Efficient road roughness characterization using Gaussian process regression and 2D imaging

This research demonstrates a simple and economical way to assess road pavement conditions through any common smartphone. The phone was safely mounted inside a vehicle and sensor readings had been recorded during vehicle travel along different road segments. The vertical movement had been computed from the phone’s accelerometer and its location had been monitored through GPS. These two measurements made it feasible to predict pavement conditions without access to expensive instruments. As a data observation tool, a python script calculated Roughness Index (RI) based on vertical acceleration's root mean square (RMS) in short time windows. The output RI measures were categorized into three groups: smooth, moderate, and rough. The data were represented in green, yellow, or red dots onto a map and represented road quality well along the corridor. To better measure pavement condition, the campus road network was disaggregated into distinct sections based on natural corridor conditions such as intersections, curves, and direction changes. Breaking it down in this way enabled each section to be studied independently. The data showed strong variation between sections. Road segments near entrances and access points had repeatedly high Roughness Index (RI) values, indicating surface deterioration, patching, and unevenness. Straight internal segments and long corridors, on the other hand, had low RI values, which meant relatively smooth and well-maintenance pavement. This section-by-section analysis pinpointed areas in the campus network in need of maintenance and highlighted conditions based on pavement usage and location.

In bridge widening projects under uninterrupted traffic conditions, vehicular vibration easily leads to damage in the interfacial transition zone (ITZ) and microstructural degradation of early-age concrete in wet joints. Taking a typical hollow slab-low T-beam widening structure as the object, this study introduces basalt micro fiber (BMF)-based ultra-high-performance concrete (UHPC) as the wet joint material and establishes a refined vehicle-bridge coupled dynamic model considering the time-varying stiffness of the joint material and road roughness excitation. The research indicates that although UHPC possesses excellent ultimate mechanical properties, its early-age setting process is extremely sensitive to vehicle-induced vibration. Numerical analysis reveals that while traditional temporary steel fixtures can effectively control the vertical relative displacement between the new and old girders within the critical value of 5.5 mm, the peak particle velocity (PPV) induced by heavy vehicles (buses and trucks) during the early pouring stage (<12 h) significantly exceeds the safety threshold of 3 mm/s, posing a severe threat to the directional distribution of steel fibers and interfacial bond strength. Therefore, this paper designs a single tuned mass damper (TMD) optimized based on Den Hartog's fixed-point theory. Simulation results confirm that with the TMD configured, the vibration responses induced by buses across the entire speed range (≤120 km/h) are reduced below the safety limit; the vibration velocity induced by heavy trucks is also effectively controlled when combined with an 80 km/h speed limit. The collaborative strategy of "passive TMD vibration reduction + active traffic speed limit" proposed in this paper provides a theoretical basis for guaranteeing the early-age micro-forming quality of UHPC wet joints and overall traffic efficiency.

Liquid fuel intrusion causes carbon canister deterioration and excess evaporative VOC emissions from in-use vehicles.

Electric vehicles (EVs) rely on efficient energy management to extend driving range, and regenerative shock absorbers using helical gears can capture kinetic energy from suspension motion. Key challenges include mechanical complexity, vibration damping and maximizing energy conversion without compromising ride comfort. This paper proposes a combined approach for an efficient regenerative absorber related to a twin helical-gear for range-extended EVs. The proposed absorber converts suspension vibrations from road roughness into electricity using a suspension input module, helical gears, one-way clutches and a generator module. The proposed approach combines the techniques of the Electric Eel Foraging Optimization (EEFO) Method and Generative Pre-Trained Physics-Informed Neural Networks (GPT-PINNs). Hence, it is called the EEFO-GPT-PINNs technique. The EEFO is utilized to optimize the motor driving system’s power consumption, while the GPT-PINNs method is utilized to forecast the EV Range based on current driving conditions. To increase the efficiency of energy transfer, the adaptive EEFO-GPT-PINNs optimize the one-way clutches and helical gears. The objective of this work is to develop the efficiency and power of the system. The proposed strategy is implemented and analyzed using MATLAB and its performance is compared with existing methods, including Proximal Policy Optimization (PPO), Multi-Objective Optimization (MOO) and Genetic Algorithm (GA) techniques. The proposed approach achieves significant improvements, yielding 21% higher energy harvesting at a damping ratio of 0.1[Formula: see text]m/s, 46% mechanical efficiency at 6[Formula: see text]Hz and 94% recoverable power effectiveness at 3[Formula: see text]Hz, thereby outperforming existing methods and validating its potential for enhanced power and efficiency optimization.

