With the rapid development of the electronic and electrical architecture of intelligent and connected vehicles， the demand for real-time reliability in in-vehicle communication networks has significantly increased. In this context， Time-Sensitive Networking （TSN） has become a critical technology to meet the demand. In this paper， the implementation of the IEEE 802.1CB protocol in vehicular networks is realized， filling the gap in current research regarding the combined use of link redundancy transmission and routing planning. An innovative multi-path routing strategy is proposed which balances network efficiency and reliability through dual-path transmission involving both primary and redundant paths. The core contribution of this study includes： （1） a novel NSGA2-based primary path routing algorithm， which achieves the dual objectives of load balancing and low latency through intelligent path planning， and （2） an improved Dijkstra-based redundant path routing algorithm， which ensures high-reliability transmission for information flows with varying priority levels. Finally， a hardware-software integrated experimental framework is proposed， demonstrating that the proposed algorithms outperform existing comparison algorithms by 18.19% to 62.29% in terms of load balancing and end-to-end latency， while also enhancing network reliability by 19.18% to 42.87%.

In unstructured scenes， there are often obstacles of various sizes， and the path planning process that only considers obstacle avoidance methods such as detours will lead to decrease in vehicle traffic efficiency. For these problems， in this paper an intelligent vehicle path planning method with multiple obstacle-avoidance modes is proposed by integrating a layered collision detection strategy into the traditional Hybrid A* algorithm. Firstly， a double-layer grid map is constructed based on the vehicle chassis height， and a layered collision detection strategy is designed using the body contour and four-wheel contour. Then， through a well-designed heuristic function and cost function calculation method， the Hybrid A* algorithm can efficiently search for paths in multi obstacle scenes. Finally， the gradient descent method is used to smooth and optimize the path. Simulation and real vehicle experiment results demonstrate the effectiveness of the proposed algorithm in improving path search efficiency and significantly enhancing path smoothness. Moreover， the planned paths consider both crossing and bypassing strategies for obstacle avoidance， enabling vehicles to have better passability in multi-obstacle scenarios.

In the field of intelligent connected vehicles， the recognition accuracy of in-car systems for non-command voice input in complex environment （the proportion of correct voice input recognition by the system） is of great significance. To address this challenge， in this paper a multimodal rejection model is proposed. The model is based on the open-source ChatGLM2-6B large language model and has undergone exclusive rejection dataset construction and model fine-tuning for the in-vehicle interaction scenario. The rejection dataset is collected from real driving scenarios， integrating voice information with the driver's facial orientation， gestures， and emotion， and other non-verbal signals to provide richer interaction information， effectively overcoming the limitation of pure language recognition mechanisms in complex environment. Through experiments， it is found that the multimodal rejection model shows higher recognition accuracy （ACC） and lower false rejection rate （FRR） on the test set compared to the pure language rejection model.

Commercial platoon cruise control is an effective method to improve transportation efficiency， but existing research is mostly based on homogeneous platoon control with one single vehicle following optimization objective， while using a simple architecture to cope with communication time delay， which is not universally applicable in practical scenarios. Therefore， based on heterogeneous electric commercial vehicle fleets， in this paper a distributed model predictive control strategy is proposed to achieve multi-objective control that takes into account of requirements of vehicle following， economy， and comfort. Delay buffers and compensators are designed for delay prediction models， effectively solving the problems of excessive tracking distance error caused by non-ideal communication conditions. Matlab/Simulink simulation shows that the proposed control algorithm can achieve multi-objective optimization control of heterogeneous commercial vehicle fleets. Compared with traditional model predictive control （MPC）， it significantly reduces the tracking distance error， energy consumption， and jerk， effectively improving performance of the platoon in terms of tracking， economy and comfort and significantly reducing the adverse effect of time delay.

Traditional 3D object detection methods in Cartesian coordinate systems often overlook the symmetry and continuity of the target from different perspectives to some extent during camera image encoding due to the fixed wedge-shaped imaging geometry of in-vehicle cameras. To address this， in this paper， PolarDet， an innovative end-to-end 3D object detection method in polar coordinates based on position and semantic information weighting is proposed. This method generates BEV （Bird's Eye View） position and semantic information in polar coordinates through polar coordinate queries and predefined polar grid， which then interacts with the BEV information from the previous frame to incorporate temporal information. When outputting the final detection results， PolarDet performs a weighted sum of position and semantic information to enhance information utilization efficiency， allowing the network to achieve higher detection accuracy. Extensive experiments on the challenging BEV object detection nuScenes dataset show that the optimal model of PolarDet achieves a mAP （mean average precision） of 0.469 and an NDS （nuScenes detection score） of 0.56， significantly outperforming Cartesian coordinate-based BEV detection methods.

