Vehicle Response Research Articles

Revolutionizing emergency vehicle response: a smart traffic management approach with adaptive CNN and hybrid deep learning

Revolutionizing emergency vehicle response: a smart traffic management approach with adaptive CNN and hybrid deep learning

Ride comfort assessment of road vehicles on a long-span truss girder suspension bridge under crosswinds

The precise identification of the aerodynamic loads on vehicles is a fundamental concern in assessing the ride comfort of vehicles crossing long-span bridges in the presence of strong crosswinds. However, the consideration of aerodynamic interference between vehicles and truss girder bridges is limited in existing studies. Furthermore, in practical engineering, wind barriers are commonly installed to enhance the ride comfort of vehicles crossing long-span bridges, but previous studies have primarily focused on the impact of wind barriers on the aerodynamic coefficients and dynamic responses of vehicles. This paper focuses on the aerodynamic interaction between trucks and truss girder bridges, through wind tunnel testing. The aerodynamic coefficients of the vehicle with three different ventilation ratios and four different heights of wind barriers were obtained. The dynamic response of road vehicles on a truss girder suspension bridge with a main span of 965 m was numerically calculated using the coupled analysis of wind-vehicle-bridge system and measured aerodynamic coefficients considering aerodynamic interference. On this basis, the comfort of road vehicles was investigated with different section types of the girder and different wind barrier parameters, and it was evaluated accordance with the overall vibration total value (OVTV) recommended in the ISO 2631–1. It is found that the aerodynamic coefficients of road vehicles are sensitive to the type of wind barrier. Wind barriers can reduce the road vehicle’s aerodynamic loads at high wind yaw angles. Wind barriers can also improve the ride comfort of road vehicles on the long-span truss girder suspension bridge.

Nonlinear model predictive dynamic positioning of a remotely operated vehicle with wave disturbance preview

Exploiting preview information of disturbances within robot control is an effective method of handling state perturbations. For underwater vehicles operating in wave-dominated environments, disturbances significantly influence vehicle response and pose a threat to operational safety. In consideration of this, a complete end-to-end control architecture is developed in this work for disturbance rejection during station keeping tasks under wave perturbations, encompassing a nonlinear model predictive controller (NMPC) combined with a deterministic sea wave predictor (DSWP). The wave predictor exploits a continuous measurement of the wave elevation at a location upstream of the vehicle to form a forecast of the temporal evolution of the wave elevation at the vehicle location. The predicted wave parameters are then used to estimate the impending wave-induced hydrodynamic loading, enabling explicit consideration of accurate short-term disturbance forecasts within the controller, ensuring this preview is incorporated in the optimised control sequence. Experimental testing confirms the validity of the wave predictor, producing RMSE as low as 0.017 m. The proposed strategy is then simulated under various wave conditions, optimising control actions inclusive of the preview information. The NMPC outperforms a similar disturbance mitigating feed-forward controller, with average improvements of up to 52% and is found to perform best even with noisy, lower accuracy wave predictions, as well as with communication time delays, providing a high degree of robustness. This demonstrates the potential of the proposed control framework to effectively tackle disturbance mitigation of underwater vehicles subject to large magnitude wave disturbances, expanding the operational envelop of autonomous systems towards forbidding ocean environments.

Real-Time Embedded Control of Vehicle Dynamics Using ESP32: A Discrete Nonlinear Approach

This article explores the application of the Espressif ESP32 System-On-Chip (SoC) for managing vehicle dynamics through real-time digital proportional–integral (PI-like) control. We present the development of advanced driving assistance algorithms for Active Front Steering (AFS) and Rear Torque Vectoring (RTV) on this cost-effective, commercially available embedded system. Using digital PI-like control algorithms designed for AFS and RTV, the primary ESP32 board receives and processes steering signals, executing a discrete-time control model of the vehicle dynamic to enable dynamic adjustments to steering and torque. To enhance simulation realism, a secondary ESP32 is employed to generate the steering signal, effectively mimicking a steer-by-wire system via its analog output ports. This configuration facilitates the simulation and evaluation of control algorithms in a realistic test environment, ensuring enhanced vehicle dynamic stability and maneuverability under various conditions. Additionally, simulations are conducted using MATLAB 2023a and CarSim 2017.1 to compare the efficacy and benefits of the implementation. Our objective is to establish a platform for evaluating discrete controllers capable of real-time vehicle operation. This methodology accelerates and reduces the cost of improving vehicle system stability and responsiveness, enabling the immediate verification and fine-tuning of control parameters as needed.

