The design of the floating valve plate effectively addresses the problem of cylinder block tilting at high speed in axial piston pumps/motors，aligning with the trend towards high-speed development of axial piston pumps/motors and gaining attention in recent years. However，there is still a lack of systematic dynamic modeling and dynamic characteristic research for the piston pump/motor system designed with floating valve plate，which limits the design of floating valve plate piston pump/motor products. A comprehensive parameterized dynamic model of the system that considers its detailed structural features is established for the piston pump motor system designed with floating valve plate. The study focuses on the laws of dynamic changes of the pressure in the high-pressure oil circuit and the auxiliary hydraulic chambers，and the model's correctness is verified through bench tests. The results show that the high-pressure oil circuit exhibits a "sharp drop and slow rise" characteristic of "sawtooth" pressure pulsations，which are intense. At a pump speed of 1 000 revolutions per minute （r/min），the average pressure in the high-pressure oil circuit at 20 and 40 MPa pressure levels results in pulsation amplitudes as high as ±1.5 and ±3 MPa，respectively. The fluid pressure in the auxiliary hydraulic chamber exhibits a dynamic change pattern of "rapid follow-up and slow decline，" meaning that once the auxiliary hydraulic chamber is connected with the high-speed rotating piston chamber，its fluid pressure almost immediately follows the piston chamber pressure changes without attenuation or lag. After disconnection from the piston chamber，it can still maintain the chamber pressure effectively.

The interaction ability between Highly Automated Vehicles （HAV） with human-driven vehicles is critical to the operational safety and efficiency of hybrid traffic in future. In order to test the interactivity of HAV，the background vehicle in the testing scenario needs to have naturalistic interaction characteristics and reflect the heterogeneous interaction strategy of human drivers. Based on the game theory，the Game-theoretical Strategic Interaction Model （GSIM） is developed in this paper. In the individual utility function，the interactive social characterization parameters with distinguishable values are introduced to directionally regulate the interaction strategy of the background vehicle. The test results of unprotected left-turning scenarios at intersections show that GSIM preserves the interpretability of natural driving stepwise planning and mutual interactions to ensure simulation accuracy of interactive behaviors. GSIM is also able to effectively reflect the interactive strategy of human driving in high-risk scenarios，helping to provide challenging and valuable testing scenarios. Compared to traditional Intelligent Driver Models，GSIM improves average simulation accuracy by 42.8% in unprotected left turn scenarios and serious conflicts recurrence rate by 25.8%.

The microstructure evolution and deformation behavior of resistance spot-welded joints of the two layer plates of 1500HS hot-formed steel sheets are studied in this paper. Through metallographic analysis，heat input distribution map，and alloy material property map，the microstructural changes at various positions relative to the weld nugget are analyzed. As the distance from the center area of the weld nugget increases，the microstructure of the welded joint can be divided into columnar crystal martensite，coarse-grained martensite，fine-grained martensite，ferrite-martensite dual phase microstructure and tempered martensite microstructure. Combined with Vickers hardness analysis，the differences in hardness under different organizational characteristics are clarified. The results show that the hardness decreases significantly in the ferrite martensite dual phase structure and tempered martensite region，which are the weak areas of the welding joints. Based on the experimental results of fusion size，maximum failure load，fracture surface macro-morphology，initial fracture location，and Vickers hardness of different plate thickness combinations，the influence of plate thickness and plate strength on the fracture mode，initial fracture location，and maximum failure load of the spot welded joints is explained.

A variable following characteristic traffic vehicle modeling method for intelligent driving system testing is proposed in this paper. Firstly，by clustering and analyzing natural driving data，a highly realistic interactive personalized car following model is established，and the model output coupling is used to assign multiple weights to construct a traffic vehicle model with variable car following characteristics that can be used for intelligent driving system testing. Then，by establishing the traffic vehicle trajectory evaluation method，the rationality，diversity and authenticity of the model's output trajectory are verified. Finally，a joint simulation platform is built to test the application of the constructed traffic vehicle model to the Automatic Emergency Braking （AEB） algorithm. The results show that the traffic vehicle model constructed in this paper can output reasonable，diverse，and realistic trajectories under different car following characteristics. When the number of trajectories reaches 60 000，the average root mean square error matched with the real natural driving speed trajectory is 0.427 m/s. Moreover，the behavioral response of the tested system varies under different traffic vehicle trajectory characteristics. By changing the weight coefficients，the evolution law of the tested system response can be revealed，and targeted testing of the tested system performance can be achieved.

For the problem of performance degradation in LiFePO₄ batteries under low-temperature conditions，a lightweight，high-strength，low-voltage，safe，and energy-efficient Fiber Carbon-nanotube film Laminated heating structure for LiFePO₄ battery is designed and developed，and experimental validation is conducted. The thermocompression technology is used to achieve the integrated molding of the Carbon-nanotube film and composite laminated structure. The experiment verifies the uniformity，stability and thermal fatigue resistance of a FCL （Fiber Carbon-nanotube film Laminated composite） heater. Furthermore，heating experiments on LiFePO₄ batteries in low-temperature environments are carried out，which is compared with the traditional Positive Temperature Coefficient （PTC） heater. The results show that compared to the traditional PTC heater，the FCL heater exhibits a 59% reduction in weight，a 3.5% decrease in energy consumption，a 26% improvement in temperature rise efficiency，and a 195% increase in power-to-weight ratio.

