Search Results (95)

Hydrogen-powered heavy-duty vehicles (HHDVs) operating under multiple driving conditions are subjected to coupled longitudinal, vertical, and lateral dynamic excitations, which significantly affect their dynamic performance and rollover stability. To investigate these characteristics, a coupled vehicle dynamic model consisting of a vertical dynamic model and a yaw–roll dynamic model was established, and numerical simulations were conducted under multiple operating conditions. The effects of operating condition, road roughness, initial braking speed, and braking deceleration on ride comfort and dynamic tire load were systematically analyzed. Furthermore, rollover stability was evaluated under J-turn, Fishhook, and Double Lane Change (DLC) maneuvers using yaw rate, slip angle, lateral acceleration, and lateral load transfer ratio (LTR) as evaluation indices. The simulation results show that braking causes the greatest deterioration in ride comfort, with the peak human–seat vertical acceleration increasing by 33.10% compared with the constant-speed condition, while acceleration results in a 27.55% increase. Road roughness substantially affects both ride comfort and dynamic tire load. Under braking, the peak front and rear tire dynamic loads on a Class D road are approximately 3.1 and 2.9 times those on a Class B road, respectively. Increasing the initial braking speed intensifies dynamic responses, whereas increasing the braking deceleration effectively suppresses tire dynamic load fluctuations. Among the three steering maneuvers, the Fishhook maneuver exhibits the highest rollover propensity, with the maximum absolute LTR approaching 0.8. These simulation-based findings provide insights into chassis parameter optimization, vehicle dynamic performance evaluation, and rollover prevention of HHDVs under multiple operating conditions. The present study is limited by the lack of experimental validation of the developed dynamic models, and experimental or hardware-in-the-loop validation will be considered in future work. Full article

This paper extends the classical Merton structural credit risk model by incorporating dynamic refinancing risk into the measurement of bank default risk. The study addresses a key limitation of traditional structural models, which treat default as a function of asset values relative to liabilities but abstract from debt maturity structure and rollover conditions. The proposed framework integrates an issuance-based refinancing ladder, a market-consistent funding curve, and firm-level balance sheet data within a liquidity-adjusted structural model featuring an endogenous default barrier and a refinancing-adjusted distance-to-default measure. Using bank-level data (1564 daily observations, 2020–2026), the results show that, although the institution remains solvent under conventional structural measures, refinancing exposure materially compresses the effective solvency buffer. Short-term refinancing exposure averages R8.1 billion and reaches approximately R19.6 billion during stress periods; the liquidity-adjusted distance to default averages 1.47 (range 0.46–2.15) and the implied probability of default averages 9.5% (range 1.6–32.2%). A Newey–West heteroskedasticity- and autocorrelation-consistent regression that controls for asset volatility and the underlying solvency ratio confirms that the refinancing ratio has a negative and highly statistically significant partial effect on distance to default (coefficient −1.95, t = −7.41, p < 0.001, N = 1564), isolating the liquidity-adjustment channel from concurrent changes in volatility and balance sheet solvency. Stress testing further reveals nonlinear amplification of default risk when funding and refinancing shocks interact. The findings indicate that bank default risk is driven not only by leverage, but also by the interaction between asset values, liability structure, and funding conditions, with implications for credit risk modelling, stress testing, and prudential risk management. Full article

Roll motion induced by steering maneuvers and vertical vibration excited by road unevenness are strongly coupled in active suspension vehicles. Neglecting this coupling may deteriorate the performance of coordinated chassis control and compromise both roll stability and ride comfort. To improve roll stability and ride comfort under combined steering and road excitation conditions, this paper develops a roll–vertical coupled control framework. First, a nine-degree-of-freedom roll–vertical coupled vehicle model is established by integrating lateral–yaw dynamics, sprung mass heave motion, roll and pitch motion, and four unsprung mass vertical dynamics. Second, an adaptive square root cubature Kalman filter (ASRCKF) is designed to estimate key roll states, including the roll angle and roll rate. The square root structure improves numerical stability, while the Sage–Husa adaptive estimator updates the measurement noise covariance online using the innovation sequence. Third, a load transfer ratio-based rollover risk assessment method and a model predictive control (MPC)-based active suspension controller are introduced to realize coordinated roll–vertical control. Finally, the proposed framework is validated using a MATLAB/Simulink–CarSim co-simulation platform. The results demonstrate that the proposed method effectively improves vehicle roll stability and vertical ride performance under complex driving conditions. Full article