Researching The Dynamic Response Law of High and Steep Subgrade Slopes Using ABAQUS

The study aimed to investigate the dynamic response of vehicles traveling on expressways across high and steep subgrade slopes under vehicle loads. This was done by creating a finite element model of high and steep subgrade slopes through ABAQUS software and its secondary program development. Specific engineering examples were used to analyze the dynamic response states and laws of high and steep subgrade slopes under varying vehicle speeds and load conditions. The study's results indicate that: (1) When the vehicle is travelling at 100 km/h, its vibration frequency is close to the natural frequency of the subgrade in the section, leading to strong dynamic responses. (2) As the number of axles increases, the pressure on the contact surface between the wheel and the roadbed rises, and dynamic responses become stronger. The four-axle vehicle shows the most significant dynamic response. (3) The maximum vertical stress fluctuates around an intermediate value over time. (4) The highest maximum vertical displacement value is observed in the penultimate analysis step, with a noticeable peak value. At this point, the vehicle load has the most prominent impact on the roadbed slope.

Multimodal Dataset Construction and Validation for Driving-Related Anger: A Wearable Physiological Conduction and Vehicle Driving Data Approach

Anger impairs a driver’s control and risk assessment abilities, heightening traffic accident risks. Constructing a multimodal dataset during driving tasks is crucial for accurate anger recognition. This study developed a multimodal physiological -vehicle driving dataset (DPV-MFD) based on drivers’ self-reported anger during simulated driving tasks. In Experiment 1, responses from 624 participants to anger-inducing videos and driving scenarios were collected via questionnaires to select appropriate materials. In Experiments 2 and 3, multimodal dynamic data and self-reported SAM emotion ratings were collected during simulated and real-vehicle tasks, capturing physiological and vehicle responses in neutral and anger states. Spearman’s correlation coefficient analysis validated the DPV-MFD’s effectiveness and explored the relationships between multimodal data and emotional dimensions. The CNN-LSTM deep learning network was used to assess the emotion recognition performance of the DPV-MFD across different time windows, and its applicability in real-world driving scenarios was validated. Compared to using EEG data alone, integrating multimodal data significantly improved anger recognition accuracy, with accuracy and F1 scores rising by 4.49% and 9.14%, respectively. Additionally, real-vehicle data closely matched simulated data, confirming the dataset’s effectiveness for real-world applications. This research is pivotal for advancing emotion-aware human–machine- interaction and intelligent transportation systems.

Estimate non-Gaussian road roughness from international roughness index

Gaussian model is commonly used to characterize road roughness, while field measured data is usually non-Gaussian. A new method based on the amplitude modulation and International Roughness Index is proposed in this paper to estimate non-Gaussian road roughness. A modified Gamma distribution is proposed to describe the International Roughness Index and to create the amplitude modulation signal. To ensure that the high kurtosis of the amplitude-modulated non-Gaussian profile is correctly transferred to the vehicle response, the method is further developed to fulfill the specifications for the fatigue damage spectrum by controlling the spectral content of the amplitude modulation signal. Herein, a Gaussian amplitude modulation signal with a cutoff frequency is firstly generated, and then converted to a modified Gamma distribution amplitude modulation signal based on the cumulative distribution function transform. Field data are used to verify the proposed method. The results show that a good match is obtained between the estimated and measured non-Gaussian road roughness. The estimated International Roughness Index and fatigue damage potential of vehicle response are well represented using the proposed method.