Electrical-vehicle (EV) dynamics are significantly influenced by battery mass and the location. Therefore, the combined investigation of a 7-DoF model, random road excitation, and eccentric battery effects is necessary for more accurate characterization of vehicle dynamics. In this study, a 7-DoF EV full-vehicle model considering battery eccentricity is derived to examine the coupled heave–roll–pitch vibrations under random road excitation. A spectral method is employed for the solution. EV response spectra are obtained using the power-spectral-density (PSD) of road roughness and the EV frequency-response-functions (FRFs). RMS values of response are investigated for different battery eccentricities and vehicle speeds [1-160km/h]. The results show that the normalized RMS varies up to ~30% total with EV speed and battery eccentricity. The highest RMS occur at vehicle speeds where the natural frequency is excited, while the lowest vibration RMS is obtained at low battery eccentricity on the x-axis. The developed EV model and solution method have been presented as a successful tool for investigating random vibrations and vehicle dynamics.

This study develops and evaluates a semi-active seat suspension system for tractors using magnetorheological damper (MRD) and fuzzy logic-based control strategies. A five-degree-of-freedom (5-DOF) half-car tractor model is constructed, integrating human-seat dynamics, cab vibration, chassis motion, tire flexibility, and MRD nonlinear hysteresis represented by the Bouc-Wen model. To address uncertainties caused by road roughness, vehicle speed, and human-seat mass variation, a Type-1 fuzzy logic controller (T1FLC) and an interval Type-2 fuzzy logic controller (IT2FLC) are designed. The stability of both controllers is analyzed through phase-plane trajectories. Simulations are performed under random C-F class road excitations, bump inputs at different speeds, and human-seat massranging from 50 to 150kg. Results show that semi-active control substantially improves ride comfort and reduces suspension dynamic deflection compared with passive suspension. Under random road excitations, the IT2FLC reduces the root mean square (RMS) vertical acceleration by approximately 60% and dynamic deflection by over 50%, effectively mitigating suspension bottoming-out. Under bump excitations, improvements reach 61.27% and 55.94%, respectively. The IT2FLC consistently demonstrates stronger robustness than the T1FLC, especially under highly uncertain or severe road conditions. These findings provide theoretical and engineering support for intelligent vibration-control strategies in agricultural tractor seat suspension systems.

Vehicle traction control is the cornerstone for enhancing driving performance, ensuring road safety, and enabling intelligent driving. For off-road vehicles, variable road roughness imposes varying degrees of vertical dynamic loads on the wheels and affects changes in vehicle traction. Through simulation experiments, this paper found that the slip rate control performance of vehicle traction control systems traditionally designed for static loads exhibited significant variations when dynamic loads were introduced. To address this issue, vertical dynamic wheel load data under different vehicle speeds and road classifications were subjected to clustering, leading to the determination of three as the optimal number of clusters. The integration of the immune whale algorithm for the design of corresponding traction control systems for different dynamic load categories, and their subsequent comparison with traditional controllers, revealed a significant enhancement in slip rate control performance. Under Class I dynamic loads, the control performance on all road surfaces achieved the best results, reaching a maximum of 63.4%. Consideration of the dynamic variations in dynamic loads caused by changes in vehicle speed led to the design of a global controller capable of adapting to different dynamic load conditions, based on the aforementioned classification controller. The final simulation verification confirmed the maintained excellent control performance of this adaptive controller under various operating conditions.

