The wirecontrolled braking system has gradually replaced the traditional vacuum booster solution and has become the leading technology in the braking field of new energy vehicles. Among them, the integrated ElectroHydraulic Braking (EHB) system, as a form of wirecontrolled braking, relies mainly on basic hydraulic braking and motor regenerative braking to fulfill the driver's braking intention when its EHB module fails. These two braking methods can provide relatively limited braking power, which is difficult to achieve the deceleration effect expected by the driver, to some extent increasing the risk of traffic accidents. In order to comprehensively enhance the driving safety performance of the vehicle, in this paper the Electronic Parking Brake (EPB) system is incorporated as one of the executing mechanisms for driving brakes. When the power assist function of the integrated EHB system fails, the intelligent braking system can, based on the deceleration requested by the driver, send a braking force or deceleration request signal through the vehicle network communication. This process coordinates the motor regenerative braking and EPB braking to work together to enhance the vehicle's deceleration performance, thereby significantly improving braking efficiency. In addition, by implementing multilevel control strategies for the EPB system, the system can meet the needs for different levels of deceleration, which not only optimizes the driving experience and improves comfort but also effectively reduces the probability of traffic accidents.

In order to meet the requirements of large output force value, high working frequency and good linearity of forcedisplacement of electromagnetic actuator for active mounting, a multiobjective parameter hierarchical optimization method is proposed to solve the problems of different influence of different structural parameters on optimization objectives, difficulty of expression of dynamic electromagnetic force by analytical formula, and difficulty of realization of optimal characteristics at the same time of the output force value, working frequency and forcedisplacement. In the upper layer, Taguchi algorithm is used to preliminarily optimize parameters, screen sensitive parameters and update the optimization range of high sensitivity parameters. In the lower layer, the backpropagation (BP) neural network prediction model is used to characterize the dynamic electromagnetic force, and the multiobjective genetic algorithm (NSGAII) is used to search and optimize the dynamic electromagnetic force. Through simulation and experiments, the results show that the parameters of electromagnetic actuator obtained by the optimization method in this paper have better comprehensive performance, which verifies the effectiveness of this method.

The development of lightweight car body technology has led to widespread usage of highstrength steel in automotive industry, which brings new challenges to the resistance spot welding (RSW) process in car body welding and manufacturing. The occurrence of common abnormal working conditions, such as electrode axis off normal (ON) can negatively impact the consistency of RSW process. In this paper, the influence mechanism of ON condition on spot welding process is revealed by comparing multisensor process signals, weld surface morphology, nugget size and joint formation process under standard and ON conditions. The results indicate that compared with the standard condition, the ON condition increases the initial contact area of the sheetsheet interface, which leads to a slower temperature rise of sheets, a later peak in resistance signal and a delayed nucleation time. In the welding process, the contact area of sheetsheet and electrodesheet interface increases, which leads to the decrease of dynamic resistance signal and heat generation, so the nugget size and electrode displacement signal are smaller than the standard condition. Furthermore, the larger contact area along the length direction leads to more heat generation, ultimately resulting in a larger nugget dimension and indentation size in this particular direction. This study can provide theoretical support for the optimization of highstrength steel resistance spot welding process in actual production environment and online quality monitoring of spot welding under complex working conditions.

The accurate acquisition of tire body deformation has a crucial influence on the simulation accuracy of theoretical model, so the deformation rules and expression accuracy of different cord are studied by beam body model and finite element model. Firstly, a detailed theoretical model considering the flexible deformation characteristics of the beam carcass is established, and the expressions of tire cornering stiffness and driving/braking stiffness are obtained. Secondly, the tire finite element model is established, and the tire rubber and cord material parameters are accurately obtained to complete the comparison between the simulation results and the test data. On this basis, the finite element model of smooth tire with isotropic tread stiffness distribution is established, and the lateral stiffness, torsional stiffness and steadystate glide stiffness are simulated to obtain the lateral deformation of the tire under the action of lateral force and aligning moment, and the superposition principle of lateral deformation of different cord lines is verified. Then, the lateral deformation of different cord lines is fitted according to the established beam matrix model. Finally, the tread stiffness obtained by different cord lines is compared and verified by combining the flexural stiffness and slip stiffness models. The results show that the principle of deformation superposition is satisfied for different tire cord. The beam matrix model has a better expression precision for the lateral deformation of cord. The bending stiffness of cord shows a nonlinear decreasing trend with the increase of load, and the difference is small under large load. The calculation accuracy of tread stiffness obtained by different cord positions is different. The calculation accuracy of crown cord is the lowest at 93.6%, and the calculation accuracy of body 2 cord is the highest at 97.3%. The research position of the beam body model in the theoretical model is clarified in the study, improving the simulation accuracy of the theoretical model, and providing the reference for the study of tire dynamics.

For the problem of tire force estimation deviation caused by the change of mechanical properties due to temperature rise during the rolling process, the vertical force estimation correction algorithm of heavyduty tires based on thermalmechanical coupling is studied in this paper. A variable temperature mechanical tensile test is carried out to obtain the mechanical parameters of the tire shoulder rubber with temperature change, and a heavyduty tire thermalmechanical coupling model is established. The ground loading test and modal test are carried out to verify the accuracy of the model. The grounding characteristics and mechanical characteristics of heavyduty tires under the action of variable temperature vertical force are discussed, and the sensitive characteristics of the grounding parameters of the vertical force are analyzed, with the sensitive signal offset caused by the temperature rise during rolling corrected. A heavyduty tire vertical force estimation model based on the Gaussian regression process is established and the vertical force estimation accuracy before and after temperature correction is compared. The results show that when the sensitive characteristic value after temperature correction is used as input, the maximum error of the model under vertical force loading of 10~80 kN is 3.45%, with good vertical force estimation effect, and an improvement of the estimation accuracy by 9.17% compared with that before temperature correction.

The Active Sound Enhancement (ASE) system in electric vehicles plays a crucial role in constructing diverse sound features and enhancing driving control perception. For the ASE technology for electric vehicles, a variableweight multimodal switching sound synthesis algorithm is proposed in this paper. By constructing a modeswitching factor matrix, it organically combines order synthesis, pitch modulation synthesis, and particle synthesis methods to form a deep sound fusion ASE system to achieve realtime synthesis of multimodal incabin sound profiles aimed at enriching subjective auditory perception, increasing the richness of the ASE system, and making the synthesized sound more threedimensional and saturated, thus enhancing the driving experience. Subsequently, a sound modulation software for electric vehicles is developed using C#, integrating ASE system control and sound modulation function to realize quick and flexible modulation of vehicle sound. Finally, the application of the sound modulation software in the sound modulation of a specific pure electric SUV is demonstrated. Sound tests combined with subjective evaluation results indicate that this software can effectively achieve multimodal sound synthesis goals, offering practical engineering application value.

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 high-way 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.