Current Issue : January-March Volume : 2025 Issue Number : 1 Articles : 5 Articles
Cooperative vehicle infrastructure technology has emerged as a cutting-edge and indispensable trend within the transportation sector. While addressing the supply-side requisites of the technology, it is equally important to investigate its demand-side response. To investigate the public acceptance of cooperative vehicle infrastructure technology and its influencing factors, this paper constructs an extended Technology Acceptance Model (TAM). Then, the paper employs the structural equation model (SEM) to validate the path hypotheses of the model, and pinpoints the variables that significantly influence the intention to use the technology. Moreover, the Bayesian network (BN) model is utilized to assess the magnitude of the effects of diverse influencing factors on the acceptance of the technology. The research findings can provide recommendations for the government to expedite the promotion and implementation of cooperative vehicle infrastructure technology....
An automotive bumper is a key component designed to protect passengers from front and rear collisions. Bumpers serve a crucial function in absorbing collision forces to protect both the vehicle and its occupants. The ABAQUS/Explicit code is used to investigate the influence of the bumper beam parameters on their impact resistance. Eight materials (4 metallic and 4 composite) were compared to see how material modifications would improve outcomes and determine the optimal bumper beam material. Four bumper designs (profile-A, profile-B, profile-C, and profile-D) were compared to see how design adjustments would improve outcomes and discover the optimum bumper beam configuration....
This paper presents a novel incremental sliding mode control scheme to address the attitude-tracking issue in both the helicopter and airplane modes of an electric vertical takeoff and landing vehicle, guaranteeing the stabilization of the attitude-tracking error within a predefined time. Firstly, an incremental model of the vehicle’s attitude control system with external disturbances is established. The high-order terms of the incremental model and instantaneous perturbations are retained as lumped terms rather than directly discarding them to ensure the accuracy of the incremental model. Then, a novel nonsingular sliding surface is developed. Once the ideal sliding motion is established, the states on the sliding surface will converge to the equilibrium point within a predefined time. Furthermore, a predefined-time incremental sliding mode controller is developed by using sliding mode control and incremental control techniques. It effectively reduces the reliance on the model information and attenuates the effects of external disturbances. The predefined-time stability of the entire controlled system is rigorously proven using Lyapunov theory. Finally, numerical simulation examples verify the effectiveness of the proposed control scheme....
With the rapid advancement of autonomous driving technology, the accurate prediction of vehicle trajectories has become a research hotspot. In order to accurately predict vehicles’ trajectory, this study comprehensively explores the impact of driving style and intention on trajectory prediction, proposing a novel prediction method. Firstly, the dataset AD4CHE was selected as the research data, from which the required trajectory data of vehicles were extracted, including 1202 lane-changing and 1137 car-following driving trajectories. Secondly, a long short-term memory (LSTM) network based on the Keras framework was constructed by using the TensorFlow deep-learning platform. The LSTM network integrates driving intention, driving style, and historical trajectory data as inputs to establish a vehicle-trajectory prediction model. Finally, the mean absolute error (MAE) and root-mean-square error (RMSE) were selected as the evaluation indicators for the models, and the prediction results of the models were compared under two conditions: not considering driving style and considering driving style. The results demonstrate that models incorporating driving style significantly outperformed those that did not, highlighting the critical influence of driving style on vehicle trajectories. Moreover, compared to traditional kinematic models, the LSTM-based approach exhibits notable advantages in long-term trajectory prediction. The prediction method that accounts for both driving intention and style effectively reduces RMSE, significantly enhancing prediction accuracy. The findings of this research provide valuable insights for vehicle-driving risk assessment and contribute positively to the advancement of autonomous driving technology and the sustainable development of road traffic....
In order to improve the autonomous lane-changing performance of unmanned vehicles, this paper aims to solve the problem of inaccurate decision classification in traditional support vector machine (SVM) algorithms applied to the lane-changing decision-making stage of intelligent driving vehicles. By using game theory-related theories and combining the improved support vector machine (SSA-SVM) method, a vehicle autonomous lane-changing strategy based on game theory is established. The optimized SVM method has certain advantages for vehicle lane-changing decisionmaking with a small sample size in actual production processes. The lane-changing decision judgment accuracy rate of the SSA-SVM algorithm model can reach 93.6% compared with the SVM algorithm model without algorithm optimization; the SSA-SVM algorithm model has obvious advantages in decision performance and running speed. Therefore, the proposed new algorithm can effectively solve the problem of the objective consideration of the payoff function in conventional decision game theory....
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