As critical damping components of vehicles, suspension systems play an essential role in maintaining vehicle stability and enhancing ride comfort. This paper studies the dynamic behaviors and reliability of the suspension system. First, based on Newton’s second law, a single-degree-of-freedom suspension system model is established through simulating the rough road fluctuations as a combination of typical cosinusoidal road excitation and Gaussian white noise. Then, considering the linear damping and nonlinear damping, respectively, the dynamic evolution and first-passage failure of the system under primary resonance and 1/3 subharmonic resonance conditions are examined by the path integral method. The influence mechanisms of dampings, road surface excitation amplitude and noise intensity on the dynamics of suspension systems are explored. The results demonstrate that reduced damping, increased road excitation amplitude and higher noise intensity collectively impair system stability. Crucially, the system’s response to these parameters is governed by the resonance type. Within a certain range, under primary resonance, road amplitude predominantly affects displacement, whereas under 1/3 subharmonic resonance, it significantly alters both displacement and velocity distributions, even inducing stochastic P-bifurcation. These findings provide valuable insights into the design and optimization of vehicle suspension systems for improving performance and reliability.

Indirect Bridge Health Monitoring (BHM) using indirect measurements of the response from passing vehicles has recently gained significant attention from researchers within the Structural Health Monitoring (SHM) domain. This approach requires only one or a few sensors installed on the vehicle, making it more cost-effective, efficient, and easier to implement than traditional methods, which demand numerous sensors on bridges. Recent advancements in both algorithms and hardware have further accelerated progress in this field. This paper aims to provide a comprehensive, one-stop review of indirect BHM using measured vehicle response since 2004. It systematically analyzes the connections and integrations within existing literature, incorporating rapidly emerging state-of-the-art studies. The review initiates with a bibliometric analysis, covering annual publication trends, keyword cooccurrence, and authorship networks, followed by a discussion on the fundamental theories of vehicle–bridge interaction. Subsequently, it summarizes the vehicle, bridge, and road roughness models used in indirect BHM. Furthermore, it explores current techniques and challenges in identifying bridge modal parameters, such as bridge frequencies, mode shapes, and damping ratios, as well as in indirect bridge damage detection using signal processing, modal-based, and data-driven methods. Additionally, this review includes affiliated studies that, while not directly related, contribute to the advancement of indirect BHM. Finally, recent developments in 2025, future investigation directions, and key conclusions are provided. It is intended to serve as a fundamental resource for researchers seeking to advance their studies in the field of indirect BHM.

The road between Muara Pinang and Muara Payang Villages is severely damaged. Research is needed to assess the level of road service in terms of road surface roughness. The purpose of this study is to analyze the level of road roughness in Muara Pinang and Muara Payang Villages, Empat Lawang Regency, and to provide recommendations for addressing road damage. The method used is the International Roughness Index (IRI) based on the Roadbump Pro smartphone application. The survey was conducted over a 10.5 km stretch, divided into 100-meter segments, using an SUV at a speed of 40-50 km/h. Measurement data showed that most of the road was in the severely damaged category with an average IRI value of 13-14 m/km, indicating poor road surface conditions. These results confirm that the condition of the road pavement at the study site requires serious attention for immediate maintenance and repair. The use of smartphone technology has proven effective and efficient in conducting road condition surveys. Based on the results of the data analysis, the road handling recommendations that must be carried out are improvements or reconstruction.

Accurate identification of road roughness is pivotal for optimizing vehicle suspension control and enhancing passenger comfort. However, existing data-driven methods often struggle to balance classification accuracy with the strict computational constraints of real-time onboard monitoring. To address this challenge, this paper proposes a lightweight and robust road roughness classification framework utilizing a single sprung mass accelerometer. First, to overcome the scarcity of labeled real-world data and the limitations of linear models, a high-fidelity co-simulation platform combining CarSim and Simulink is established. This platform generates physically consistent vibration datasets covering ISO A–F roughness levels, effectively capturing nonlinear suspension dynamics. Second, we introduce DB-MLP, a novel Dual-Branch Multi-Layer Perceptron architecture. In contrast to computationally intensive Transformer or RNN-based models, DB-MLP employs a dual-branch strategy with multi-resolution temporal projection to efficiently capture multi-scale dependencies, and integrates dual-domain (time and position-wise) feature transformation blocks for robust feature extraction. Experimental results demonstrate that DB-MLP achieves a superior accuracy of 98.5% with only 0.58 million parameters. Compared to leading baselines such as TimeMixer and InceptionTime, our model reduces inference latency by approximately 20 times (0.007 ms/sample) while maintaining competitive performance on the specific road classification task. This study provides a cost-effective, high-precision solution suitable for real-time deployment on embedded vehicle systems.

