Virtual simulation testing has become the industry-wide norm for intelligent connected vehicles. Such testing demands a rich set of simulation scenarios. Based on data collected from naturalistic highway-driving scenarios, the paper develops an automated strategy for extracting lane-change cut-in events. A perception risk coefficient is introduced, and one-way ANOVA is used to analyze discrete factors while Pearson correlation analysis is employed for continuous factors, revealing the relationships between scenario elements and risk and identifying the key influencing factors. Typical scenarios are then derived with K-Means clustering and the elbow method, key parameters are retained, and each scenario is constructed on the Prescan simulation platform. The results show that this method can effectively extract critical lane-change cut-in scenarios from real-world data, cluster them into typical scenarios, and reconstruct these scenarios on the simulation platform, providing scientific support for autonomous-driving system testing.

The strong uncertainty of acoustic material parameters leads to the poor stability of its sound absorption and insulation performance. In this paper, a method based on evidence theory is proposed to analyze the stability of acoustic materials. The uncertainty of acoustic material parameters is described by evidence theory, and the focal element interval and the basic confidence assignment of each parameter are determined. Using the interval perturbation method, the upper and lower bounds of sound absorption and insulation performance corresponding to all focal element interval combinations are calculated. The reliability and plausibility of uncertainty problems are calculated according to the identification framework. Reliability and plausibility are taken as optimization objectives, and particle swarm optimization is used to improve the stability of acoustic material's sound absorption and insulation performance. Taking an inner front wall as an example, the stability analysis and optimization of the acoustic insulation performance of the acoustic material were carried out. After optimization, the quality of the part was reduced by 18%, and the stability of the acoustic insulation performance was greatly improved in the whole frequency band, especially in the middle and low frequency band.

The European New Car Assessment Programme (Euro NCAP) is an important reference for consumers choosing vehicles, and a leading indicator for global advances in automotive safety technology. This paper provides an in-depth interpretation of the latest trends in Euro NCAP testing protocols and compares the latest assessment results. Focusing on the segmentation of the safety-protection assessment systems, the paper reviews research progress in safety assessment techniques throughout the entire process, from safe driving and collision avoidance, to crash protection and post-crash safety. It also summarizes the current status of mainstream assessment systems, discusses the performance and characteristics of leading models, and offers practical guidance for improving China's vehicle-safety evaluation system and supporting the overseas expansion strategies of domestic brands.

To resolve the difficulty of identifying representative data and the poor fatigue-damage consistency in low-sampling-rate online data when constructing electric vehicle load spectra on big data platforms, the paper proposes a method with strong user association for compiling the load spectrum of an electric vehicle drive system. First, on the big data platform, user characteristics are described from five dimensions: road type, driving style, load capacity, vehicle speed, and torque. Based on these user profiles, the paper proposes a global-optimal-pairing filter that selects a representative online user dataset, and applies a constraint-based fragment stitching method to join the data segments in order, establishing a multi-feature association between the load spectra and users. To improve damage consistency in low sampling rate online data, high-sampling-rate offline data collected from real vehicles are incorporated to enhance damage equivalence between the load spectra and users. The feature matching results show that the filtered data set deviates from the target user by only about 0.05 for each feature parameter, with no deviation exceeding 0.15. Fatigue-damage calculations confirm that the fusion of low-rate online data with high-rate offline data effectively enhances the damage equivalence between the load spectrum and the users.

In order to ensure the safety, comfort and fuel economy of hybrid electric vehicle platoon, a hierarchical control strategy based on intelligent transportation systems is proposed. The upper controller used vehicle-vehicle(V2V) communication technology and used Nonlinear Distributed Model Predictive Control (NDMPC) to optimize the speed control and calculate the optimal speed. The lower controller obtained the upper vehicle speed, used the Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm for energy management, and embedded the engine best economic curve and battery characteristic curve as expert experience to improve the convergence and stability of the algorithm. The results show that under this strategy, the maximum relative error of vehicle distance is 4.83%, and the average acceleration of the platoon is 0.331 m/s². Compared with the Deep Q-Network (DQN) algorithm used in the lower layer, the fuel consumption is reduced by 14.25% on average, and the maximum is reduced by 15.30%. Compared with the DP algorithm, the average increase is 8.31%. It not only ensures the safety and comfort, but also effectively improves the economy of the platoon.

Existing driver-assistance systems often deliver late or inaccurate alerts when a vehicle cuts in suddenly from an adjacent lane. To address this issue, the paper develops a collision-warning model that detects the lane-change intention of the lead vehicle. The vehicle-vehicle communication is utilized to send that intention to the following vehicle, which then predicts the cut-in path and performs collision detection. The collision time TTC-S is proposed, the avoidance time TTA is re-examined, and a tiered warning strategy is designed. In order to verify the effectiveness of the collision warning system, a joint simulation platform is built based on Simulink and PreScan. The results show that the collision-warning model achieves an average true-positive rate of 90.32%, outperforming the Mazda model by 8.44% and the traditional TTC model by 11.66%. The system also provides earlier alerts, extending the average warning lead time by 1.42 s and 1.9 s, respectively, which provides a larger safety margin.