This paper presents the research, development, and functional validation of an original rear suspension system designed for hybrid reverse trike vehicles (two guided wheels on the front axle and a twin-tire-driven assembly at the rear). Conventional configurations featuring a single rear wheel exhibit severe limitations regarding lateral stability under critical dynamic regimes and induce roll-induced torsional loading in flexible chain drives. The proposed solution utilizes a twin-tire rear assembly integrated into an articulated suspension mechanism with two degrees of freedom (2 DoF), which reconfigures the geometric stability polygon from a triangle into an isosceles trapezoid. A mathematical model based on tire dynamics and tire slip phenomena demonstrates that introducing a controlled roll stiffness on the rear axle stabilizes the slip angles, ensuring a neutral and predictable steering behavior. Structural validation via finite element analysis (FEA) performed in SOLIDWORKS Simulation on the entire assembly under a conservative combined load scenario (2400 N vertical force shared by the two wheel bearings, 2400 N lateral force, and 1200 N tractive force) indicated a minimum factor of safety of 1.26 on S275N structural steel, confirmed by an eleven-run mesh independence study. Finally, the system’s functionality was experimentally confirmed through the manufacturing and road testing of a full-scale (1:1) demonstrator vehicle powered by an 1129 cc Boxer engine, highlighting a measurable increase in rollover resistance and trouble-free operation of the two-stage chain drive throughout the test program. A numerical evaluation shows that for rear-biased vehicles of the category the proposed axle raises the rollover-related lateral acceleration threshold by up to 54% and replaces the strongly oversteering balance of the single-wheel layout with a near-neutral, tunable one. Full article

The development of electric bus superstructures requires an integrated engineering approach combining structural design, numerical simulation, experimental validation and durability assessment. This need is particularly important for electric buses, where heavy roof-mounted battery systems and auxiliary components influence structural load paths, fatigue durability and rollover crashworthiness. This paper presents a measurement-supported workflow for the development of a virtual electric bus superstructure model, including finite element analysis, multibody dynamics simulations, operational load assessment, fatigue-oriented evaluation and rollover crashworthiness analysis. The finite element model is used to assess static load cases, modal properties and structural response under selected design conditions. A multibody vehicle model with nonlinear suspension characteristics is applied to simulate representative operating scenarios and to support the definition of dynamic load cases. Operational measurement data from previous work are used as a basis for realistic load characterization. Experimental torsional stiffness and modal tests are used to validate the numerical model. The main contribution of the study is the integration of these numerical, experimental and operational-data-based activities into a consistent early-stage verification process. The proposed workflow supports early identification of critical structural regions, assessment of design modifications and reduction in prototype-based design iterations. Full article

Heavy-duty truck rollovers on curved road sections remain a critical safety concern because lateral excitation generates large roll moments and rapid vertical load transfer across wheels, potentially leading to wheel lift-off. This study proposes a reproducible scenario-sweep protocol in dSPACE Automotive Simulation Models (ASMs) to generate rollover datasets for a three-axle truck and to develop a rollover classifier using physically interpretable wheel/axle vertical load features. Scenarios were parameterized by the vehicle speed (20–30 km/h), curve radius (10–25 m), and bank angle $\beta$ (0 to −7.5°), with the sign convention defined in this paper) and gross vehicle mass (7000–23,000 kg), where the lateral excitation is governed primarily by $a_y\approx {\displaystyle \frac{v^2}{R}}$ together with the banking contribution. A total of 180 scenarios were used for training, while an independent interpolated validation set of 90 scenarios was constructed using intermediate parameter levels. In offline validation, the proposed model achieved an accuracy of 95.56% (86/90) with no missed-rollover case (FN = 0; errors otherwise consisted of false positives). To assess real-time deployability, the trained pipeline was implemented and evaluated in a hardware-in-the-loop simulation (HILS) configuration over 32 scenarios, achieving an accuracy of 90.63% (29/32) while maintaining FN = 1. By explicitly linking driving-condition inputs to load transfer observables $F_{z,i}\left(t\right)$ and validating the resulting classifier across offline and real-time environments, the proposed workflow provides a reproducible vehicle dynamics grounded pathway for scenario-based rollover risk classification in simulation-to-HILS studies. Full article