In order to achieve customizable design and high-fidelity intelligent driving simulation test perception data generation， an intelligent driving test scenario simulation architecture that integrates virtual and real perception data is established in this paper. By fusing simulated traffic subject perception data with real environment scene data， perception simulation data can be continuously generated with dangerous test scenarios as the target. On this basis， the RANSAC method is used to extract the position of obstacles in the real point cloud and determine the operating space constraints of simulated traffic subjects in the real environment scene at each moment. Then， in order to realize the interactive relationship between the behavior and position of the main vehicle and other traffic subjects in the test scenario， in the simulation software， simulation modeling and behavior design of the main vehicle and traffic subjects are conducted based on the real main vehicle sensor parameters and motion trajectories for output of continuous simulated traffic participant perception data. Finally， the mask replacement method and ray replacement strategy are used to perform virtual and real fusion on the image and point cloud data respectively， and the virtual and real fusion perception data of dangerous driving test scenes in different real environment scenarios are obtained. The simulation data is tested and verified. The results show that most scenarios in the real road collection data set have the ability to support simulation data injection. The injected simulated traffic subject behaviors can match the test scene requirements and have high authenticity. At the perceptual level， the injected simulated traffic subject and the real traffic subject have a similarity of 86.5% in the target detection algorithm confidence level. The proposed method can controllably inject simulated traffic subjects that meet test requirements into real environment scene data， and quickly and synchronously obtain virtual-real fusion images and point cloud data with high realism.

Given the high exposure and risk of rainfall as a trigger condition for visual perception systems， various rainfall simulation tests are the main research methods. However， the realism of rain simulation of different testing methods impacts the confidence in test conclusions. In this study indicators are selected to quantify the impact of rainfall on machine vision from the aspects of image quality and object detection. Using the numerical range and trend of index changes under real rainfall as a benchmark， the comparative study of the realism of different rainfall simulation methods in the dimension of machine vision is carried out. Additionally， in this study 1 950 images of no rain and various levels of real rainfall are collected to construct a dataset， so as to obtain statistical patterns of the impact of real rainfall on machine vision. Two simulated rainfall test sites， three simulation software， and one generative model are selected for rainfall simulation tests to compare and analyze the realism of different types of rainfall simulation methods horizontally. The results show that， in terms of image quality， simulation software and rainfall simulation equipment can better simulate the real rain in terms of DR value range and trend. Regarding target detection， simulation software and generative model are closer to real rainfall in terms of CC change values. Overall， in terms of realism， digital simulation of rainfall performs best， followed by physical rainfall simulation on site and generative model， providing a reference for testing the SOTIF of the visual perception system of intelligent and connected vehicles.

The vehicle stability region is an important aspect of research on vehicle stability analysis and control. For the problems of inaccurate description and difficult solution of stability region in existing research， a quadrilateral description and automatic solution method for vehicle stability region is proposed. A nonlinear two-degree-of-freedom vehicle model is established， and the ant colony algorithm is used to solve the equilibrium state of vehicle system. The Lyapunov indirect method is applied to determine the stability of the equilibrium state. Based on the phase plane of the sideslip angle-sideslip angle velocity of mass center， several phase trajectory feature points and the phase plane stability region boundary point search method are established to solve the stability region boundary points. According to the different distributions of the vehicle stability region， two types of stability regions are proposed， and corresponding judgment methods， stability region quadrilateral description and its automatic solution methods are established. Based on the proposed method， the stability region of vehicle under common medium speed driving condition is solved. The results are compared with the parallel line method and diamond method， and the correctness of the quadrilateral description is validated by CarSim sine wave simulation results. The results show that the proposed quadrilateral description of the vehicle stability region can better describe the boundary of the stability region than the parallel line method and diamond method， and automatic solution reduces the workload of stability region solution.

To improve the handling stability of distributed drive electric vehicles （EVs） at high speeds on different road surfaces， in this paper an integrated control strategy for AFS/DYC based on hybrid model predictive control is proposed. Firstly， a piece affine tire model is constructed based on system identification methods. In conjunction with the vehicle dynamics model and the conversion relationship between propositional logic and linear inequalities， the vehicle system’s mixed logical dynamic model is constructed. Then， an integrated control strategy for AFS/DYC based on hybrid model predictive control is designed. The strategy uses mixed integer quadratic programming to track target reference values for decision-making on additional yaw moment and additional steering angle， and constructs an optimized wheel driving torque distribution control strategy with the goal of minimizing tire load rate. Finally， a driver-in-loop handling stability test experiment is conducted on the CarSim-Simulink co-simulation platform. The test results show that compared to the traditional model predictive control， the designed hybrid model predictive control strategy reduces the root mean square error of yaw rate and side slip angle by 31.61% and 19.51% respectively under high-speed double lane change conditions and the peak average torque amplitude of the four wheels is reduced by 24.27%.

