulator and we study under which conditions traffic patterns of maximum flow can or cannot be reach. We show the existence of suboptimal flow conditions that require external coordination mechanisms (that we do not consider in this paper) in order to reach the optimal flow achievable with the given density. In contrast with other approaches, we do not try to reproduce observed or measured traffic patterns. We analyze a deterministic speed regulator in order to decipher the emergent dynamics, and to ponder what maneuvers can be safely performed. Here, we restrict our attention to the car following problem. By comparing our speed regulator with classical models, auch as the Nagel–Schreckenberg and KKW models, we observe that although our regulator is formulated in simple terms, its dynamics share similarities with these models. In particular, the KKW model is designed to reproduce the observed behavior that a trailing car in the synchronization range of the leading car tends to regulate its speed to maintain a constant distance. this same behavior is adopted by our speed regulator, showing that this is a safe way of driving. [ABSTRACT FROM AUTHOR]

How to effectively and accurately evaluate and analyze the volatility and reliability of travel speed on urban road before and after students back to school is a hot and key problem in urban road traffic congestion governance research. The Beijing 3rd Ring Road was taken as the research object and the impacts of the students back to school on the volatility and reliability of the travel speed of road sections were qualitatively and quantitatively analyzed based on the road section travel speed data during the weekday morning peak (7:00–8:59). The results showed that the travel speed of the Beijing 3rd Ring Road had cyclicity, time variability, large-scale volatility, and light congestion during the weekday morning peak, and the volatility and reliability indexes of the travel speed of road sections significantly decreased under the impact of the students back to school. The data showed that after the students back to school, the maximum reduction ratio of average travel speed was larger than 55%, and the maximum travel speed reliability reduction value was larger than 0.85 based on the evaluation model of travel speed reliability of car commuters. The research results provide data and theoretical support for urban road traffic congestion mitigation and governance. [ABSTRACT FROM AUTHOR]

The Fourth Industrial Revolution has had a huge impact on manufacturing processes and products. With rapidly growing technology, new solutions are being implemented in the field of digital representations of a physical product. This approach can provide benefits in terms of cost and testing time savings. In order to test and reflect the operation of an electric car, a digital twin model was designed. The paper collects all the information and standards necessary to transform the idea into a real and virtual model of an electric car. The significance and impact of the study on the improvement of the project are described. The research stand, correlations of components (DC and AC motors, shaft, and wheel of the electric car), and development prospects are presented in the paper. The communication method with the research stand is also presented. The digital twin should communicate in real time, which means obtaining the correct output when the input changes; the input is the AC motor current, and the output is the rotational speed of the DC motor. The relation between inputs and outputs are tested. The kinematics of the electric car are modelled in LabVIEW. The results obtained are compared with historic racing data. The track is also modeled based on satellite data, taking into account changes in terrain height, using the SG Telemetry Viewer application. The parameters of the electric car engine model are tuned based on actual data on the car's speed and current in the electric motor. The achieved results are presented and then discussed. [ABSTRACT FROM AUTHOR]