To enhance ride comfort and safety in autonomous vehicles, a suspension control strategy based on multi-sensor information fusion is proposed. A multi-sensor processing system integrates light detection and ranging (LiDAR) and cameras, utilizing point cloud clustering, segmentation techniques, and the You Only Look Once version 7 (YOLOv7)-tiny algorithm to analyze the geometric characteristics and spatial positions of road obstacles. The chain code method is applied to calculate obstacle areas. A vibration model is established to describe the relationship between road roughness and vehicle vibrations, followed by the development of an optimal damping ratio model for intelligent suspension control. A fuzzy neural network algorithm is employed to pre-adjust suspension damping based on obstacle characteristics and vehicle speed, enabling proactive adaptation to road irregularities. If an obstacle exceeds a predefined threshold, the system automatically reduces vehicle speed to further enhance safety and ride comfort. Experimental validation demonstrated that for potholes of 0.5 m 2 with depths of 0.06, 0.08, and 0.10 m, vertical vibration acceleration was reduced by 11.4%, 12.8%, and 15.9%, respectively. These results confirm the effectiveness of the proposed approach in improving ride quality and vehicle stability.

Adverse weather conditions, low-light environments, and bumpy road surfaces pose significant challenges to SLAM in robotic navigation and autonomous driving. Existing datasets in this field predominantly rely on single sensors or combinations of LiDAR, cameras, and IMUs. However, 4D millimeter-wave radar demonstrates robustness in adverse weather, infrared cameras excel in capturing details under low-light conditions, and depth images provide richer spatial information. Multi-sensor fusion methods also show potential for better adaptation to bumpy roads. Despite some SLAM studies incorporating these sensors and conditions, there remains a lack of comprehensive datasets addressing low-light environments and bumpy road conditions, or featuring a sufficiently diverse range of sensor data. In this study, we introduce a multi-sensor dataset covering challenging scenarios such as snowy weather, rainy weather, nighttime conditions, speed bumps, and rough terrains. The dataset includes rarely utilized sensors for extreme conditions, such as 4D millimeter-wave radar, infrared cameras, and depth cameras, alongside 3D LiDAR, RGB cameras, GPS, and IMU. It supports both autonomous driving and ground robot applications and provides reliable GPS/INS ground truth data, covering structured and semi-structured terrains. We evaluated various SLAM algorithms using this dataset, including RGB images, infrared images, depth images, LiDAR, and 4D millimeter-wave radar. The dataset spans a total of 18.5 km, 69 minutes, and approximately 660 GB, offering a valuable resource for advancing SLAM research under complex and extreme conditions. Our dataset is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://gongweisheng.github.io/DIDLM.github.io/</uri>

Energy-saving control framework for lunar rover driving on low-gravity and rough roads

We present a cost-effective approach to evaluating road surface quality and roughness based on low-cost smartphones by leveraging accelerometer and gyroscope data sampled at 10 Hz. We extracted features from the vertical accelerations and rolling motions, in cloud to standard deviation and interquartile range, and trained a support vector machine (SVM) classifier to identify Good and Poor roughness levels based on IRI thresholds. We selected SVM specifically because it affords consistent performance with small datasets, reliably handles low-dimensional statistical features, and provides a stronger generalization than more complex machine-learning approaches. We provide experimental results from four vehicles on a thrice 50-m segment based dataset, demonstrating that the proposed method can discriminate (i.e., classify) roadway roughness and quality levels 80–100% of the time based on smartphone IMU data, which implies that even low-frequency smartphone IMU signals can provide useful roughness screening for planning and maintenance.

