Relative ride performance analysis of a torsio-elastic suspension applied to front, rear and both axles of an off-road vehicle

Chai, Mu; Rakheja, S.; Shangguan, Wen‐Bin

doi:10.1504/ijhvs.2019.102683

Cited by 4 publications

(3 citation statements)

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“…Data production without supervision would impact a model trained with redundant training sets. In order to recognise the environment in which predictable and unpredictable things are regarded, we must limit the data required to train the reinforcement learning model [30][31][32][33][34][35][36][37][38][39] to a set of data points.…”

Section: Related Workmentioning

confidence: 99%

“…The study can further be improved by considering the video saliency associated with object detection that involves both static and dynamic video saliency in future prediction systems. This can especially be tested on off-road vehicle for torsio-elastic suspension applied to front, rear and both axles [33]. This system can be extended with deep learning modules on base/meta classifier to enrich the transmission of hybrid vehicles in case of component sizing optimization [31] and wheel interaction measurement [32].…”

Section: The Model Is Trained On Various Environment and This Is Prov...mentioning

confidence: 99%

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Route Planning for Autonomous Transmission of Large Sport Utility Vehicle

Vijayakumar¹,

Shanthini²,

Karthik³

et al. 2023

Computer Systems Science and Engineering

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The autonomous driving aims at ensuring the vehicle to effectively sense the environment and use proper strategies to navigate the vehicle without the interventions of humans. Hence, there exist a prediction of the background scenes and that leads to discontinuity between the predicted and planned outputs. An optimal prediction engine is required that suitably reads the background objects and make optimal decisions. In this paper, the author(s) develop an autonomous model for vehicle driving using ensemble model for large Sport Utility Vehicles (SUVs) that uses three different modules involving (a) recognition model, (b) planning model and (c) prediction model. The study develops a direct realization method for an autonomous vehicle driving. The direct realization method is designed as a behavioral model that incorporates three different modules to ensure optimal autonomous driving. The behavioral model includes recognition, planning and prediction modules that regulates the input trajectory processing of input video datasets. A deep learning algorithm is used in the proposed approach that helps in the classification of known or unknown objects along the line of sight. This model is compared with conventional deep learning classifiers in terms of recall rate and root mean square error (RMSE) to estimate its efficacy. Simulation results on different traffic environment shows that the Ensemble Convolutional Network Reinforcement Learning (E-CNN-RL) offers increased accuracy of 95.45%, reduced RMSE and increased recall rate than existing Ensemble Convolutional Neural Networks (CNN) and Ensemble Stacked CNN.

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Section: Related Workmentioning

confidence: 99%

Section: The Model Is Trained On Various Environment and This Is Prov...mentioning

confidence: 99%

Route Planning for Autonomous Transmission of Large Sport Utility Vehicle

Vijayakumar¹,

Shanthini²,

Karthik³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Liu [8] constructed a nonlinear vehicle dynamics model and used an integration method to derive an analytical periodic solution of the system in the neighborhood of the critical speed. Mu [17] showed that elastic torsional suspension can improve the roll stability of vehicles, but the directional stability at high speed of vehicles with suspension is poorer than that of vehicles without suspension. Lopatka and Muszynski [18] analysed the limited perception of driver, which has insensitivity zones of the lateral and angular locations.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Stability Analysis of Articulated Vehicles

2021

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This study constructs a nonlinear dynamic model of articulated vehicles and a model of hydraulic steering system. The equations of state required for nonlinear vehicle dynamics models, stability analysis models, and corresponding eigenvalue analysis are obtained by constructing Newtonian mechanical equilibrium equations. The objective and subjective causes of the snake oscillation and relevant indicators for evaluating snake instability are analysed using several vehicle state parameters. The influencing factors of vehicle stability and specific action mechanism of the corresponding factors are analysed by combining the eigenvalue method with multiple vehicle state parameters. The centre of mass position and hydraulic system have a more substantial influence on the stability of vehicles than the other parameters. Vehicles can be in a complex state of snaking and deviating. Different eigenvalues have varying effects on different forms of instability. The critical velocity of the linear stability analysis model obtained through the eigenvalue method is relatively lower than the critical velocity of the nonlinear model.

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Ride and roll/yaw stability analysis of articulated frame steer vehicle with torsio-elastic suspension

Chai

Zhang

Wang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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An articulated frame steered vehicle model with torsio-elastic suspension is established in Adams/View. The model considered the influence of the hydraulic steering system on the yaw stability of articulated vehicles, thus, the hydraulic steering system is formulated and modeled in MATLAB/Simulink. The ride and roll/yaw stability of the vehicle model is investigated via co-simulation of Adams and Simulink. The Adams vehicle model is verified based on the vibration acceleration responses near the seat position at constant forward speeds. The hydraulic steering system model is validated through the steady-state steering maneuver. Relative ride performance of unsuspended and fully suspended vehicle is investigated in terms of unweighted and frequency-weighted root-mean-square accelerations. The roll and yaw stability of vehicle model with and without suspension at loaded and unloaded conditions are subsequently analyzed in terms of roll angle, roll safety factor, lateral acceleration, critical speed, and so on. The results show that the torsio-elastic suspension can efficiently reduce the vibrations of the vehicle, and the articulated frame steer vehicles applied with torsio-elastic suspension yield slightly lower roll/yaw stability but substantial reductions in the ride vibration levels. The results provide some reference for the suspension and steering system design of articulated engineering vehicle.

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Relative ride performance analysis of a torsio-elastic suspension applied to front, rear and both axles of an off-road vehicle

Cited by 4 publications

References 0 publications

Route Planning for Autonomous Transmission of Large Sport Utility Vehicle

Route Planning for Autonomous Transmission of Large Sport Utility Vehicle

Modelling and Stability Analysis of Articulated Vehicles

Ride and roll/yaw stability analysis of articulated frame steer vehicle with torsio-elastic suspension

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