Most of the existing domain adaptive visual object detection algorithms are based on two-stage detector design and fail to exploit the semantic topological relationship between different elements in the image space， resulting in suboptimal cross-domain adaptation performance. Therefore， in this paper a domain adaptive visual object detection algorithm based on multi-granularity relationship reasoning is proposed. Firstly， a coarse-grained patch relationship reasoning module is proposed， which uses the coarse-grained patch graph structure to capture the topological relationship between the foreground and background and perform cross-domain adaptation on the foreground area. Then， a fine-grained semantic relationship reasoning module is designed to reason about the fine-grained semantic graph structure to enhance cross-domain multi-category semantic dependencies. Finally， a granularity-induced feature alignment module is proposed to adjust the weight of feature alignment according to the affinity of the nodes， thereby improving the adaptability of the detection model when facing overall scene changes. The experimental results on multiple cross-domain scenarios of autonomous driving verify the robustness and real-time performance of the proposed algorithm.

For the problems of difficult and uncontrollable data acquisition， as well as limited quantity of available rainy day scene samples in the process of unmanned driving perception performance training， a multimodal fusion-based algorithm for constructing rainy day traffic scenes is proposed. Firstly， the rainy day scenes are analyzed and categorized into two models of rain line models and raindrop models for reconstruction. Secondly， a stochastic multisource fusion-based rain line model is proposed， which integrates rain effect from multiple directions and densities. Next， a heterogeneous mapping-based raindrop model is proposed to achieve realistic convex transparency mapping for individual raindrops， coupled with collision prevention design to mitigate cumulative errors of multiple raindrops in the same area. Finally， the two models are integrated to realize reconstruction of rainy day scenes by using various foundational forms. The experimental results show that as rainfall intensity increases， detailed information in the constructed scenes becomes richer initially， with metrics such as entropy and average gradient showing an initial increase followed by a decrease， while image quality continuously decreases， approaching realistic rainy day conditions. With higher rainfall intensity， both interference and detail in the images notably increase， with higher entropy and average gradient， as well as decreased PSNR and SSIM parameters， indicating significant image quality degradation.

In this paper， the characteristics of driver out of position and active and passive fusion damage caused by AES are studied by using finite element method for several typical collision conditions caused by automatic emergency steering （AES） intervention. The results show that AES can cause significant lateral displacement of the driver， and the out of position degree increases slightly with the increase of initial speed. High HIC₁₅ and BrIC values are easily generated in oblique angle and side-to-side collision conditions due to high speed and hard contact. The risk of craniocerebral injury in side impact is greater， and the strain of liver and lung is greater than that of other internal organs. Overall， AES intervention results in more significant head， neck， and chest injuries in oblique and lateral near-end collision.

The lateral， longitudinal， and yaw motions of corner module vehicles can be planned and controlled relatively independently. However， the impact of the trajectory on the vehicles' yaw motion is not adequately considered by traditional trajectory planning methods. A polynomial-based pose trajectory planning method for corner module vehicles is proposed in this paper. Firstly， a quintic polynomial-based pose trajectory parameter model is established to generate pose trajectory clusters， Then， considering the road adhesion state constraint， kinematic model constraint， and sideslip angle constraint， the evaluation functions including lane-changing efficiency， lateral performance， yaw angle deviation， and yaw performance are established to generate the optimal polynomial pose trajectory as well as the optimal classical position trajectory. Finally， the two optimal trajectories are compared in highway lane-changing scenarios， and the traceability of the polynomial pose trajectory is verified using MATLAB/Simulink and CarSim co-simulation. The simulation results show that the efficiency of lane-changing can be increased by the polynomial pose trajectory， and the vehicle's yaw comfort and stability can be substantially improved.

To improve the control accuracy of intelligent vehicle tracking controllers in variable operating conditions， controllers generally use multidimensional control parameter tables based on operating condition characteristics. When engineers manually adjust multidimensional control parameter tables， the workload is large and the tuning effect is not satisfactory. In order to enable the tracking controller of dynamic parameter adjustment capability， in this paper a vehicle speed and curvature adaptive parameter tuner is proposed based on radial basis function （RBF） neural network. Besides， a training set construction method based on Monte Carlo Probabilistic Inference for Learning Control （MC-PILCO） algorithm is proposed to address the problems of excessive real vehicle testing interactions and heavy tuning workload encountered during the training of tuner. By grouping typical operating conditions based on vehicle speed in the construction process of the training set， all different curvature working conditions within each vehicle speed working condition group are trained using the dynamic model trained on the data collected from tracking the straight-line scene at that vehicle speed for parameter tuning. By sharing the model， the number of real vehicle interactions is reduced. Real vehicle experiments show that the parameter adaptive tracking controller proposed in this paper has better lateral trajectory-tracking performance compared to controllers with fixed parameters under medium and low speed conditions.

