A weight distribution hitch (WDH) is a mechanical device that enables the adjustment of load distribution between the axles of a vehicle and a trailer. Since axle load distribution significantly influences the dynamic characteristics of vehicle-trailer (VT) systems, WDHs are widely adopted in light VTs to improve stability and maneuverability. However, despite their widespread use and the potential adverse effects due to improper usage, there is a notable lack of academic and technical literature addressing the operating principles of WDHs and their impact on vehicle dynamic characteristics. Thus, this paper aims to explain the operating principles of WDHs and analyze their effects on VT dynamic characteristics through pole-zero analysis with a linear model and time response analysis with a nonlinear model. The study concludes by summarising the findings and proposing future research directions, along with a brief discussion on recommended usage strategies of WDHs.

The 4-wheel independent steering (4WIS) vehicles are capable of highly flexible maneuvers, but trajectory tracking under high-curvature conditions remains challenging due to tire nonlinear characteristics and dynamic coupling. This paper proposes a novel model-based proximal policy optimization (MBPPO) framework that integrates an accurate nonlinear dynamic model with reinforcement learning. An accurate vehicle dynamics model is established, where the relationship between the steering instantaneous center offset and the tire nonlinear characteristics is analyzed to define a curvature error combining steering angles and slip angles. Building on this formulation, the output of a model predictive control (MPC) trajectory tracking controller is embedded into the proximal policy optimization (PPO) algorithm as ‘expert demonstrations’. This integration enhances sampling efficiency, accelerates convergence, and improves robustness compared with conventional PPO and MPC approaches. Co-simulation in CarSim/Simulink and Hardware-In-Loop (HIL) tests verify that the proposed MBPPO controller achieves faster learning, reduced lateral deviation, and smaller heading angle errors under high-curvature conditions.

Accurately estimating and predicting tyre–road friction is essential for optimising safety-critical control strategies and task planning in advanced driver assistance and autonomous driving systems. Recent approaches propose the integration of camera and vehicle dynamics-based estimation methods. These hybrid approaches achieve more reliable estimation but cannot retain and recall this information for a location. They are also unable to quantify uncertainty in tyre–road friction with respect to changing road conditions. This paper proposes a novel scheme for estimating tyre–road friction under unknown road conditions. A 2D map of the environment is combined with a vehicle dynamics model to enable joint estimation of environment and vehicle state, including tyre–road friction. A tailored sequential Monte Carlo scheme enables online updating of map information and allows for recall upon revisiting a location. Results show that uncertainty in tyre–road friction can be reduced during successive trials of emergency braking. As a result, braking distance predictions become more accurate, with an RMSE reduction of 4.95 %, averaged across three road types.

During the operation of rail transit, the track structure may experience settlement deformation due to various factors, thereby impacting the operating vehicles. This study proposes a novel deep learning model – the Physics-Informed Multi-Scale Attention Neural Network (PI-MSANet) – to estimate vehicle vibration responses under settlement deformation conditions. The model takes track irregularities with settlement deformation as inputs, while the predicted outputs include the vibration responses of the vehicle body and bogies. During the training process, physical constraints are incorporated to ensure that the multiple predicted outputs comply with the vibration mechanism. In addition, a multi-scale attention module is designed to extract track irregularity features across different scales. To verify the effectiveness of each module, a series of ablation experiments were conducted. The results demonstrate that the incorporation of physical constraints not only ensures the physical consistency of the predictions but also significantly enhances training stability, while the multi-scale attention module greatly improves prediction accuracy. Finally, the PI-MSANet was employed to estimate vehicle vibration responses on complete tracks with additional settlement deformation. The model achieved R2 values for all variables exceeded 0.97, and could accurately reflect the effects of settlement wavelength, settlement amplitude, and vehicle speed.

High-speed long-span bridges are increasingly designed for speeds up to 350 km/h using ballastless track structures to improve smoothness and reduce maintenance. However, the dynamic behaviour induced by beam-end expansion devices in such high-speed bridge systems has not been evaluated. This study investigates the dynamic responses of high-speed trains traversing the beam-end regions of such bridges by developing a coupled train–track–bridge model that accounts for flexible carbody, non-Hertzian contact, and detailed configurations of both the rail expansion joint (REJ) and beam-end expansion joint (BEJ). The model is verified through comparison with field measurements and benchmark results. Simulations reveal that the REJ introduces sharp contact transitions, causing substantial increases in wheel–rail forces and vertical carbody accelerations. These effects are sensitive to wheel wear, track irregularity, and train speed, with ride quality thresholds potentially approached under realistic service conditions. In contrast, BEJ expansion primarily affects structural components, especially suspended steel sleepers, whereas its influence on vehicle response remains limited. The findings emphasise the REJ as the dominant excitation source in the beam-end region of ballastless long-span bridges designed for 350 km/h, highlighting the need for geometric refinement and irregularity control to ensure good performance at current and future higher speeds.