With excellent energy absorption properties of lightweight and high specific energy absorption，honeycomb material is widely used in various energy absorption protection structures. In this article，based on Voronoi diagrams and 3D printing technology，a novel gradient random honeycomb sandwich structure is designed and prepared. A finite element model of its three-point bending load is established and experimentally validated. Subsequently，based on the numerical model，crashworthiness research and multi-objective optimization design are conducted. The results show that for uniform random honeycomb sandwich structures，those with a lower degree of randomness have better energy absorption characteristics. Increasing the wall thickness increases the specific energy absorption but also leads to a larger load fluctuation coefficient due to the meso-structural deformation mode dominated by plastic hinges. When the relative density is consistent，the specific energy absorption of the random honeycomb sandwich structure with different cell size is not much different，and the decrease of cell size makes the deformation process more stable and reduces the bearing fluctuation coefficient. For cell size and cell wall thickness gradient random honeycomb sandwich structures，the introduction of a positive gradient of leads to a deformation mode dominated by both the support end and the loading end，which improves the energy absorption indicators. Based on the Non-dominated Sorting Genetic Algorthm-II （NSGA-II），a multi-objective optimization of the positive gradient random honeycomb sandwich structures is performed. The obtained meso-structural parameters with optimal energy absorption characteristics show a 33.9% increase in specific energy absorption compared to the uniformly random honeycomb sandwich structure without optimization design.

The detection of free space in underground mine tunnels is the key sensing technology for underground mining autonomous driving systems. However，the characteristics of low illumination and complex working environment inside the tunnels bring great challenges to this task. In view of this，in this paper an algorithm for detecting free space in underground mine tunnels is proposed. Firstly，a dual-branch feature extraction backbone network is proposed to solve the problem of difficulty in extracting image features caused by the degradation of tunnel details. Secondly，for the problem of incomplete detection of drivable areas in underground mining tunnels，an adaptive multi-scale atrous spatial pyramid pooling feature enhancement module is proposed. Finally，a dual-branch channel attention mechanism fusion module is developed to solve the problem of inaccurate boundary extraction in the underground mine tunnels. The experiments are conducted on a self-made dataset specifically designed for underground mine tunnels. The results show that the proposed algorithm surpasses other existing methods such as Deeplabv3+，UNet，DDRNet-23，and PIDNet，with an increase of 2.07，2.39，1.87，and 1.92 percentage points in terms of MIoU scores，and 1.78，2.45，1.84，and 1.86 in terms of mAcc scores，respectively. The effectiveness of the proposed algorithm has been validated through its successful application in real mine tunnel scenarios，particularly for underground mining autonomous driving vehicles.

The autonomous driving perception system must perceive the movement of the target vehicle to make reasonable interactive decisions. For the time lag in behavior perception，as well as the problem that possible fluctuations and outliers in the data lead to poor perception accuracy，an online semi-supervised hybrid approach is proposed in this paper. Firstly，a data-driven online prediction algorithm for vehicle motion state is designed using autoregressive integral moving average and online gradient descent optimizer. Then，an initial model based on micro-clusters is constructed，and an ensemble learning strategy is established using K nearest neighbor as the base classifier. Error-driven representative learning and exponential decay strategies are designed to achieve iterative updates of the initial model. Finally，experimental data to verify the effectiveness of the proposed algorithm is collected based on the driving simulation platform. The results show that the proposed method has rapid adaptability to vehicle behavior fluctuations. The online prediction algorithm can accurately predict vehicle motion trends，and the behavior perception algorithm has strong adaptability to vehicle behavior at different prediction times.

In order to ensure the safety and reliability of the high-level assisted driving system decision-making，a vehicle assisted driving behavior decision-making method based on dynamic driving risk assessment is proposed. Firstly，an obstacle risk assessment model and a virtual lane risk assessment model are established based on the potential field theory，which are used to describe the driving risk caused by dynamic traffic scenarios to the driving vehicle. Secondly，lane change behavior is divided into two stages according to the vehicle lane change process，which are lane change motivation generation and target lane safety decision-making. Further，the risk assessment indicator for lane change scenarios is proposed to formulate safe lane change rules，and the public data set is used to analyze and verify the risk assessment representation capability in lane change scenarios. Then，based on real-time traffic environment information，the driving behavior decision-making method in lane is determined to achieve safe decision-making in various driving scenarios. Finally，the proposed vehicle assisted driving behavior decision-making method is verified on the PreScan/CarSim/Simulink joint simulation platform and real vehicle road test platform. The results show that the proposed risk assessment model and driving behavior decision-making method can accurately identify and evaluate driving risk，and decide the vehicle driving behavior in real time and rationally，which effectively ensures the driving safety of the high-level assisted driving system.

Considering the limitation of global fixed bandwidth of load extrapolation for kernel density estimation，a load extrapolation method based on K-Average Nearest Neighbor Density-Based Spatial Clustering of Applications with Noise （KANN-DBSCAN） kernel density estimation （KDE） is proposed. The load data is grouped and clustered using the KANN-DBSCAN clustering algorithm，and the Rule-of-thumb （ROT） method is used to obtain the optimal bandwidth between different clusters. Then the kernel density estimation is conducted，and finally extrapolation is carried out using Monte Carlo simulation. The extrapolation rationality is verified using the measured load data of a certain electric vehicle on user road as the application object. The extrapolation effect is assessed by the three indicators of statistical parameter quantity，goodness of fit，and pseudo-damage. The results show that compared with the traditional fixed bandwidth kernel density estimation extrapolation method，the extrapolation load obtained by the DBSCSN kernel density estimation extrapolation method is closer to the actual load in statistical parameters，and the error of the mean，standard deviation，and maximum value is only 1.9%，4.3%，and 1.9%，respectively. The magnitude cumulative frequency curve fits R ² are all greater than 0.99，and the pseudo-damage is close to 1. The results show the effectiveness of the clustering method in kernel density estimation load extrapolation，which is helpful for compiling the load spectrum of electric vehicles on customer service road，and can provide reference for the load extrapolation of mechanical parts with similar load distribution characteristics.