Rain and snow often lead to slippery and low adhesion road surface， and the bottleneck of vehicle braking technology caused by it needs to be broken through. Among them， due to the differences in road adhesion in complex open-side road conditions， higher stability requirements are put forward for emergency braking control of multi-axle commercial vehicles. In order to improve the braking efficiency， the model free adaptive control （MFAC） algorithm is used to control the slip rate of each tire near the ideal value， and the electric and hydraulic coupling braking torque distribution strategy is set for distributed drive technology. In order to reduce the lateral errors in the braking process， PID-sliding mode observer （PID-SMO） is used to accurately observe the longitudinal force of each wheel， and the additional yaw torque caused by the longitudinal force difference is compensated by the middle and rear axle assisted steering. Through the joint simulation analysis， the emergency braking control strategy based on MFAC reduces the braking distance and avoids the slip rate fluctuation at the end of the braking period， ensuring the consistency of the wheel speed. The intervention of middle and rear axle steering greatly improves the lateral stability of the vehicle during braking.

Taking into account of factors such as core magnetic saturation， torque fluctuation， and component flexibility， an electromechanical coupling dynamic model for the switched reluctance motor-planetary gear electric drive system suitable for unsteady state conditions is established， with translational and angular displacements as generalized coordinates， which is validated through experiments. Through simulation analysis， the dynamic characteristics of the system under acceleration and variable load conditions are studied. The results show that under acceleration conditions， the speed at which the electric drive system is most prone to resonance is 3 900 r/min， at which multiple excitation frequencies cross the natural frequency of the system. Among them， the vibration energy generated by the excitation of the 15th order natural frequency at the gear mesh frequency is the largest， and the vibration energy is mainly concentrated in the θ_y direction of the planet carrier. At the moment of sudden load change， the system produces low-order free vibration dominated by the 5th order natural frequency， with vibration energy mainly concentrated in the θ_{xand θy directions of the inner gear ring and gear housing.}

Torsional vibration of power system is a hot and difficult problem in NVH field of extended range electric vehicles. In order to investigate the torsional vibration characteristics of the range extender under electromechanical coupling， taking a diesel engine range extender as the research object， systematic quantification of the shaft system is carried out and a torsional vibration mechanical model of the eight-degree-of-freedom shafting system is established. A non-contact measurement method is used to conduct torsional vibration tests on the range extender platform to verify the accuracy of the model. The method of obtaining shafting structure parameters， electromagnetic parameters and excitation torque is discussed. The coupling calculation of the range-extender shafting is carried out and the comparison analysis with the original machine is made. It is concluded that the addition of the motor rotor system will reduce the natural frequency of shafting by 27.6Hz and the maximum amplitude by about 21%. The resonant speed is shifted forward by about 200 r/min on the basis of the original machine， and a natural frequency is increased in the first 12 steps. The influence of electromagnetic parameters on torsional vibration characteristics of shafting is analyzed according to the particularity of range extender working condition. The results show that the electromagnetic damping is linearly and negatively correlated with torsional vibration amplitude， but it does not change the natural frequency and resonant speed of shafting. The electromagnetic stiffness has no obvious effect on the amplitude of torsional vibration， and mainly affects the size of zero frequency， which will lead to low frequency rolling vibration of shafting.

Transformer-based models have made significant progress in Remaining Useful Life （RUL） prediction. However， existing Transformer models have the following limitation of difficulty in local feature extraction and failure to consider the importance of varying temporal and spatial input features. To solve the problems， in this paper， an enhanced two-stream Transformer model is proposed， which is reinforced by the local feature extraction module and the interaction fusion module. Firstly， the local feature extraction module captures local features from both the temporal and spatial streams to compensate for the Transformer's deficiency in local feature extraction. Then， the two-stream Transformer is used to extract long-term dependencies in the temporal and spatial dimensions， enhancing complementary learning between the two streams. Finally， the interaction fusion module is constructed to capture stream-level interaction using bilinear fusion， further improving prediction performance. Experiments using multiple models on two real-world datasets from a diesel engine manufacturer demonstrate that the evaluation metrics RMSE and Score are reduced by at least 3.23% and 5.89%， respectively.

With the increasingly strict emission standards for automobiles， non-exhaust particulate matter emission， especially brake wear particulate matter emission， is becoming more prominent. In order to investigate the emission characteristics of brake wear particles under different test cycles， in this study a set of drum brakes is selected to conduct research on PM_2.5 and PN₁₀ emission under different test cycles （WLTP-Brake、WLTP、C-WTVC and CHTC-LT cycle） on a brake emission testing system modified based on a brake inertia table. The research results show that there are significant differences in the average initial/final braking temperature of the brake drum under different test cycles， and the highest final braking temperature generally occurs in the braking event corresponding to the maximum initial braking speed in the cycle. The PM_2.5 emission factor of the test brake drum under WLTP-Brake cycle is 1.67 mg/km/wheel （the Euro 7 vehicle emission limit of 7 mg/km/vehicle）， so the particulate matter emission control of the brake drum， like the brake disc， needs our attention. In addition， the initial braking speed of each cycle has a significantly greater impact on brake particle emission than braking characteristic parameters such as average braking deceleration. This study has practical reference value for the development and testing of low emission brakes for brake enterprises.

