In order to realize the estimation of tire mechanical characteristics and the identification of tire models that do not rely on the physical sample of tires, and to accelerate satisfying the technical and accuracy requirements of tire virtual delivery, in this paper, based on the finite element software ABAQUS, a method for simulating the tire camber-turn-slip combined condition is proposed, and the influence of camber on the turn-slip is analyzed. Firstly, the tire finite element model is constructed, and the simulation accuracy of the model is verified by the bench test data, and simulation methods of tire camber-turn-slip combined using implicit solver is proposed. Secondly, According to the spin turn condition under the special case of turn-slip, the influence of the inclination angle on the spin turn aligning moment is analyzed. Finally, the camber-turn-slip conditions of tires with different load are simulated, and the influence of camber on the lateral force, aligning moment stiffness region and the whole area of the turn-slip mechanical characteristics are analyzed. It is concluded that the camber has a significant nonlinearity on the stiffness area of the lateral force and aligning moment, and affect the curve features of the decay rate of the lateral force, the curvature of the transition zone of the aligning moment and so on.

In this article, the effect of adhesive type, substrate properties, and structural dimensions on the mechanical properties of basalt fiber reinforced polymer (BFRP)-aluminum alloy (AA5052) and BFRP-BFRP single lap adhesive joints is investigated. Using the response surface methodology (RSM), a predictive model is es-tablished to evaluate the impact of three process parameters (aluminum substrate thickness, overlap length, and the angle between the loading direction and the primary direction of the basalt fibers) on the mechanical performance of the joints. The results indicate that the strength and stiffness of the adhesive joints are influenced by the yield strength and stiffness of the bonded substrates. BFRP-BFRP adhesive joints exhibit higher peak load, whereas BFRP-AA5052 adhesive joints demonstrate greater overall stiffness. The use of brittle structural adhesives can sig-nificantly enhance the strength and fracture energy absorption of the adhesive joints, reaching up to 57.4% and 1 128.5%, respectively. The shear strength Y is introduced as an evaluation metric for assessing the adhesive strength utilization rate. A strength prediction model for Y is established based on RSM, resulting in a regression equation with good significance, and the optimal range for the process parameters is predicted. The analysis of the coupling effect of the process parameters based on the strength prediction model reveals a negative correlation be-tween fiber direction and joint strength, while overlap length and aluminum substrate thickness show a positive cor-relation with joint strength. Considering the joint strength, adhesive cost, and lightweight effect, it is recommended that the loading direction aligns with the primary direction of the fibers, with the overlap length controlled within the range of 20 mm to 25 mm, and the aluminum substrate thickness within the range of 2 mm to 2.5 mm. This study provides theoretical and data support for the application of BFRP-AA5052 adhesive structures in transportation equipment.

Al-Si coated press hardening steels (PHS) with coating thicknesses ranging from 8 to 18 µm demonstrate enhanced toughness, drawing significant attention from the industry. However, there is limited evalua-tion of the resistance spot welding performance of Al-Si coated PHS with reduced coating thickness. This research compares the weldability of PHS with thin Al-Si coatings at strengths of 1 000, 1500, and 2 000 MPa. The results show that the weldability current range and mechanical properties of welds for all three grades of PHS meet industri-al production requirements. Further analysis reveals that the mechanical properties of the welds are closely linked to the strength and toughness of the martensite in the nugget. As the matrix strength increases, the strength (hardness) of the martensite in the nugget also rises, while toughness decreases. Consequently, the tensile-shear ultimate load increases with rising weld strength, whereas the cross-tensile ultimate load decreases as weld toughness diminishes.

Thin-walled tubes, commonly used structures in automobile lightweight and industrial production, have the advantages of lightweight and high strength. The study of the axial compression instability characteristics of thin-walled tubes is helpful for its application in optimizing structural design and safety. Therefore, a novel axial compression fold model of thin-walled tubes is proposed to describe the morphological characteristics and average compressive load of deformation folds for thin-walled tubes based on the energy method. The prediction accuracy of the new theoretical model and the plastic hinge model for the fold length and compressive average load is validated by experiments and finite element simulation. The results show that the fold length predicted by the new theoretical model is closer to the experimental and finite element results compared with the plastic hinge model, with the average prediction error reduced by 55.2%. The prediction accuracy for compressive average load is improved by 29.7% after the friction coefficient correction. When guiding engineering practice, a fold prediction model considering friction effect correction should be adopted.

Constructing accurate surrogate models is an effective solution to addressing the problem of multi-dimensional design variables and implicit nonlinear responses in the reliability design of complex structures. However, using experiment design based on a predetermined sample size to construct surrogate models may face challenges of inefficiency or insufficient accuracy. Therefore, an active learning PC-Kriging model for reliability analysis is proposed, which combines the advantages of Polynomial Chaos Expansion for enhancing global approximation accuracy and Kriging for capturing local features. The active learning strategy is utilized to adaptively select the optimal sample points to minimize the training sample size, reducing computational cost of structural performance analysis, and improving analysis efficiency. Further, an active learning PC-Kriging model-driven multi-software co-design framework is constructed. Secondary development of pre-processing and post-processing software is conducted to enable seamless integration of parametric modeling, performance analysis, and post-processing, forming a comprehensive automated analysis workflow. Finally, reliability analysis is performed using a battery pack structure as a case study to verify the efficiency and accuracy of the proposed method.