Analysis and optimization of self-propelled performance of wave-driven vehicle hydrofoil under high sea-level condition

The wave-driven vehicle is a surface vehicle powered by capturing wave energy, which is required to face harsh sea conditions when performing tasks in parts of the ocean. However, wave-driven vehicles are usually small in size, and their seakeeping and speed are generally poor in high sea conditions. Wave driven vehicles are usually equipped with rigid connected hydrofoils to capture wave energy, which can provide power for wave driven vehicles and enhance seakeeping. Aiming at the long-term survival and operation requirements of wave-driven vehicle under high sea conditions, this paper studies the effect of high sea conditions launching wing on the self- propelled performance of wave-driven vehicle. After installing rigid connected hydrofoils on wave-driven vehicles, the structural parameters of the hydrofoils are changed, and the kinematic and dynamic responses of wave-driven vehicles at 0–90 ° wave encounter Angle are numerically simulated based on CFD method. The effects of underwater wing depth, hydrofoil spacing and hydrofoil span length on the self- propelled performance of wave-driven vehicles are analyzed. Based on this, the structural parameters of rigidly connected hydrofoils are optimized, which improves the seakeeping and rapidity of wave-driven vehicles in high sea conditions.

Study on terrain response of the military mobile repair vehicle towing power generator trailer before and after improvement

Study on terrain response of the military mobile repair vehicle towing power generator trailer before and after improvement

Compensation control of commercial vehicle platoon considering communication delay and response lag

The real-time accurate control of vehicle is of great significance for the stability of platoon driving. To address the impact of random time-varying communication delays, communication packet loss, data disorder, and significant response lag of commercial vehicle on platoon control performance in non-ideal communication environment, a novel compensation control method for commercial vehicle platoon is proposed to overcome the abovementioned issues. The proposed method significantly improves the impact of communication issues and vehicle response lag on platoon control, ensuring platoon safety while reducing inter-vehicle spacing and spacing error, and maintaining platoon stability. In addition, this method combines the comprehensive compensation mechanism with the Distributed Model Predictive Controller (DMPC), avoiding the establishment of additional compensation control modules and effectively reducing the computational burden. The results under emergency braking condition show that, compared with the method without compensation, the proposed method significantly reduces inter-vehicle spacing and spacing error, reducing the average maximum spacing error from 29.25 m to 1.44 m, shortening the error convergence time of the platoon by 28 %, and ensuring platoon stability. Additionally, compared to the compensation method based on Kalman Filtering (KF) and Smith predictor, the proposed method can reduce the average maximum spacing error by 60 % while maintaining similar convergence time and platoon stability.

Effect of Damping on the Identification of Bridge Properties Using Vehicle Scanning Methods.

Vehicle scanning methods are gaining popularity because of their ability to identify modal properties of several bridges with only one instrumentation setup, and several methods have been proposed in the last decade. In the numerical models used to develop and validate such methods, bridge damping is often overlooked, and its impact on the efficacy of vehicle scanning methods remains unknown. The present article addresses this knowledge gap by systematically investigating the effects of bridge damping on the efficacy of vehicle scanning methods in identifying the modal properties of bridges. For this, acceleration responses obtained from a numerical model of a bridge and vehicle are used. Four different scenarios are considered where vehicle damping, presence of road roughness, and traffic on the bridge are varied. Bridge damping is modeled using mass-proportional, stiffness-proportional, and Rayleigh damping models. The impacts of ignoring bridge damping or considering one of these damping models on the modal frequencies and mode shapes identified using the vehicle response are investigated by comparing the results. The outcomes of the numerical analysis show that ignoring bridge damping in vehicle scanning applications can significantly increase the efficacy of these methods. They also show that the identifiability of the bridge frequencies and bridge mode shapes from the vehicle response decreases significantly when bridge damping is considered. Further, the damping model used impacts which bridge modes can be identified because different damping models provide different modal damping ratios for each mode. The results highlight the importance of correctly simulating damping behavior of bridges, which is often ignored, to be able to correctly evaluate the efficacy of vehicle scanning methods, and they provide an important stepping stone for future studies in this field.