With the rapid development of automotive active safety technology， the chassis electronic control unit of modern electric vehicles has seen explosive growth. In order to improve the real-time performance and accuracy of chassis active safety control， for the rapid growth of chassis electronic control units and the coupling conflict problems of low integration degree of control system and multi-objective co-optimization， in this paper firstly a chassis system integration control architecture based on multi-agent is established， and a hierarchical control system integrating the front and rear wheels' active steering system and the differential braking control system is proposed. Secondly， based on this， the state equations of each agent and its contribution to the vehicle's center of mass model are established and combined with the model predictive control to consider the characteristics of constraints. The cost function containing global state tracking error and local control effort is designed considering both the actuator constraints and the ground friction ellipse constraints. Finally， each agent realizes its collaborative control through the interaction of dynamic information of its respective contribution. The results show that the vehicle stability control method based on multi-agent model prediction proposed in this paper has obvious improvement in terms of traverse stability compared with independent control of each active safety unit under the driving conditions of high and low road attachment and large curvature curves， which has certain value for engineering application.

The torque distribution strategy plays a crucial role in improving the safety and energy efficiency of distributed drive electric vehicles. In order to reduce the energy consumption of electric vehicles with dual-motor drive on the front and rear axles， a multi-objective torque distribution method based on a hierarchical control architecture is proposed in this paper， that comprehensively considers vehicle safety， handling stability， and energy efficiency. The upper layer is the active safety layer， which uses nonlinear model predictive control （NMPC） to achieve vehicle safety and stability control. The lower layer is the torque distribution layer， which considers the torque control of the front and rear axle motors under no-load loss of the motor. The simulation results show that compared with the average distribution method， the proposed multi-objective torque distribution method can improve the vehicle's stability while ensuring safe driving， with the total energy consumption reduced by 6.6% and 3.5% under the NEDC and WLTC driving cycles， respectively.

To solve the problem that a single-stage reduction hub drive system cannot meet the performance specifications of specialized vehicles， and that a two-stage reduction would require the addition of extra control mechanisms， a novel coupled dual motor hub drive system scheme which is composed of two motors， a planetary gear mechanism， and a one-way clutch is proposed in this paper. The system is designed to operate in two coupling modes： torque coupling mode at low speed and speed coupling mode at medium and high speed with an autonomous mode switching capability as a functional requirement. Parameter matching is conducted with the objective of maximizing power utilization rate， and a control strategy for autonomous mode switching is developed. Under the same simulation initial condition， compared to the single motor two speed hub drive system， the coupled dual motor hub drive system exhibits an 81.25% reduction in maximum vehicle speed fluctuation and an 81.58% decrease in maximum acceleration during the mode switching process. An instantaneous optimal control strategy is established. Under the same operating conditions， the coupled dual motor hub drive system demonstrates a 21.42% reduction in energy consumption compared to the single motor two-speed hub drive system. Experimental tests are conducted using a prototype model to further validate the functionality and feasibility of the novel coupled dual motor hub drive system.

In this paper， an adaptive wheelbase preview robust H_∞ control method is proposed based on vibration based road roughness recognition to address the impact of unknown road surface input on the control effect of active suspension. By collecting the vibration acceleration response of the wheels through real vehicle experiments， the longitudinal road surface roughness information is identified based on the vibration based road surface roughness detection method of the front wheels. A speed adaptive wheelbase preview method is designed to obtain the delay relationship of the road surface excitation received by the front and rear wheels of the vehicle， providing real vehicle data for the wheelbase preview control of the rear wheel suspension. On this basis， a multi-objective speed adaptive wheelbase preview robust H_∞ control method considering motion constraints is designed， and the optimal solution of parameters in linear matrix inequality （LMI） is achieved through multi-objective genetic algorithm （MOGA） to improve control accuracy. The experimental and simulation results show that the method proposed in this paper can accurately identify road roughness information and effectively improve suspension performance indicators and vehicle vibration frequency， effectively suppress vibration within the frequency range sensitive to motion sickness， and balance passenger driving experience while meeting driving smoothness requirements. Meanwhile， this method also provides a new approach for vertical vibration control of multi axle vehicles.

The wire-controlled 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 Electro-Hydraulic Braking （EHB） system， as a form of wire-controlled 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 multi-level 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 force-displacement of electromagnetic actuator for active mounting， a multi-objective 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 force-displacement. 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 multi-objective genetic algorithm （NSGA-II） 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 high-strength 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 multi-sensor 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 sheet-sheet 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 sheet-sheet and electrode-sheet 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 high-strength 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 steady-state 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 heavy-duty tires based on thermal-mechanical 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 heavy-duty tire thermal-mechanical 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 heavy-duty 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 heavy-duty 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 variable-weight multimodal switching sound synthesis algorithm is proposed in this paper. By constructing a mode-switching factor matrix， it organically combines order synthesis， pitch modulation synthesis， and particle synthesis methods to form a deep sound fusion ASE system to achieve real-time synthesis of multimodal in-cabin sound profiles aimed at enriching subjective auditory perception， increasing the richness of the ASE system， and making the synthesized sound more three-dimensional 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.