To improve the olfactory experience satisfaction of intelligent cockpit vehicle fragrance users, and to find out the preferred fragrance categories of target user groups, this paper proposes a method for assessing vehicle fragrance preferences based on users' physiological signals. An experimental setup was created to assess the olfactory preferences of vehicle fragrance users, utilizing a smell experience tester as the odorgenerating device. Three commonly used vehicle fragrances, i. e., mint, jasmine and orange, were selected as the test samples. Thirtytwo participants were recruited for the test, and the changes in skin conductivity, pulse and respiration were measured and recorded by using the ErgoLAB multichannel physiological monitoring system. After the initial data processing on the ErgoLAB humancomputer interaction platform, users' subjective preference data were collected by using a semantic difference scale. A comprehensive evaluation model based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was then established to analyze the objective data, and correlation analysis was conducted using Spearman's correlation coefficient to validate the findings. The results show a significant positive correlation between the comprehensive scores of objective physiological data and users' subjective preference ratings, with mint fragrance being the most satisfying to users.

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.

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.

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.

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.

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.

Aiming at the lack of effective methods for testing abnormal signals during the operation of unmanned vehicles, the paper focuses on signal anomalies caused by environmental disturbances in reliability driving tests. By using the correlation of signals from multiple sensors in both the time domain and spatial domain, a crossmathematical model is established based on the multisensor data. The signals collected from sensors are assigned as the row elements and the sensors as the column elements within the signal matrix. This numerical method transforms the original multisensor signals into a parameterized signal matrix model. A method combining matrix completion and deep matrix decomposition fusion (MC+DMF) is proposed to recover certain abnormal signals resulting from environmental disturbances. According to the forward propagation characteristics of the neural network, dimensionality reduction is applied to the row vectors (data collected by individual sensors at time i ) and column vectors (sensor arrays) in the original matrix. This process reduces the computational load during feature extraction from the distorted signal matrix. Additionally, the Hadamard product is used to regularize the MC+DMF loss function after feature extraction to avoid overfitting. The proposed method is applied on the SODA10M and KITTI public datasets, and comparing with traditional approaches, such as the single matrix factorization (MF), probability matrix factorization (PMF) and BiasSVD, the experiments using root mean square error (RMSE) show that the method can effectively detect abnormal sensor signals caused by vibration interference during driving. The results show that the MC+DMF method can greatly reduce the data recovery error and time. Compared with the probability matrix decomposition method, it achieves a 1% lower error rate and approximately 20.65% less recovery time.

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.

Lane line detection is a key technology in the field of autonomous driving, and it currently faces many challenges. The sparsity of the lane line supervision signal, as well as factors such as occlusion and shadows in complex scenes, can affect detection accuracy and realtime performance. Based on this, this paper proposes a lane line detection model that integrates the CBAM attention mechanism and a line anchor feature aggregation module. The proposed algorithm achieves an accuracy of 96.19% and a comprehensive F1 score of 76.24% on the Tusimple and CULane datasets, respectively. Real vehicle tests show that the algorithm detects a frame rate of 67 fps, allowing for realtime detection in complex traffic scenarios and more effectively addressing the problem of lane line occlusion.

To fully utilize the energysaving and emissionreduction performance of the oilelectric hybrid system on mine truck, a dualmode energy management strategy (EMS) is proposed for a series hybrid electric mine truck. The back propagation neural network (BPNN) was used to construct the models of "optimal fuel consumption mode of engine" and "optimal efficiency mode of range extender" in this EMS. On this basis, a dualmode EMS was designed to adjust the power output of the range extender and battery pack, realizing the realtime adjustment of the energy consumption for the vehicle under complex working conditions. Finally, the proposed EMS was verified by hardware in the loop simulation with actual working condition data. The results show that compared with the rule strategy and equivalent consumption minimization strategy, the fuel saving rate of the dualmode EMS increases by 12.74% and 7.4% respectively, further improving the fuel saving performance of the realtime strategy.