With advances in electric drive technology, electric tracked vehicles (ETVs) have emerged as a promising solution for high-mobility ground vehicles. However, under high-speed steering conditions, the equivalent motor load inertia varies significantly, introducing strong nonlinear and time-varying characteristics into the ETV that may induce lateral instability and even rollover. To address this issue, a novel augmented deep Koopman operator-based model predictive control (ADK-MPC) method is proposed. First, a high-order sliding-mode (HOSM) observer is designed to estimate the lumped load disturbances associated with the time-varying equivalent motor load inertia. Then, the estimated disturbances are introduced as an augmented state into the DK operator to construct a data-driven augmented model. The proposed model transforms the nonlinear dynamics into a lifted linear time-invariant representation in the augmented-state space while capturing the dominant nonlinear characteristics. Based on the ADK model, an ADK-MPC controller is developed to convert the nonlinear optimization problem into a quadratic programming problem, thereby improving steering stability and reducing computational complexity. Simulation results under steering conditions indicate that the proposed method achieves better yaw rate tracking and lower computational cost than nonlinear MPC. The yaw rate tracking error is reduced by 45.5%, while the average solving time is shortened by 11.7%. Full article

This study proposes an attitude stabilization method based on an improved adaptive Kalman filter (AKF). The aim is to address attitude fluctuations and rollover risks in rail-based agricultural transport platforms on hilly terrain caused by slope changes, load shifts and vibrations. A dynamic model integrating the load distribution and center-of-mass migration was established, and an adaptive noise covariance mechanism was used to precisely estimate the roll and pitch angles in real time. A dual-channel proportional–integral–derivative controller was designed for automatic leveling, and a rollover risk index (RRI) was adopted for safety evaluation. Simulations revealed the ability of the improved AKF to decrease the roll estimation (RMSE) from 1.2684° to 0.8670° and the stabilization time from 0.6250 to 0.3830 s for the roll and from 0.6930 to 0.4110 s for the pitch. Under 10–30° slope disturbances, the average RRI decreased from 0.1861 to 0.1506. Field tests further demonstrated decreases in the peak roll and pitch angles from 4.8° and 4.1° to 3.1° and 2.7°, respectively, and a decrease in the average RRI from 0.203 to 0.169. The improvements in estimation accuracy, leveling performance, and operational safety under complex disturbances indicate the strong engineering potential of the proposed method. Full article

For autonomous vehicles, high-speed cornering can easily lead to degraded handling stability and increased risks of sideslip or even rollover. Therefore, vehicle phase-plane stability-region analysis has become an important topic in active safety-control research. However, most existing studies still construct phase-plane stability regions mainly based on simplified vehicle models, without sufficiently considering the influence of vertical load transfer during cornering on tire lateral forces and stability boundaries. To address this issue, this paper proposes a hierarchical control strategy based on phase-plane analysis for active inward tilt vehicles. This method adopts a three-degree-of-freedom vehicle dynamics model and a tire model. By carefully comparing the phase-plane stability regions of active inward tilt and passive roll vehicles and by further analyzing the state-trajectory convergence characteristics of active inward tilt vehicles under different longitudinal speeds, front wheel steering angles, and road adhesion coefficients, the effects of active inward tilt on stability-region expansion and vehicle-state convergence are revealed. Subsequently, a hierarchical control strategy is proposed as an integrated solution to improve vehicle handling stability. The upper-level controller dynamically adjusts the reference values and objective weights according to whether the vehicle state is located in the stable, critical, or dangerous region. The lower-level NMPC controller optimizes the front wheel steering angle and active suspension forces to achieve coordinated trajectory tracking and stability control. Double lane-change simulation results show that active inward tilt can improve the left–right vertical load distribution and expand the lateral stability region. Compared with passive roll and conventional active inward tilt control, the proposed strategy reduces the phase-plane state convergence area by 68% and 75%, respectively, thereby improving vehicle handling stability and active safety under extreme conditions. Full article