Research on Indirect Influence-Line Identification Methods in the Dynamic Response of Vehicles Crossing Bridges

The bridge influence line can effectively reflect its overall structural stiffness, and it has been used in the studies of safety assessment, model updating, and the dynamic weighing of bridges. To accurately obtain the influence line of a bridge, an Empirical and Variational Mixed Modal Decomposition (E-VMD) method is used to remove the dynamic component from the vehicle-induced deflection response of a bridge, which requires the preset fundamental frequency of the structure to be used as the cutoff frequency for the intrinsic modal decomposition operation. However, the true fundamental frequency is often obtained from the picker, and the testing process requires the interruption of traffic to carry out the mode decomposition. To realize the rapid testing of the influence lines of bridges, a new method of indirectly identifying the operational modal frequency and deflection influence lines of bridge structures from the axle dynamic response is proposed as an example of cable-stayed bridge structures. Based on the energy method, an analytical solution of the first-order frequency of vertical bending is obtained for a short-tower cable-stayed bridge, which can be used as the initial base frequency to roughly measure the deflection influence line of the cable-stayed bridge. The residual difference between the deflection response and the roughly measured influence line under the excitation of the vehicle is operated by Fast Fourier Transform, from which the operational fundamental frequency identification of the bridge is realized. Using the operational fundamental frequency as the cutoff frequency and comparing the influence-line identification equations, the empirical variational mixed modal decomposition, and the Tikhonov regularization to establish a more accurate identification of the deflection influence line, the deflection influence line is finally identified. The accuracy and practicality of the proposed method are verified by real cable-stayed bridge engineering cases. The results show that the relative error between the recognized bridge fundamental frequency and the measured fundamental frequency is 0.32%, and the relative error of the recognized deflection influence line is 0.83%. The identification value of the deflection influence line has a certain precision.

Prediction of rail wear band evolution under excitation by periodic track irregularities and its influence on vehicle dynamic performance

Periodic track irregularities (PTIs) are an important and sensitive source of vehicle vibration and have attracted considerable attention from railway researchers. However, previous studies have mainly focused on the irregularities themselves, with limited investigation of how the rail wear band (RWB) generated by PTIs excitation affects vehicle responses. To assess this, a three-dimensional rail wear prediction model is developed in this study to simulate the evolution of the RWB under PTIs conditions and then the effect of RWB excitation on the vehicle dynamic performance is investigated. The results show that the spatial trace of the rail contact points generated by the PTIs also exhibits clear periodic characteristics. Accordingly, the distribution pattern of the RWB also reflects these periodic features. The excitation of the periodic rail wear band (PRWB) has a negative effect on the vehicle running stability, especially when the vehicle is traveling at speeds that induce coupling resonance, which exacerbates the consequences. As the wear level of the PRWB increases, there is also an escalation in the vibration of the carbody. Furthermore, the amplitude of the change in the wheelset balance position resulting from variations in rail profile is found to serve as an effective descriptor for characterizing the wear intensity of PRWB, and the vibration amplitude of the carbody exhibits a clear linear correlation with PRWB excitation. The research results are helpful for understanding the relationship between PRWB excitation and vehicle vibration behavior and serve as a theoretical basis for track maintenance and profile optimization.

Dynamic response of high-speed railway vehicle and welded turnout on large-span bridges based on rigid-flexible coupling system

Dynamic response of high-speed railway vehicle and welded turnout on large-span bridges based on rigid-flexible coupling system

Implementing Model Predictive Control (MPC) in Steer-by-Wire Systems for Future Automated Vehicles

Abstract The need for future green sustainable transportation represents a motivation for the researchers in order to create a better society. In this paper a proposed Steer-by-Wire system for future automated vehicles is analysed by implementing model predictive control (MPC) controller that would optimise the desired behaviour of the vehicle. By using MATLAB/Simulink, a bicycle vehicle model is simulated in standardised test manoeuvres according to standard ISO:7401. These results are used as referent data for the desired trajectory of the vehicle and as input data for the MPC controller. The idea of using these results for the controller is generated by the desire of creating a shared information intelligent systems. Shared information and control in the future intelligent systems would be crucial for creating the desired sustainable transport. By traveling, the vehicle would collect data of the desired measured variables and those data would be send to every other vehicle. Therefore, the next vehicle would possess the previous data and would try to improve the vehicle response and safety. Every optimised trajectory would be shared with the rest of the vehicles and this cycle would result in further optimization and in creation of a safer, more comfortable and more sustainable transport. The optimisation is done by the MPC controller which is responsible for defining the steering wheel angle and therefore creating the Steer-by-Wire system that is entirely defined by the MPC controller without driver commands. By comparing the trajectory of the automated vehicle and the desired trajectory gained from the previous simulations, the desired steering wheel angle is defined. This results in creation of a Steer-by-Wire system that improves the vehicle dynamics, safety and dynamic response of the vehicle. The mathematical modelling, results of the simulations and advantages of using the MPC controller for the Steer-by-Wire system in automated vehicle are presented.