This paper proposes an energy management strategy (EMS) based on double‐deep Q‐Networks (DDQN) with demand torque prediction (DTP) to optimize the fuel consumption of hybrid electric vehicles (HEVs) by online utilizing vehicle‐to‐vehicle (V2V) information. The main framework of the EMS is designed as DDQN, combining Q‐learning with the deep neural network to realize real‐time training and online optimization of the torque distribution between the internal combustion engine and the electric motor, and given that the model training easily falls into local minima and overestimation, two different Q‐networks are designed to decouple action selection and target evaluation. Meanwhile, BP‐network predicts future demand torque using the ego vehicle speed, front vehicle speed, and distance between the two vehicles to reduce the burden of model training due to the introduction of traffic information. The predicted demand torque is introduced into the DDQN‐based EMS as states, together with the distance traveled in the driving cycle and the current information on HEV. The effectiveness and adaptability to actual driving cycles of the proposed strategy are verified by comparison with DDQN‐based EMS without DTP and rule‐based EMS. The results show that the fuel consumption using DDQN‐based EMS without DTP is reduced by 4.1% compared with rule‐based EMS. The introduction of demand torque prediction resulted in a further 3.9% reduction in fuel consumption. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Inappropriate or unsafe speed is one of the main factors that affects the number of road crashes as well as the severity of the consequences. Research shows that speed is an influential factor in the occurrence of road crashes in more than 30% of road crashes with fatal outcomes and in over 12% of all road crashes. With an increase in speed, the risk of road crashes increases as well as the severity of the consequences. The perception of the vehicle speed in the traffic lane is one of the basic prerequisites for the safe functioning of traffic, that is, for the successful and timely interaction of all road users. Therefore, the challenge of this paper is to examine how the assessment of the speed of a passenger vehicle in different environments affects the prediction of the respondent's participation in road crashes. Bearing the above in mind, an experimental study was carried out, in real traffic conditions (RTC) as well as in a virtual environment using a driving simulator (DS), at different passenger vehicle speeds (30, 50 and 70 km/h), and at different perspectives of observing the oncoming vehicle (observing the vehicle from the front, from the back, from the side and from the driver's seat) by the respondents. The respondents had the task of evaluating the passenger vehicle speed, in all tested conditions and at all tested speeds. Standard statistical models and fuzzy logic were used to analyze the obtained results. The results show statistically significant differences for all tested situations and all tested speeds as well as statistically significant differences depending on the gender of the respondents, the driver's license category, the driver's experience, frequency of driving and depending on whether respondents wear glasses. Bearing in mind the results of the developed model, by applying fuzzy logic, it can be concluded that the proposed model can be used to assess the propensity of respondents to participate in road crashes, based on perception of vehicle speeds in two tested environments. [ABSTRACT FROM AUTHOR]

In this paper, we present a proposed scheme for speed tracking control specifically designed for freight trains. This innovative speed tracking approach effectively prevents wheel slippage and ensures optimal speed control of the traction motor. The strategy integrates the use of Direct Torque Control (DTC), a technique employed by the HXD1 electric traction locomotive to regulate the asynchronous motor. To achieve velocity tracking control, we implement a Predictive Auto Disturbance Rejection Control (PADRC) system. Notably, the PADRC system includes an output prediction module estimator that enables accurate forecasting of time-delay system responses. Additionally, we develop an Unscented Kalman Filter (UKF) observer and seamlessly integrate an adaptive parameter adjustment mechanism, powered by Dung Beetle Optimizer-Fuzzy Neural Network (DBO-FNN), into the observer architecture. By utilizing the anti-slip parameters obtained through this observer, we determine the control scheme for anti-skid control. We validate the efficacy of this scheme through simulations using an actual speed curve of a freight train under both wet and dry pavement conditions. The results show a remarkable improvement in speed tracking accuracy, with respective increases of 66.45% and 56.29% over Adaptive Model Predictive Control (AMPC) and Non-Linear Auto Disturbance Rejection Control (NLADRC), in terms of speed tracking. Moreover, the tracking stationarity witnesses notable enhancements, increasing by 42.07% and 60.87% respectively. Additionally, the anti-slip performance of trains running on dry and wet tracks increased by 12.45% and 26.56%, respectively. [ABSTRACT FROM AUTHOR]

As technology advances in almost all areas of life, many companies and researchers are working to develop fully autonomous vehicles. Such level 5 autonomous driving, unlike levels 0 to 4, is a driverless vehicle stage and so the leap from level 4 to level 5 autonomous driving requires much more research and experimentation. For autonomous vehicles to safely drive in complex environments, autonomous cars should ensure end-to-end delay deadlines of sensor systems and car-controlling algorithms including machine learning modules, which are known to be very computationally intensive. To address this issue, we propose a new framework, i.e., an environment-driven approach for autonomous cars. Specifically, we identify environmental factors that we cannot control at all, and controllable internal factors such as sensing frequency, image resolution, prediction rate, car speed, and so on. Then, we design an admission control module that allows us to control internal factors such as image resolution and detection period to determine whether given parameters are acceptable or not for supporting end-to-end deadlines in the current environmental scenario while maintaining the accuracy of autonomous driving. The proposed framework has been verified with an RC car and a simulator. [ABSTRACT FROM AUTHOR]