When an autonomous vehicle (AV) is in motion, the driving risks caused by the external environment can lead to occupants' distrust, reducing their acceptance of AVs. Therefore, quantifying occupants' perceived risk is crucial for designing and evaluating AV behavior, as it provides theoretical support for mitigating that risk. The paper quantifies the relationship between objective scenario-level risk factors and subjective perception in overtaking scenarios using a logistic regression model. Firstly, based on 92 overtaking segments of data collected in real-world driving experiments, 7 candidate risk factors are identified. Then, a logistic regression model is established in which the 5 risk factors that passed the hypothesis test are used as the independent variables and the binary classification of occupants' perceived risk serves as the dependent variable. The model analysis indicates that three factors, i.e. risk in adjacent areas (S), time to collision (t_{\mathrm{c}\mathrm{o}\mathrm{l}}) and time headway (t_{\mathrm{h}\mathrm{e}\mathrm{a}\mathrm{d}}), are significantly related to occupants' perceived risk, with t_{\mathrm{h}\mathrm{e}\mathrm{a}\mathrm{d}} being the most influential factor. To classify whether occupants perceive risk, the cut-off value of the prediction model is set at 0.462, which is calculated from the Receiver Operating Characteristic (ROC) curve. By using the HighD dataset, the cut-off value is verified and the accuracy of the prediction model is found to be 89.1%. On this basis, three optimized driving strategies are formulated to mitigate high perceived risk in overtaking scenarios. These three strategies are compared in driving-simulator tests in terms of traffic efficiency and perceived risk, confirming the validity of the model's analysis conclusions.

At present, the vehicle side impact safety evaluations rarely consider the effect of pre-crash braking on occupant posture. In order to study the occupant displacement induced by braking during the pre-crash phase and its impact on occupant kinematics and injury outcomes under side impact conditions, the paper combines volunteer experiments with CAE simulations. Three side impact models, including a standard posture model, a muscle-tensed model, and a muscle-relaxed model, were developed to compare differences in occupant kinematics and key injury indicators across the models. The results show that, during braking, the volunteers experienced greater displacement under the relaxed muscle state, with the maximum displacements of the head and first thoracic vertebra (T1) reaching 225 mm and 145 mm, respectively. This displacement significantly changed the contact between the upper body of the dummy and the side restraint system during the side impact, creating a risk that the dummy's chest could move outside the effective protection zone of side airbag. The peak Y-acceleration of the head was increased by 163.98 m/s²(79.4%), and the maximum abdominal compression was increased by 13.53 mm (64.0%) during the side impact. These results provide valuable insights for the development of advanced restraint systems and integrated safety testing methods.

Biodiesel is a good substitute for petroleum diesel, because of its good environmental performance, good engine starting performance, good fuel performance, wide source of raw materials, renewable and so on, it has a wide application prospect. However, the fat composition and content of different raw materials are different, and the performance and emission characteristics of the prepared biodiesel also show different trends during combustion. Based on the types of raw materials for biodiesel production, this paper classifies biodiesel and summarizes the characteristics of different kinds of biodiesel. Firstly, the physical and chemical properties and the preparation process of biodiesel was introduced in detail. Secondly, this paper is different from the classification of biodiesel based on the technological iteration of biodiesel production, but classifies biodiesel according to the source of raw materials, which provides a basis for the summary of the table and the horizontal comparison of biodiesel produced from various raw materials. Then, the use characteristics and changing trends of biodiesel prepared from different kinds of raw materials are summarized and sorted out in the table, and the summarized experimental results are visually presented, the characteristics of biodiesel produced from different raw materials were evaluated, in all the studies on biodiesel, it has been shown that in terms of pollution emissions, the engine found that the emission of CO and carbon oxides was reduced when using most biodiesel, and most studies showed that the emission of hydrocarbons was also reduced, but the emission of NO_x was increased, which is almost a common feature of all different types of biodiesel. Finally, according to the limitations and shortcomings of different types of biodiesel in the use process, the future direction of biodiesel use characteristics optimization was proposed.

This paper investigates the mesh independence of wheel fatigue life simulations. To address issues such as long fatigue assessment cycles, high testing costs, and the strong influence of mesh size on the finite element simulation accuracy, fatigue life predictions were carried out using both the nominal stress method and the local stress-strain method. The mesh independence of the simulation results was verified based on the grid convergence index (GCI) theory, leading to the recommendation of an optimal mesh configuration for fatigue life simulation. A fatigue life simulation method is proposed with high-quality meshing and low error range, which provides a theoretical foundation for establishing mesh independence in fatigue life prediction. Taking an aluminum alloy wheel as the research object, the wheel was meshed with different grid sizes according to the GCI theory. Radial and bending static simulations of the wheel, as well as fatigue life prediction simulations, were then conducted. The simulation results under different grid sizes were analyzed and a criterion was proposed to assess the rationality of tetrahedral meshing for aluminum alloy passenger car wheel hubs. Corresponding verification analysis was conducted. The results show that the GCI can effectively supports the mesh independence analysis and validation in wheel fatigue life simulations. The results in this paper provide a reliable basis for evaluating the suitability of tetrahedral mesh sizes in such applications.