Autonomous tractors have been shown to possess the capability to ensure a high degree of operational precision during seeding activities on flat terrain. However, in topographically challenging environments characterised by significant elevations and pronounced variations in slope, factors such as road gradients have been shown to compromise the precision of satellite-based positioning systems. This, in turn, can lead to alterations in vehicle posture and the generation of disparate longitudinal driving forces between the left and right tyres. It is important to note that this deviation from the predefined path has the potential to result in rollover accidents. Evidence has been presented that indicates a correlation between road gradient and vehicle roll motion. The proposed methodology is an algorithmic approach to the estimation of lateral slope, integrating inertial measurement unit (IMU) sensors and ground-based ultrasonic radars. This algorithmic approach is proposed as a means to achieve more accurate estimations of lateral slope. The initial development of the vehicle dynamics model was based on slope operation requirements, and the model was endowed with eight degrees of freedom. The utilisation of an unscented Kalman filter (UKF) facilitates the integration of inertial measurement unit (IMU) and ground-based ultrasonic radar measurements, thereby enabling real-time estimation of key motion states, such as lateral slope. The validity of the proposed algorithm was established through a combination of hardware-in-the-loop testing and field trials involving real tractors. The findings indicate that the implementation of this algorithm leads to a substantial enhancement in the trajectory tracking accuracy of tractors during slope operations. This enhancement is characterised by a substantial reduction in lateral deviation and an effective augmentation in the operational pass rate. In the course of empirical trials conducted in a mountainous environment, the lateral positioning deviation during straight-line driving was diminished from 10 cm to within 5 cm. Concurrently, the precision of lateral slope estimation was enhanced to 0.04 degrees. Full article

This paper investigates the stability of telehandlers operating on inclined terrain through a sequential methodological approach. In a first stage, stability is assessed using quasi-static methods based on force and moment equilibrium, including the load transfer matrix and the stability pyramid. These approaches account for gravitational and inertial effects through equivalent external forces and moments applied at the global centre of gravity, enabling efficient evaluation of load redistribution and proximity to rollover thresholds under generalized quasi-static conditions. The application of these methods highlights intrinsic limitations when addressing structurally complex machines such as telehandlers equipped with a pivoting rear axle and evolving mass distribution due to boom motion. In particular, quasi-static approaches require a priori assumptions regarding the effective rollover axis and cannot fully capture the coupled geometric and contact interactions between rear axle articulation limits, centre of gravity migration, tyre–ground interface behaviour, and support polygon evolution. To overcome these limitations, a nonlinear dynamic multibody model based on the three-dimensional Bond Graph (3D Bond Graph) methodology is introduced. The model is implemented within a virtual tilt–rotation test platform and validated against experimental results obtained from ISO 22915-14 stability tests. The comparison confirms compliance with normative requirements and demonstrates that the dynamic framework captures condition-dependent rollover mechanisms and transitions between distinct virtual rollover axes that cannot be fully explained by quasi-static formulations. Unlike most previous studies, which focus on fixed configurations or forward-driving scenarios, the proposed framework analyzes stability evolution under spatial inclination while accounting for structural articulation constraints. The explicit identification of rollover axis transitions induced by rear axle articulation provides a deeper mechanistic interpretation of telehandler stability and supports the use of high-fidelity dynamic simulation as a complementary tool for test interpretation, experimental planning, and the development of predictive stability and operator assistance systems. Full article

Objectives: To analyze speech perception outcomes of a cohort of experienced adult cochlear implant (CI) users to explore whether there is a correlation with electrical dynamic range (EDR) parameters, and to describe speech intelligibility curve morphology according to the degree of CI performance. Methods: A bicentric retrospective observational study. Data were extracted from a cochlear implantation database from a total of 36 CI users implanted with Advanced Bionics devices. Results: Mean age at implantation was 56.61 years. In the majority of cases, hearing loss onset was more than 15 years before implantation (80.55%), and only 11.11% of cases preserved residual hearing. This resulted in a significant relationship between speech therapy and better speech recognition (p = 0.044). At the same time, no correlation was found between age, duration of deafness before implantation, and maximum speech perception achieved (p > 0.05). Mean speech audiometry curves displayed a roll-over phenomenon in poor performers and a plateau effect in average performers. In contrast, the mean curve of high performers exhibited a steeper morphology (p < 0.0001). Speech recognition threshold (SRT) and word recognition score (WRS) were predictors of speech audiogram curves (p = 0.006). No direct correlation was found between the mean T-level, M-level, dynamic range, and maximum recognition score, even after clustering electrodes by position along the cochlea (p > 0.05). Conclusions: EDR parameters did not emerge as independent predictors of speech recognition outcomes within this specific cohort. Speech therapy and rehabilitative efforts showed a significant relationship with improved performance, and speech audiogram curve morphology may offer a more specific clinical tool for assessing global CI performance. Further prospective studies with larger, more homogenous populations are required to validate these findings. Full article