The gear-rack meshing system is a critical subsystem of rack railways. Its periodic track excitation of the gear-rack meshing force and impact significantly reduces the ride comfort of the rack train. However, limited research has been published on suspension system optimisation for the rack train. To address this gap, the multi-objective optimisation of the rack train suspension system is studied in this paper. Firstly, the four-marshalling rack vehicle-track coupled dynamic model was established based on the vehicle-track coupled dynamics and gear dynamics. The reliability of the model was verified through field test. Subsequently, a parameter sensitivity analysis on the ride comfort of the rack train was conducted, and an improved dung beetle optimisation algorithm (INSDBO) was proposed. Finally, a UM-MATLAB co-simulation optimisation model was developed. The results indicate that the ride comfort of the rack train is primarily influenced by the partial secondary suspension, including traction rod stiffness. The performance of the improved algorithm is better than that of the common method. After optimisation, the RMS values of the carbody are significantly reduced by more than 21%. The vibration transmission from the bogie frame to the carbody decreases by 43.3% at the human-sensitive frequency and the gear-rack meshing frequency.

A high-speed vehicle running on a tangent track exhibited bogie hunting motion dynamic characteristics under specific conditions, resulting in significant abnormal vibration phenomena. These vibrations significantly deteriorated both ride comfort and running stability. Moreover, pronounced asymmetric rail wear is observed near the affected section, adversely affecting wheel-rail contact dynamics. This study systematically investigates the intricate interplay between asymmetric rail wear and bogie hunting motion through combined experimental and numerical approaches. Train-borne and in-situ experiments quantify vibration characteristics, while a three-dimensional vehicle-track interaction model elucidates the mechanism relationship. A rail grinding maintenance strategy is proposed and validated to effectively mitigate hunting motion. The results indicate that asymmetric rail wear excites the vehicle's natural frequencies, triggering bogie hunting oscillations as the mechanism underlying abnormal vibration phenomena, with rail grinding proving an effective countermeasure.

Two mainstream design approaches for inerter–spring–damper (ISD) suspensions are structure-based methods and immittance-based methods. However, structure-based approaches struggle to achieve optimal performances, while immittance-based designs can lead to increased structural complexity. To address the challenge, this paper proposed a reinforcement learning-powered generative design (RLGD) framework which simultaneously optimised both the structure and parameters of the ISD suspensions within a pre-defined number of elements. The RLGD framework integrated reinforcement learning with suspension dynamic simulation, enabling automatic generation and evaluation of candidate layouts through a reward-driven optimisation process. The design process was further interpreted and visualised, to make it trustworthy and provide guidelines for future ISD development. In addition, the relative network parameters, vehicle parameters and working conditions could be flexibly adjusted to accommodate different vehicle requirements. Test results showed that, for a sedan travelling on a Class A road at 30 m/s, the RLGD-generated suspension achieved 30.1% and 36.2% reductions in body acceleration and dynamic tyre load, respectively, compared with a conventional ISD layout of equal complexity. For a truck driven on a Class C road at 20 m/s, a three-element passive ISD layout demonstrated superior handling performance, compared with an MPC-governed active suspension while maintaining comparable ride comfort.

Automated parking requires accurate localisation for quick and precise manoeuvring in tight spaces. While the longitudinal velocity can be measured using wheel encoders, the estimation of the lateral velocity remains a key challenge due to the absence of dedicated sensors in consumer-grade vehicles. Existing approaches often rely on simplified vehicle models, such as the zero-slip model, which assumes no lateral velocity at the rear axle. It is well established that this assumption does not hold during low-speed driving and researchers thus introduce additional heuristics to account for differences. In this work, we analyse real-world data from parking scenarios and identify a systematic deviation from the zero-slip assumption. We provide explanations for the observed effects and then propose a lateral velocity model that better captures the lateral dynamics of the vehicle during parking. The model improves estimation accuracy, while relying on only two parameters, making it well-suited for integration into consumer-grade applications.

Integrated chassis controllers (ICCs), which are based on the coordinated or unified control of multiple vehicle dynamics actuators, are likely to benefit from vehicle-to-everything communication, advanced sensors, sensor fusion, and driving automation. In fact, such techniques anticipate information that can increase prediction accuracy and performance of model-based vehicle dynamics controllers. Although a few publications have explored road-preview-based vehicle dynamics control, there is a gap in terms of a critical assessment of the benefits of ICCs with different levels of preview. This preliminary proof-of-concept study analyses the potential advantage of a set of road preview variables provided to nonlinear model predictive controllers (NMPCs) for integrated torque-vectoring and front-to-total anti-roll moment distribution. In addition to the commonly adopted steering input preview, multiple preview combinations are implemented, e.g. on the longitudinal and lateral accelerations, as well as on the tyre-road friction factors. The sensitivity analyses, using an experimentally validated high-fidelity vehicle model and optimised control tunings, cover the benefit of each source of preview, while considering the effects of (i) actuation dynamics, (ii) prediction horizon and (iii) level of mismatch of the preview information.