Fluctuating wind load encountered by linearly moving vehicles: Investigation on fluctuating wind spectral characteristics

Wind load is one of the controlling loads for moving vehicles. Existing studies on the wind-induced response of vehicles mostly apply traditional wind spectra directly to the vehicles, overlooking their motion status. Fluctuating wind characteristics encountered by linearly moving vehicles were investigated in this paper. The traditional static frame of view was adjusted to a moving one, two principal flow directions were considered and the idea of positional equivalence introduced in Taylor frozen assumption was extended to the moving direction. A fluctuating wind power spectral density (PSD) was proposed. Then a novel test was designed to enable a measuring probe to travel across a wind field. The fluctuating wind characteristics encountered by moving points are quite different from those encountered by a static point. The integral scale decreases as the speed ratio of the measuring point to the wind increases, and an elliptic model was suggested. The proposed PSD aligns well with the test results, indicating that it can be used to accurately characterize the fluctuating wind load encountered by moving vehicles. Both the test results and the numerical analysis showed that as the speed ratio increases, the PSD and energy spectrum as a whole migrate to higher frequencies.

Indirect bridge modal identification enhanced by iterative vehicle response demodulation

This paper introduces a novel indirect bridge modal identification framework for extracting higher-mode bridge modal parameters. The method involves processing the vehicle response using a newly proposed iterative vehicle response demodulation technique, which separates multi-component vehicle responses while preserving the physical meaning of variables. This property is crucial for ensuring accurate and robust modal identification from vehicle responses. The feasibility of this approach is initially tested through the analysis of vehicle responses generated by numerical simulations. This analysis includes a detailed parametric study and discussions on various bridge conditions and types. Subsequently, the same methodology is applied to an experimental bridge, where structural modal parameters are extracted from the measured responses of a passing instrumented vehicle. The key contributions of this study include the development of the iterative vehicle response demodulation formulation and the ability of this method to identify modal parameters using fewer processing steps compared to conventional indirect identification methods. Results from both numerical simulations and experimental tests demonstrate that the iterative demodulation method can effectively extract higher-mode bridge modal parameters, validating the proposed framework’s effectiveness.

A Fast Multi-Objective Optimization Method for Control Parameters of High-Speed Maglev Vehicle-Bridge System

A fast multi-objective optimization method (FMOOM) is proposed by optimizing control parameters to improve the dynamic performance of a high-speed maglev vehicle–bridge system. This approach involves generating the corresponding dynamic response to the sampled control parameters using a theoretical model of a high-speed maglev vehicle–bridge system, followed by establishing an adaptive surrogate model for the relationship between the control parameters and the dynamic response extrema. In the second step, we combine the adaptive surrogate model and the multi-objective gradient-based optimizer (MOGBO) algorithm to obtain the Pareto solution set satisfying different performance indexes. Additionally, the control parameters are optimized using the fuzzy comprehensive evaluation method. In the numerical simulation, we investigate five maglev trains and ten-span simply supported beam bridges and the theoretical model is verified by comparing the calculations with the measured results. The optimization effect of FMOOM is analyzed under different working conditions. The results show that the adaptive surrogate model has good prediction accuracy based on the radial basis function. Furthermore, the Pareto solution distribution of different schemes using FMOOM is reasonable, and the optimization results are as expected. Compared with the reference scheme, the dynamic response of the maglev vehicle–bridge system is smaller after being subjected to FMOOM optimization, and the six performance indexes are dramatically improved.