The curve geometry influences the driver's speed choice behavior when passing through a horizontal curve. Hence, speed prediction models are essential to ensure highway safety, particularly under heterogeneous traffc conditions prevailing in India, where various classes of vehicles occupy the roadway space. Toward this, the present study aims to develop an operating speed increment and decrement model to identify the influence of the highway geometry variables on the drivers' speed choice behavior. The National Highway NH-953 is located on hilly terrain, from which 15 curves with various geometric characteristics were selected. The speed data were collected using the radar gun for three dominant vehicle types: motorized two-wheelers (2Ws), cars, and heavy commercial vehicles (HCVs) at three curve points. After that, the two types of speed differences are calculated: the differential of 85th percentile speed (ΔV85) and 85th percentile speed differential (Δ85V). The backward stepwise regression (BSR) technique was used on the collected speed data to develop the operating speed increment and decrement models. The results revealed that the curve geometric variables like curve radius, deflection angle, curve length, and tangent length significantly influence the drivers' speed choice behavior passing through the horizontal curves. Besides, for all three vehicle types, speed increments and decrement models ΔV85 underestimated Δ85V by 3 to 13 km=h [ABSTRACT FROM AUTHOR]

Purpose: This study aims to achieve optimum flow separation control for a road vehicle using a reverse flow fan on rear side. Design/methodology/approach: A full-length reverse flow fan array (fan's air speed is 50% of the car's speed) is attached throughout the width of the vehicle at rear edge corner. Findings: The reverse flow fan array positioned at rear edge of car pushes the airflow against the car's rear window. It creates the recirculation region and alters the pressure distribution. This reduces the lift coefficient by 150%, which becomes the downforce and reduces the drag coefficient by 22%. As the car speed increases, fan speed should also be increased for effective flow control. Research limitations/implications: This active flow control method for 3D Ahmed car body has been studied computationally at low speed (40 m/s). Practical implications: This design increases the downforce, thus gives better cornering speed and stability, and decreases the drag which improves fuel efficiency. It can be used for effective flow control of cars (hatchback/sedan). The findings can be applied to the bluff bodies, road vehicles, UAV and helicopter fuselage for flow separation control. Originality/value: The fan array is attached on car's rear side, which blows air against the car's rear window. It alters the pressure distribution and aerodynamics forces favorably. But the existing high-speed fan used in a sports cars sucks the air from bottom and pushes it rearward, which increases both the traction force and drag. [ABSTRACT FROM AUTHOR]

The rapid global expansion of the taxi industry has brought significant safety concerns regarding taxi trips, particularly related to the frequency of taxi crashes and their potential impact on road safety. To address this issue, the present study focuses on investigating the impact of time pressure on the driving speed of male taxi drivers through the use of driving simulator experiment. The aim of this study was to understand how time pressure influences driving speed at speed violation camera locations. A total of 56 taxi drivers participated in the study, with five drivers excluded due to simulation sickness. The findings revealed a significance of time pressure as a critical factor influencing driving speed among taxi drivers. Specifically, the study results showed that under time pressure situation, taxi drivers displayed significantly higher speeds, particularly in proximity to speed camera locations. To improve road safety, policymakers should focus on addressing time pressure concerns among taxi drivers. Implementing strategies to alleviate time constraints and optimize trip schedules can lead to safer driving practices, benefiting both drivers and passengers. [ABSTRACT FROM AUTHOR]

This article will focus on adaptive cruise control in autonomous automobiles. The adaptive cruise control inputs are the safety distance which determines thanks to conditions set depending on the distance value, the measured distance, the longitudinal speed of the autonomous automobile itself, the output is the desired acceleration. The objective is to follow the vehicles in front with safety, according to the distance measured by the ultrasonic sensor, and maintain a distance between the vehicles in front greater than the safety distance which we have determined. For this, we used super twisting sliding mode controller (STSMC) and non-singular terminal sliding mode controller (NTSMC) based on neural network applied to the adaptive cruise control system. The neural network is able to approximate the exponential reaching law term parameter of the NTSMC controller to compensate for uncertainties and perturbations. An autonomous automobile adaptive cruise control system prototype was produced and tested using an ultrasonic sensor to measure the distance between the two automobiles, and an Arduino board as a microcontroller to implement our program, and four DCs motors as actuators to move or stop our host vehicle. This system is processed by code and Simulink Matlab, the efficiency and robustness of these controllers are excellent, as demonstrated by the low longitudinal velocity error value. The safety of autonomous vehicles can be enhanced by improving adaptive cruise control using STSMC and NTSMC based on neural network controllers, which are chosen for their efficiency and robustness. [ABSTRACT FROM AUTHOR]