To enhance the passenger space and safety performance of electric vehicles， Cell to Body （CTB） technology is used to integrate batteries as structural components into the bottom of the vehicle body， which not only reduces the number of body components and connectors， but also helps to achieve the lightweight and range requirements of electric vehicles. Addressing potential collision safety issues and the risk of interrupted force transfer paths associated with the CTB structure， in this paper a frontal collision safety design process for vehicle bodies based on the CTB structure is proposed. The design method of "force decomposition-simulation analysis-test benchmarking" is adopted. Firstly， safety of the battery under frontal collision is ensured. Then， multi-level force transmission path is designed for optimization of vehicle body and the front structure of the vehicle is planned and designed based on the collision force value. The feasibility of the research method in this paper is verified through finite element simulation analysis and experiments， providing an effective design method for future vehicle body design and application.

Based on the research on the combination of vehicle application scenarios and electric drive torque near zero anti-jerk control requirements， in this paper four vehicle anti-jerk control requirements and their software architectures from the perspective of vehicle anti-jerk， including transmission clearance， torque limitation， electric drive torque near zero， and high-frequency de-noising of wheel speed fluctuation. Based on the requirements of anti-jerk control， vehicle anti-jerk algorithm architecture as well as the control strategy with electric motor torque near zero is designed， and the corresponding control process and calculation analysis are provided. Through simulation and real vehicle testing of the designed anti-jerk control strategy， it is proved that the control algorithm architecture and strategy can effectively achieve the anti-jerk function of the vehicle. According to the longitudinal acceleration curve of the real vehicle test and the fluctuation of the electric drive speed， it can be seen that the designed software architecture and control strategy have achieved good results in vehicle drivability.

With the increasing power levels and integration of electric vehicles， the thermal load of power modules is rising rapidly， which puts higher demand on the thermal management technology of power modules. The topology optimization design of power module liquid cooled plates is becoming a key technology for achieving high heat flux density heat dissipation due to its high heat transfer and low-pressure drop loss characteristics. In this paper， based on the density topology method， a topology optimization design model is constructed for the flow channel structure of the power module liquid cooling plate. Through the coupling of multiple physical fields of flow and heat transfer； multi-objective topology optimization design for the flow channel of the liquid cooling plate is carried out. The results show that the topology-optimized liquid cooling plate design presents a multi-level biomimetic flow channel structure， which significantly reduces pressure drop loss and improves heat dissipation capacity. Compared to the traditional finned liquid cooling plate structure of the benchmark， the pressure drop loss of the flow channel structure after topology optimization is reduced by 72.8% ， with a maximum temperature reduction of 33.28 K， which provides a new design idea for high-performance liquid cooling plates of automotive electronic control power modules.

With the aim of improving vehicle ride comfort， in this paper a fractional-order skyhook control strategy based on the fractional order calculus theory is proposed. Firstly， a fractional-order skyhook vehicle suspension dynamic model is established to derive analytical expressions for the fractional-order skyhook damping force and fractional-order skyhook inertial force. Subsequently， particle swarm optimization algorithm is used to optimize the key parameters of the suspension. In order to solve the problem that fractional-order force cannot be realized physically， a vehicle ISD （inerter-spring-damper） suspension with mechatronic inerter is chosen as the controlled model. A model reference adaptive controller based on fractional-order skyhook is designed to track the mechanical performance output of the fractional-order suspension. Dynamic performance analyses of fractional-order skyhook inerter suspension and fractional-order skyhook damper suspension is conducted from both frequency-domain and time-domain perspectives. Simulation results show that fractional-order skyhook ISD suspension has more significant advantages in reinforcing ride comfort than integer-order skyhook ISD suspension. Under random road input， the root-mean-square value of vehicle body acceleration of fractional-order skyhook damper suspension decreases by 18.3%， while the fractional-order skyhook ISD suspension decreases by 20.6%. The bench test results demonstrate that the vehicle ISD suspension based on fractional-order skyhook further enhances ride comfort， offering new insights for the design of vehicle ISD suspension.

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 established 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 significantly 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 between fiber direction and joint strength， while overlap length and aluminum substrate thickness show a positive correlation 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 evaluation 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， 1 500， and 2 000 MPa. The results show that the weldability current range and mechanical properties of welds for all three grades of PHS meet industrial 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.