Extended Modified Bridge System (EMBS) method for decoupling seismic vehicle‐bridge interaction

AbstractSeismic vehicle‐bridge interaction (SVBI) is the study of vehicle‐bridge interaction (VBI) in the presence of earthquake excitation. SVBI is an interdisciplinary problem of increasing importance to the design and safety of railways. This study deploys a consistent methodology to decouple the vehicle‐bridge system and solve independently the bridge and vehicle subsystems, bypassing multiple challenges the seismic response analysis of a coupled vehicle‐bridge system entails. The proposed approach builds upon the previously established Extended Modified Bridge System (EMBS) method for decoupling vehicle‐bridge systems (in the absence of earthquake excitation). Its premise is to first characterize and then assess the relative importance of the VBI effect on the bridge and vehicle responses and replicate it by modifying the pertinent uncoupled equations of motion (EOMs). The formulation deployed accommodates multi‐degree of freedom models for both the vehicle and bridge and can thus tackle complex systems. The analysis examines the ability of the proposed decoupling approach to predict the response of a realistic system vehicle‐bridge system under a suit of historical earthquake records. The decoupled results are in excellent agreement with the coupled solutions for all earthquake records and scenarios (i.e., earthquake excitation solely in the transverse direction of the bridge, as well as in both the transverse and vertical directions simultaneously).

Tapinarof Cream 1% Once Daily is Efficacious for the Treatment of Atopic Dermatitis in Patients with Skin of Color Down to 2 Years of Age in Two Pivotal Phase 3 Trials

Introduction: Patients with atopic dermatitis (AD) and skin of color can have heterogeneous presentations and treatment responses. In the pivotal phase 3 ADORING 1 and 2 trials, tapinarof cream 1% (VTAMA®, Dermavant Sciences, Inc.) once daily (QD) was significantly efficacious and well tolerated versus vehicle in adults and children down to 2 years of age with AD. Objective: Here, we report analyses of efficacy by skin color in ADORING 1 and 2, based on patients’ self-identified race and investigator-assessed Fitzpatrick skin type. Methods: In ADORING 1 and 2, patients with a Validated Investigator Global Assessment for Atopic DermatitisTM (vIGA-ADTM) score of ≥3, an Eczema Area and Severity Index score of ≥6, and body surface area involvement of 5–35% were randomized 2:1 to tapinarof cream or vehicle QD for 8 weeks. The primary efficacy endpoint was a vIGA-ADTM score of 0 (clear) or 1 (almost clear) and ≥2-grade improvement from baseline at Week 8. Secondary endpoints included proportion of patients with a ≥75% improvement in Eczema Area and Severity Index (EASI75). Results: Of the 407 and 406 randomized patients, 8.8–15.3% were Asian, 26.5–35.0% were Black, 44.8–56.8% were White, and 2.7–5.2% were Other groups (American Indian or Alaska Native, Native Hawaiian or Pacific Islander, or multiple races) across trials. Patients with Fitzpatrick skin types IV, V, and VI represented 23.8–25.1%, 20.6–22.2%, and 7.6–8.9%, respectively, of patients (>50% in both trials). Across trials, vIGA-ADTM responses (ranges) for tapinarof versus vehicle were: Asian, 39.5–48.9% vs 3.7–18.5%; Black, 43.1–47.0% vs 17.5–24.1%; White, 49.4–52.1% vs 12.2–14.5%; and Other, 26.0–44.8% vs 0.0–40.2%. EASI75 responses for tapinarof versus vehicle were: Asian, 47.6–76.6% vs 17.7–20.2%; Black, 48.9–55.3% vs 25.7–30.0%; White, 61.4–67.8% vs 19.6–20.7%; and Other, 38.3–63.3% vs 0.0–40.6%. Similarly, high and consistent vIGA-ADTM and EASI75 responses were reported with tapinarof versus vehicle in patients with Fitzpatrick skin types I–III and IV–VI. Conclusion: Tapinarof cream 1% QD was consistently efficacious among all racial groups and Fitzpatrick skin types in adults and children down to 2 years of age with AD, including patients with skin of color, who were highly represented in these trials.