The actual service life of the pavement is related to dynamic tire loads. To accurately calculate dynamic loads, a multibody dynamics model of a 6-axle heavy truck was developed. The difference between simulated and experimental RMS values of wheel centre force and acceleration was within 15%. Based on the validated dynamics model, the effects of ride quality index (RQI), vehicle speed, load capacity, and suspension damping coefficient on dynamic impact factor (DI) and dynamic load amplitude (DLA) were explored. When the vehicle was fully loaded, both DI and DLA increased with decreasing RQI, increasing vehicle speed, and increasing suspension damping coefficient. Among them, the effect of RQI and vehicle speed on DLA and DI was much greater than that of suspension damping factor. In addition, the dispersion of DI values for each axle increased as the vehicle mass decreased. Therefore, it is necessary to consider the combined effects of RQI, vehicle speed and load capacity when applying dynamic tire loads in pavement design or life estimation. The procedure in this paper facilitates the accurate calculation of dynamic loads for different pavement and vehicle parameters and is recommended to be used in vehicle-pavement coupled analysis. [ABSTRACT FROM AUTHOR]

This paper proposes a method for estimating the speed and position of unsampled vehicles using sampled data from connected automated vehicles (CAVs). The determination of vehicle speed and position on the road is a challenging and crucial task, as they can effectively reflect traffic flow characteristics and contribute to traffic state estimation and intersection signal timing optimization. Connected automated vehicles have the capability to upload their own trajectory data while also capturing trajectory data of surrounding vehicles through onboard sensors. Therefore, this paper proposes a novel approach to estimate the speed and position of unsampled vehicles. Firstly, using real vehicle trajectory data, the correlation between the velocity of following vehicles and the velocity of leading vehicles under different densities is analyzed, leading to the development of a velocity estimation model incorporating a speed correction factor. Secondly, the correlation between time headway, the rate of change of following vehicle acceleration, and traffic density is examined. To address the issue of heterogeneous behavior in vehicle following described by the Intelligent Driver Model (IDM), a real-time optimization model for estimating vehicle position by optimizing IDM parameters is proposed. The velocity estimation model and the position estimation model are summarized as two nonlinear optimization problems. Finally, the proposed method is validated using actual vehicle trajectory data. Experimental results demonstrate that when the number of connected automated vehicles (CAVs) is 2, the proposed method reduces the average absolute error by 30.73% and the standard deviation of the average absolute error by 42.8% compared to a linear model-based speed estimation method under different density conditions. Compared to a method that estimates vehicle position by calibrating desired gaps, the proposed method reduces the average absolute error by 38.2% and the standard deviation of the average absolute error by 41.7% under different density conditions. Furthermore, the proposed method exhibits good practicality under different CAV penetration rates. [ABSTRACT FROM AUTHOR]

In the experiments, 34 white thoroughbred rats aged 1,3,6 months were used. These are white non-breeding rats of both sexes weighing on average about 250-300 g. The rats were fixed on a self-propelled vehicle with manual control, and in the moving vehicle, the experimental rats approached the wooden barrier and hit them on the forehead, which led to brain injury. The speed of the car was 6.7 km per hour. In the course of experiments on a traffic accident, 30 rats survived and 4 rats died. In general, i.e. macroscopic view of the thymus of 1,3,6-month-old white rats, changes are observed depending on the month, varying degrees of morphological changes in the state of fatty metamorphosis in the thymus cortex, consisting of lymphoid cells (thymocytes), blood vessels, Hassali cells and epithelial cells. [ABSTRACT FROM AUTHOR]

The high-speed airflow generated when an ultra-high-speed elevator runs in the hoistway will cause a significant piston effect. Therefore, it is essential to study the variation law of the piston wind in the hoistway and the influence of different parameters on the piston wind in the hoistway. This paper first establishes the theoretical model of the piston wind in the hoistway when the elevator ascends, descends, and intersects based on the unsteady flow theory of the Bernoulli principle. The effectiveness and accuracy of the theoretical model are verified through experimental measurements and numerical simulations. Second, based on the verified theoretical model, the influence of the five parameters of the car speed, car height, hoistway height, blockage ratio, and frictional resistance coefficient on the variation law of the piston wind and ventilation rate in the hoistway is systematically analyzed. Integrating the five parameters, a multi-parameter universal theoretical formula for hoistway ventilation rate is proposed. Finally, a gray correlation analysis is performed on each influencing parameter, which showed that the piston wind in the elevator hoistway is most affected by the blockage ratio, followed by the car speed, hoistway height, and frictional resistance coefficient, and is least affected by the car height. [ABSTRACT FROM AUTHOR]