Lightweight alloy wheels reduce vehicle weight, enhance fuel efficiency, and improve power performance, braking efficiency, and suspension system responsiveness. Through simulated impact testing of lightweight alloy wheels, engineers can gain a more comprehensive understanding of their performance under various road conditions, and conduct corresponding design optimizations to enhance safety and durability. This paper systematically reviews the current state of research on 13degree and 90degree impact simulations of wheels, both domestically and internationally. From the perspectives of simulation efficiency, accuracy, and convergence, it discusses the influence of tire models, tire pressure models, and contact properties on the 90degree impact simulations. The paper also introduces the application of automated simulation and deep learning technologies in wheel impact simulation research. The combination of these technologies help achieve standardization, normalization, automation, and intelligence in impact simulations.

Due to its low material costs and versatility in producing diverse products, 3D printing technology has attracted attention from researchers studying the crashworthiness of automotive energyabsorbing structures. This paper conducts quasistatic experimental research on automotive energyabsorbing structures, such as crash beams and honeycomb fillers, made from different base materials but with identical structural dimensions, using 3D printing technology. A comparative analysis of their mechanical responses and deformation modes is performed. Furthermore, finite element simulations are employed to study the influence of structural parameters on crashworthiness indicators. The results show that the loaddisplacement curves of 3Dprinted PLA crash beams and honeycomb filler structures exhibit similar trends to those of metal structures and reflect the deformation characteristics of automotive energyabsorbing structures. Increasing the wall thickness raises the relative density, which enhances the crashworthiness of both the crash beam and the honeycomb structure. Additionally, changes in the cell size also affect the crashworthiness of the honeycomb structure. This paper proposes an optimization scheme for automotive energyabsorbing structures based on 3D printing technology, systematically studying the effects of structural parameters on crashworthiness through experiments and simulations. This research offers valuable insights for designing educational tools in automotive engineering and analyzing automotive energyabsorbing structures.

Analyzing the extreme drifting conditions of vehicle tires can greatly improve the horizontal and vertical control capabilities and driving safety of autonomous vehicles. This paper first adopts the UniTire model to describe the friction characteristics in high slip regions, and then optimizes the vehicle drift control algorithm. Next, based on the vehicle stability control principle, the target pressure for the wheel cylinder was calculated to achieve a quick return of the vehicle to steady straightline driving after drifting. Finally, the rapid prototype verification was carried out on the CANoe's industrial computer platform by integrating DYNA4 and Simulink. The results show that the control algorithm proposed in this paper allows the vehicle to quickly achieve lateral and longitudinal stability during drifting and to promptly return to straightline driving after drifting, meeting the realtime control requirements.

To avoid unnecessary interventions by the driver assistance system, this paper combines collision risk and driving maneuverability to introduce the concept of a risk assessment zone in longitudinal following scenarios. The boundary of this zone is determined based on the normal distribution characteristics of the driving data. Subsequently, a new humanmachine codriving longitudinal driving rights allocation strategy is proposed, which takes the inverse time to collision (TTCi) as the basis for judgment. If the TTCi exceeds the threshold value, the upper boundary of the risk assessment zone represents the maximum deviation in driving maneuverability. The control rights of the assistance system are allocated according to the deviation in the driver's maneuverability. By combining Prescan, Matlab/Simulink and the Logitech G29 driving simulator, a driverintheloop simulation platform was constructed. The platform simulated the reduced driver maneuverability due to distracted driving, thereby verifying the effectiveness of the strategy. The results show that the proposed humanmachine codriving strategy can effectively prevent collisions caused by reduced driver maneuverability under highspeed road following conditions.

The paper aims to solve the problem of forecasting passenger travel demand in ehailing car operations, thereby reducing vehicle idle rates and minimizing passenger waiting times. Considering the dynamic spatiotemporal dependencies of passenger travel demand, this study proposes a method based on spatial data visualization and the Granger causality test for analyzing the spatial dependency. A spatiotemporal graph convolutional neural network model incorporating attention mechanisms is established to predict passenger travel demand. The case study shows that this model effectively captures the dynamic characteristics of the timespace dependencies of passenger travel demand, improves the prediction performance of the model, and achieves high accuracy and practicability.