Median crossover crashes are among the most severe roadway safety events due to their high-energy nature and strong association with fatal and incapacitating injuries, posing a substantial public health burden. This study develops a multimodal intelligent analytics framework to evaluate the cable median barrier performance in Tennessee by integrating structured crash data, roadway and traffic characteristics, post-impact vehicle responses, and unstructured police narratives. Across 6094 crashes on 576 cable barrier segments, 1196 involved barrier impacts and 914 included complete post-impact response information. Deep learning-based text mining using a BERT transformer model was applied to narrative descriptions from fatal, serious injury, and minor injury crashes to extract contextual indicators of loss of control, impact dynamics, and injury mechanisms. Safety effectiveness evaluation using Empirical Bayes methods showed substantial reductions after installation, including a 96% decrease in fatal crashes and an 88% reduction in serious-injury crashes. Vehicle–barrier interactions—classified as containment, redirection, rollover, or penetration—were modeled using a multinomial logit framework with marginal effects to assess the influence of geometric, operational, and vehicle-related factors. Reduced barrier offset, narrow shoulders, high traffic volumes, outer-lane departures, and heavy-vehicle involvement significantly increased the likelihood of rollover and penetration events, which are strongly linked to higher injury severity. Through fusing multimodal data and combining explainable statistical models with deep learning text analysis, this study provided a scalable, trustworthy approach to characterizing injury risk, aligning transportation safety analytics with emerging intelligent healthcare and big-data methodologies aimed at preventing severe and fatal trauma. Full article

In this study, a three-dimensional (3D) model is developed based on an actual small forestry crawler tractor, to analyze its overturning and rollover behaviors, and a corresponding simulation model is constructed. The accuracy of the 3D model is validated by comparing its dimensions and center of gravity with those of the physical tractor, and the fidelity of the simulation model is verified using static sidelong falling angle, minimum turning radius, and driving tests. The developed simulation framework was employed to investigate the dynamic behavior of the small forestry crawler tractor, focusing on roll and pitch angular velocities across different obstacle heights, slope angles, and driving speeds. Backward rollover was not observed within the tractor’s realistic operating speed range, indicating that backward rollover is not the dominant risk mode. In contrast, lateral overturning occurs under all driving scenarios, and increases in driving speed and obstacle height lead to higher roll angular velocities, increasing the risk of lateral overturning. Across all conditions, the likelihood of lateral overturning surges when the roll angular velocity enters the 80–100°/s range, with obstacle height exerting the greatest influence. In conclusion, the small forestry crawler tractor is more prone to lateral overturning than backward rollover when driving on inclined surfaces. A distinct threshold roll angular velocity is identified as the onset point of lateral overturning, which will vary according to the tractor’s specifications. This study is a quantitative study of a small forestry crawler tractor and does not correlate with a full-scale tractor. While angular velocity values vary during lateral overturning and backward rollover, this study was conducted to identify trends under various driving conditions. Further work is required to apply the proposed analysis methodology to full-scale agricultural and forestry machinery and validate it with real-world operational data. Full article

Articulated vehicles are essential for global freight transportation but are highly susceptible to instability phenomena such as jackknifing, trailer sway, and rollover, particularly under high-speed or emergency maneuvers. These challenges become even more critical in the context of autonomous driving, where stability must be guaranteed without human intervention. Conventional systems like Electronic Stability Control (ESC) and Roll Stability Control (RSC) provide reactive interventions but lack predictive capability, while other advanced methods often address isolated objectives. To overcome these limitations, this paper proposes a Model Predictive Control (MPC)-based control strategy that integrates trajectory tracking, yaw stability, and longitudinal speed regulation within a unified optimization framework, using differential braking as the primary actuator. A dynamic model of a tractor–semitrailer combination was developed, and the proposed controller was validated through high-fidelity simulations under varying operating conditions, including speeds exceeding the critical threshold of 31.04 m/s. Results demonstrate that the MPC-based system effectively mitigates instability, reduces articulation angle and yaw rate deviations, and maintains accurate path tracking while proactively managing vehicle speed. These findings highlight MPC’s potential as a cornerstone technology for safe and reliable autonomous operation of articulated vehicles. Future work will focus on experimental validation and multi-actuator coordination to further enhance performance. Full article