In order to achieve customizable design and highfidelity 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 virtualreal fusion images and point cloud data with high realism.

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.

With the rapid development of the electronic and electrical architecture of intelligent and connected vehicles, the demand for realtime reliability in invehicle communication networks has significantly increased. In this context, TimeSensitive 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 multipath routing strategy is proposed which balances network efficiency and reliability through dualpath transmission involving both primary and redundant paths. The core contribution of this study includes: (1) a novel NSGA2based primary path routing algorithm, which achieves the dual objectives of load balancing and low latency through intelligent path planning, and (2) an improved Dijkstrabased redundant path routing algorithm, which ensures highreliability transmission for information flows with varying priority levels. Finally, a hardwaresoftware 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 endtoend latency, while also enhancing network reliability by 19.18% to 42.87%.

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 twodegreeoffreedom 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 anglesideslip 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.

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 realtime 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 multiobjective cooptimization, in this paper firstly a chassis system integration control architecture based on multiagent 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 multiagent 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.

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.

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 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.

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 dualmotor drive on the front and rear axles, a multiobjective 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 noload loss of the motor. The simulation results show that compared with the average distribution method, the proposed multiobjective 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.

In the field of intelligent connected vehicles, the recognition accuracy of incar systems for noncommand 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 opensource ChatGLM26B large language model and has undergone exclusive rejection dataset construction and model finetuning for the invehicle 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 nonverbal 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.