Assessment of road surface roughness before and after surface renewal on road II/584 in the Huty area

Tomato production is an important source of income and food for many households in Beledweyne District, Hiran Region, Somalia. However, postharvest losses remain a major challenge, particularly during transportation from production areas to the main markets, so this study examined the impact of road condition on postharvest losses in tomato production in Beledweyne District, Hiiraan Region, Somalia. A descriptive cross-sectional survey design with a quantitative approach was used. Data were collected from 92 tomato farmers who supply tomatoes to the main market in Beledweyne District. The sample size was determined by using Slovin’s formula with a 5% margin of error. Primary data was used through A structured questionnaire was used to collect data at the local main market in Beledweyne, where farmers bring their products. Using descriptive statistics such as frequencies, percentages, and figures, with Stata version 17. The findings indicate that postharvest tomato losses in the study area are a considerable majority of farmers reported losses ranging between 21% and 30%, and also a significant proportion experienced losses exceeding 30% of total production. The results indicated that poor road conditions are the main cause of these losses. The farmers indicate that rough and unpaved roads, long transportation distances, and frequent delays and also long travel times all negatively affect tomato quality, resulting in physical damage such as bruising, cracking, rotting, and softening. In addition to that, the study results show that postharvest losses have a serious negative impact on farmers' income, reducing profitability. All respondents agreed that also improving road condition would significantly reduce postharvest losses.

This research is aimed at evaluating the effect of influencing parameters on the dynamic load allowance (DLA) for steel composite bridges subject to autonomous truck platooning (ATP). A comprehensive literature review is presented on platooning configurations and their effect on bridges, the selection of candidate trucks to constitute ATP, and DLA analyses. Next, the modeling of trucks, the road surface roughness generation in MATLAB, and its realization in Abaqus® as 3-D finite element modeling is presented in detail. A parametric study of the DLA is performed for a single span steel girder bridge for a single truck and platoons with two or three trucks, with speed ranging from 60 to 100 km/h, inter-truck spacings between 6 and 10 m, and three road surface roughness profiles (ISO 8608 profile A, ISO 8608 profile B, and ISO 8608 profile C). The results indicate that the resonance of the bridge can be excited by truck platoons for specific speed and inter-truck spacings, which can increase the dynamic load allowance relative to that of a single truck. Combinations of platoon speed and spacing that result in resonance conditions and high DLA vary as a function of surface roughness. When resonance conditions are not encountered, and inter-truck spacings are small, such that all platoon trucks are simultaneously on the span, the DLA is smaller compared with a single truck for smooth surface profiles. Large inter-truck spacings for two-truck and three-truck platoons result in high DLA but lower static loads, which can result in dynamic effects that are not within current Canadian Standards Association (CSA) guidelines, especially for large road surface roughness. ATP on a smooth profile result in only marginal increases of the DLA compared with a single truck and are within current CSA S6 guidelines.

ABSTRACT Drive-by vehicle-borne mobile sensing with third-party vehicles has the advantages of high precision, low cost, appropriate coverage, and high timeliness, when compared to satellite-based, Unmanned Aerial Vehicle (UAV)-borne, or ground-based station monitoring. However, the non-prescriptive (or even unpredictable) behaviors of third-party vehicles can lead to imbalanced sampling. To obtain a good mobile sensing scheme with a maximum spatial coverage and a minimal spatial sampling heterogeneity, this study selected the best hybrid bus-taxi fleet to install sensors by proposing cooperative mobile sensing optimization models. As the traveling behavior patterns of taxis and buses mined from a huge amount of historical data were fully given consideration into optimization models, the sensors installed on the proposed mobile sensing taxi-bus fleet could automatically collect data, achieving the largest urban spatial range without operational intervention. Experimental results demonstrated the benefits of our solution in terms of global sensing ratios, sensing heterogeneity, cost savings, and geographical sampling equality. The proposed models can be used to monitor a variety of urban environmental objects, including air pollutants, noise, road roughness, and urban 3D scenes.