In the process of vehicle intelligence，in-vehicle sound field zoning control technology plays a crucial role in enhancing the acoustic experience within the cabin. In this paper，a comprehensive review of in-vehicle sound field zoning control algorithms and their application are provided. Firstly，the background and theoretical basis of the technology are introduced. Then，the development process，control principles，and characteristics of various sound field zoning control algorithms are thoroughly analyzed. Finally，based on the existing research progress，the potential advancements in sound field zoning control technology with regard to reproduction accuracy improvement，algorithm robustness，and sound field uniformity are explored，and a series of challenges limiting the widespread application of the technology in vehicles and the solutions are discussed. The review aims to provide reference for further research on in-vehicle sound field zoning control and to promote widespread application of the technology in the vehicle industry.

Uncontrolled intersections are highly dynamic and strongly interactive decision-making scenarios，in which it is a challenging task to enable automated vehicles to make safe and reasonable decisions similar to skilled drivers and pass through the intersections successfully. The subjective attributes of ontology in cognition and decision-making process are fully considered in this paper，and an interactive human-like decision-making method based on sequential games for automated vehicles is proposed. Firstly，the multi-objective driving triggers are deeply explored from multiple dimensions such as traffic efficiency，space margin，ride experience，and driving safety. Further，a game decision-making model is established，which is embedded with personalized and human-like driving characteristics and can match driver and passenger groups with different driving modes and types. On this basis，the concept of sequential priority and the self-perspective decision-making scheme that imitates human logic are proposed to realize self-evolution of sequential patterns of rolling stage game decision-making. Finally，the effectiveness of the proposed method is verified through multiple sets of comparative experiments. The results show that the interactive human-like decision-making method proposed in this paper can resolve potential conflicts and deal with safety decision-making problems in a continuous and interactive manner，while improving the naturalized and human-like effect of personalized decision-making of automated vehicles.

Accurate evaluation of scenario similarity is extremely important for optimizing test scenarios. However，existing trajectory-based evaluation methods fail to adequately capture the complex dynamic interaction characteristics between vehicles at intersections，which affects the accuracy of the evaluation results. To address this problem，in this study a directed graph-based similarity evaluation method for urban intersection scenes is proposed，which quantifies the similarity between scenes by comparing the degree of spatial and temporal matching of the global interaction topologies of vehicles in two scenarios. Firstly，a directed graph is used to characterize the interaction topology between vehicles at each urban intersection. Then，the interaction similarity between different intersection scenarios is estimated by comparing the degree of matching of their directed graph structures. Finally，a dynamic time warping algorithm is used to align the scenarios in the time dimension to effectively compare two test scenario sequences of different lengths. The results of the qualitative analysis of three pairs of typical evaluation cases demonstrate that the method is capable of distinguishing scenes with different similarity levels at a fine-grained level. Furthermore，to quantitatively validate the effectiveness of the method，an ANOVA experiment is conducted to compare scenario similarity with the performance of the autopilot system. The experimental results reveal that the safety and efficiency of the system exhibit significant differences under test conditions with different levels of scenario similarity，thus proving the method's effectiveness. Ultimately，this method is applied to optimize Apollo. Ultimately，this method rformance of the autopilot system. The experimental results reveal that the safety and efficiency of the system exhibit significantd

With the development of Intelligent Connected Vehicle （ICV） technology，ICVs with limited computing resources face the problem of significantly increased computational demand. ICVs can offload tasks to Mobile Edge Computing （MEC） servers via Roadside Units （RSU）. However，the dynamic and complex nature of vehicular networks makes task offloading and resource allocation highly challenging. In this paper，it is proposed to minimize task computing energy consumption by controlling task offloading decision，communication power，and computing resource allocation under environmental and resource constraints. To address the coexistence of discrete and continuous control variables in the problem，a Hybrid Deep Reinforcement Learning （HDRL） algorithm is designed. The algorithm employs the Double Deep Q-Network （DDQN） to generate task offloading decisions and the Deep Deterministic Policy Gradient （DDPG） to determine communication power and MEC resource allocation. Furthermore，an Improved Prioritized Experience Replay （IPER） mechanism is integrated to evaluate and select actions，outputting the optimal strategy. Simulation results show that the method achieves faster and more stable decision convergence than comparative algorithms，minimizes the energy consumption for task computation offloading，and effectively adapts to changes in the number of ICVs and task sizes，demonstrating high real-time performance and excellent environmental adaptability.

The optimal control method has become the mainstream research and industry deployment method for lateral motion control in autonomous driving. The LQR method is widely used due to its advantages of low online computational load and good real-time performance，but it cannot consider time-varying references and steering delay. The presence of delay can cause the LQR method to lose stability at high speed，so it is essential to solve this problem while maintaining the characteristic of small computational load of LQR. In this paper，under the premise of ensuring real-time performance，the problem of LQR's inability to consider time-varying references and steering delay is solved. By incorporating road curvature as time-varying references，steering delay characteristics as pure delay，and first-order inertial section into the tracking error state equation，and by looking up the KKT inverse matrix part corresponding to the control time domain into the real-time solver，the aim is to reduce computational load and ensure controller real-time performance. The simulation results demonstrate that the constructed EqLPV-MPC controller can effectively handle road curvature changes. Compared to the LQR method，under the condition of dual lane change at a speed of 72 km/h，the lateral error decreases by 39%，with the heading error decreasing by 52%，and the lateral deviation of the center of mass decreasing by 28%. The results from real vehicle tests show that under dual lane change conditions，the controller constructed in this paper can keep the maximum lateral error within 0.1 m.