Search Results

The ride performance potentials of a prototype torsio-elastic axle suspension for an off-road vehicle were investigated analytically and experimentally. A forestry vehicle was fitted with the prototype suspension at its rear axle to assess its ride performance benefits. Field measurements of ride vibration along the vertical, lateral, fore-aft, roll and pitch axes were performed for the suspended and an unsuspended vehicle, while traversing a forestry terrain. The measured vibration responses of both vehicles were evaluated in terms of unweighted and frequency-weighted rms accelerations and the acceleration spectra, and compared to assess the potential performance benefits of the proposed suspension. The results revealed that the proposed suspension could yield significant reductions in the vibration magnitudes transmitted to the operator's station.

Compared to the vehicles with conventional steering, the articulated frame steer vehicles (ASV) are known to exhibit lower directional and roll stability limits. Furthermore, the tire interactions with relatively rough terrains could adversely affect the directional and roll stability limits of an ASV due to terrain-induced variations in the vertical and lateral tire forces. It may thus be desirable to assess the dynamic safety of ASVs in terms of their directional control and stability limits while operating on different terrains. The effects of terrain roughness on the directional stability limits of an ASV are investigated through simulations of a comprehensive three-dimensional model of the vehicle with and without a rear axle suspension. The model incorporates a torsio-elastic rear axle suspension, a kineto-dynamic model of the frame steering struts and equivalent random profiles of different undeformable terrains together with coherence between the two tracks profiles.

Forecasting the motion of the leading vehicle is a critical task for connected autonomous vehicles as it provides an efficient way to model the leading-following vehicle behavior and analyze the interactions. In this study, a personalized time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The method enables a precise, personalized trajectory prediction for leading vehicles with limited inter-vehicle communication signals, such as vehicle speed, acceleration, space headway, and time headway of the front vehicles. Based on the learning nature of human beings that a human always tries to solve problems based on grouping and similar experience, three different driving styles are first recognized based on an unsupervised clustering with a Gaussian Mixture Model (GMM).

The optimal damping design of roll plane interconnected hydro-pneumatic suspension is investigated, in order to improve vertical ride and road-friendliness of heavy vehicles, while maintaining enhanced roll stability. A nonlinear roll plane vehicle model is developed to study vertical as well as roll dynamics of heavy vehicles. The damping valves and gas chamber are integrated within the same suspension strut unit to realize compact design. The influence of variations in damping valve threshold velocity on relative roll stability is explored, under centrifugal acceleration excitations arising from steady turning and lane change maneuvers, as well as crosswind. The effects of damping valve design parameters on the vertical ride vibration and vehicle-road interaction characteristics are also investigated under a medium rough road input and two different vehicle speeds.

This study investigates the performance potentials of hydro-pneumatic suspensions interconnected in the pitch plane of a heavy vehicle. Different configurations of interconnected suspensions comprising pneumatic, hydraulic or hybrid fluidic couplings between the front-and rear-suspension struts are proposed and analyzed. A 7-DOF pitch plane vehicle model is formulated to explore the relative vertical and pitch properties of different suspension configurations, as well as the dynamic responses of the vehicle under braking and road inputs. The mathematical formulations of strut forces due to both the unconnected and pitch-connected suspensions are derived. Relative performance potentials of different configurations are evaluated in terms of sprung mass pitch angle, suspension travel and stopping distance characteristics under different braking inputs and road conditions. The vertical ride quality is further assessed under a range of road roughness excitations and vehicle speeds.

This study investigates influence of compressible hydraulic fluid and suspension floating piston dynamics of fluidic suspensions on heavy vehicle roll stability and ride dynamics. Two fluidic suspension designs, including a single-gas-chamber strut and a novel twin-gas-chamber strut, are analyzed to develop the mathematical formulations of dynamic forces, upon considerations of hydraulic fluid compressibility and floating piston dynamics. Dynamic responses of the heavy vehicle with the different suspension configurations are then performed using a nonlinear roll plane vehicle model. The excitations arise from vehicle-road interactions as well as a steady steering maneuver. The results demonstrate that the compressibility characteristic of hydraulic fluid within a hydro-pneumatic suspension could affect the vehicle roll stability and ride dynamics, while the influence of suspension floating piston dynamics on vehicle dynamic responses is negligible.

Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system response to traffic situation correctly and improve the driving experience of intelligent vehicles. In this paper, two types unsupervised clustering methods are used to build a driver braking intention predictor. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. The proposed unsupervised learning algorithms can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different algorithms which are K-means and Gaussian mixture model (GMM) will be compared.

Two different concepts in hydro-pneumatic suspension struts are formulated to conveniently realize either hydraulic or pneumatic interconnections between the struts within different wheel suspensions. The formulation employs a compact strut design that integrates a gas chamber and damping valves within the same unit, and provides considerably enhanced working area to appreciably reduce the operating pressure. A transverse interconnection between the hydro-pneumatic struts in the roll plane is analyzed to investigate its static and dynamic heave and roll properties, and relative potential benefits in enhancing the roll properties, while retaining the soft heave ride. Different hydraulically and pneumatically interconnected strut configurations are analyzed for a heavy vehicle, with appropriate considerations of the fluid compressibility, while the feedback effects associated with the interconnections are emphasized.

The fusion of multi-modal perception in autonomous driving plays a pivotal role in vehicle behavior decision-making. However, much of the previous research has predominantly focused on the fusion of Lidar and cameras. Although Lidar offers an ample supply of point cloud data, its high cost and the substantial volume of point cloud data can lead to computational delays. Consequently, investigating perception fusion under the context of 4D millimeter-wave radar is of paramount importance for cost reduction and enhanced safety. Nevertheless, 4D millimeter-wave radar faces challenges including sparse point clouds, limited information content, and a lack of fusion strategies. In this paper, we introduce, for the first time, an approach that leverages Graph Neural Networks to assist in expressing features from 4D millimeter-wave radar point clouds. This approach effectively extracts unstructured point cloud features, addressing the loss of object detection due to sparsity.

The handling and braking responses of a heavy vehicle equipped with a cross-axle fluidically-coupled hydro-pneumatic suspension concept are investigated. The proposed fluidically-coupled suspension is conceived by diagonally interconnecting different hydraulic fluid chambers of the four suspension struts of the vehicle. The analytical formulations of suspension forces are derived based on fluid flows through the couplings and damping valves. A generalized full-vehicle model is developed and validated to evaluate the handling and braking responses to two critical vehicle maneuvers: (i) braking-in-a-turn; and (ii) split-μ straight-line braking. The responses of the vehicle model with the coupled suspension are compared with those of the uncoupled suspension under various inputs to demonstrate the potential benefits of the proposed cross-axle fluidic coupling of the suspension struts.