With the advancement of autonomous driving technology, the accuracy and reliability of object detection have become increasingly crucial. Deep learning, as a core component of autonomous driving systems, significantly influences the safety and stability of these systems by estimating the uncertainty in predictive results. The paper summarizes the application of deep learning uncertainty estimation in autonomous driving object detection and discusses the significance of an effective uncertainty evaluation system. Firstly, the paper introduces the fundamental theories of deep learning uncertainty estimation, including Bayesian neural networks, Monte Carlo methods, and ensemble learning. These methods quantify model prediction uncertainty in different ways, providing autonomous driving systems with richer information. Secondly, the paper delves into the application of uncertainty estimation in autonomous driving object detection. Through case studies, it demonstrates how uncertainty information can be used to improve detection accuracy, especially in complex environments and extreme conditions. In these scenarios, uncertainty estimation provides decision support, helping the system avoid potential risks. Lastly, the paper summarizes the evaluation metrics for uncertainty estimation in autonomous driving object detection, considering both the model's predictive performance and the accuracy of the uncertainty estimation.

Aiming at the problem of how to recover the waste heat of the motor to improve the thermal performance of the passenger cabin, a simulation model of the thermal management system of a battery electric passenger vehicle is constructed by using AMESim software. On this basis, the effects of refrigerant distribution ratio and thermal management system architecture on passenger cabin heating performance are analyzed under the motor waste heat recovery mode. The results show that at a vehicle speed of 60 km/h, the heat generation of the motor can be up to 1 402 W and the heat generation of the motor controller can be up to 427 W. Compared with the nomotor waste heat recovery mode, the total heat absorbed by the thermal management system from the electric drive system and the environment can be increased by 58.69%100.57% and the passenger cabin heating power can be increased by 71.36%100.37% by distributing the refrigerant rationally. In the motor waste heat recovery mode, the passenger cabin heating power with the parallel architecture was 23.42% to 27.23% higher than that with the series architecture.

Thermal oxygen aging is one of the primary thermal failure modes for nonmetallic components in the engine compartment. The traditional vehicle thermal management development process typically only focuses on the maximum working temperature of components, which is insufficient for effectively predicting the thermal degradation of these parts over their lifecycle. Based on China's climate conditions and extensive vehicle driving data, an equivalent thermal aging driving condition model is established to evaluate the thermal aging life of components. Using this method, the thermal aging life of an engine mount rubber bushing is evaluated. It is found that improving the rubber material formula increases its thermal aging life by approximately 3 times. However, if the operating temperature of the rubber bushing increases by 10 °C, its thermal aging life decreases by about 50.0%. In addition, an analysis of the heat transfer path of the rubber bushing shows that its actual working temperature can be lowered by optimizing the fan wake and reducing heat conduction between the engine and the rubber bushing, thereby improving its thermal aging life.

A selfadaptive adjustment scheme is proposed to address the problem that traditional PI controllers cannot simultaneously achieve nonlinear gain transformation in both highspeed and lowspeed weak magnetic field voltage feedback loops. By analyzing the factors that lead to system nonlinearity in the decouplingvoltagefeedback currentleadangle control loop, an inverse function speed regulator and a fuzzy control current lead angle regulator have been designed. These regulators compensate for the nonlinearity of the voltage control loop under weak magnetic field conditions.Additionally, a clamping antisaturation module is developed to solve the issue of integral saturation in voltage feedback. On this basis, the particle swarm optimization algorithm is adopted to conduct offline optimization of the fuzzy rule weights and proportional factors in the fuzzy controller. Finally, simulation and experimental results show that the adaptive weak magnetic algorithm exhibits better response characteristics compared to traditional PI voltage feedback control algorithms.

In existing visionbased intelligent wiper systems, the raindrop target detection model has a large number of parameters and excessive computational complexity, making it challenging to deploy in vehicle embedded devices. To address these issues, the paper proposes a lightweight raindrop target detection model, YOLOV5RGA. By integrating the RepVGG and GhostBottleneck modules to replace the convolution and C3 modules of the backbone network, we enhance the network's feature extraction capabilities while significantly reducing the parameters and computational load. Furthermore, adopting the Adam optimizer results in faster convergence and improves the average accuracy of the network model. Through experimental validation, compared with the YOLOv5s model, the YOLOv5RGA model achieves a 0.8% increase in average accuracy. Additionally, the number of model parameters is reduced by 48.5%, computation demand decreases by 35.2%, and the model size shrinks by 44.4%. The adoption of the lightweight raindrop target detection model effectively reduces hardware overhead and also facilitates model deployment.