In this paper a simulation testing method for intelligent vehicle based on a large language model is proposed to address the issues of heavy reliance on human resources and prominent efficiency bottlenecks in existing scenario based testing methods. Firstly, a simulation testing architecture for intelligent vehicle based on a large language model is designed, and corresponding data and simulation layers are established. On this basis, an intelligent car simulation testing process based on a large language model is constructed. Knowledge mining, model finetuning, and knowledge base enhancement retrieval application processes are designed for knowledge question answering tasks. Application paths for scenario type analysis, scenario element generation, and scenario toolchain invocation are designed for scenario generation tasks. For testing and evaluation tasks, a comprehensive application framework for testing scenario analysis, evaluation system construction, and simulation testing execution is designed. Finally, each task is tested. The results show that the testing method proposed in this paper can effectively solve different types of testing tasks and improve testing efficiency.

The dualvehicle cooperative transportation system consists of a cargo module and two transport vehicles, which are connected by articulated joints. The system's strong dynamics coupling and nonlinearity present significant challenges for accurate modeling and precise control. In this paper a trajectory tracking control scheme for the cooperative transportation system based on constraintfollowing theory is proposed. In terms of system modeling, based on the kinematics and rigid body dynamics analysis, external trajectory tracking servo constraints and internal articulated passive constraints are constructed for the cooperative transportation system. The Lagrange modeling method is then employed to establish a nonlinear constrained dynamic model of the dualvehicle cooperative transportation system. In terms of controller design, the UdwadiaKalaba (UK) method is first used to obtain the normminimal force required for the cargo to satisfy the trajectory tracking servo constraints, that is, the combined force acting on the cargo at the articulation point. Next, based on the minimum lateral forces principle of front and rear vehicles, an optimal allocation strategy for this combined force is designed, distributing it to the front and rear transport vehicles. The reaction forces of the distributed force components are modeled as the known external disturbances acting on the front and rear vehicles. Then, based on the feedforward compensation for the known external disturbances and the constraintfollowing control theory, the control forces required for the front and rear transport vehicles to satisfy the trajectory tracking servo constraints are designed. Finally, the simulation results show that the proposed cooperative control scheme achieves good trajectory tracking performance and significantly suppresses the lateral dynamic impact of the cargo on the transport vehicles, effectively enhancing the overall lateral stability of the cooperative transportation system.

Threedimensional terrain scenes typically possess complex and diverse environment along with jagged terrain features, which poses challenges to path planning. To address this issue, in this paper a highly reliable path planning approach under the influence of nontopographic fluid characteristics and threedimensional complex terrain is proposed. This method encompasses initial global path planning, path inspection, and replanning under 3D terrain. For the initial global path, an AHTR algorithm that combines the advantages of Hybrid A* and Theta* is proposed. This algorithm enhances the internode sampling and detection methods in accordance with the traits of the 3D terrain scene and introduces in a terrain risk assessment function.to plan a path for the vehicle that can evade rough terrain and comply with kinematic constraints. For path inspection, the path risk test function is designed based on the results of vehicle dynamics analysis considering the characteristics of nonterrestrial fluid, and the impact of nonterrestrial fluid characteristics on path planning is verified. For path replanning, an enhanced AHTR algorithm is proposed, which takes into account of both 3D terrain features and nonterrain fluid features to guarantee that the planned path can effectively avoid risks. Simulation experiments demonstrate that compared with Hybrid A* and Theta*, the intensity of ground undulation in the path planned by the AHTR algorithm is decreased by 26.54% and 49.04%, with the average pitch angle of the vehicle reduced by 44.39% and 69.40%, the path risk lowered by 26.32% and 41.67%, and the final path safety improved by 58.06% and 88.46%, which effectively ensures path reliability.

For the problems of inaccurate speed prediction and poor SOC adaptability under the traditional model predictive control, the plugin hybrid electric vehicle (PHEV) is taken as the research object, and the speed prediction model based on computer vision is combined with the deep deterministic policy gradient (DDPG) algorithm to achieve the realtime state of charge (SOC) reference trajectory planning and optimal power allocation control of PHEV. A SOC reference trajectory planning model based on the enhanced DDPG is constructed, and a speed prediction model based on computer vision with cascaded long shortterm memory network is constructed, based on which the optimal controller based on the model predictive control is used to achieve the accurate tracking of the SOC reference trajectory and power optimization. The results show that compared to the traditional DDPG, the strategy proposed in this paper increases the overall vehicle economy by 5.66%, reaching 97.93% of the global optimal algorithm. It also improves the overall vehicle economy by 2.92% compared to the energy management strategy without computer vision.

Instance segmentation algorithms based on deep learning are capable of helping intelligent vehicles to obtain accurate perception information. However, due to the limitation of manufacturing cost, the computing resources on intelligent vehicles are usually limited. In order to obtain highprecision recognition and segmentation under limited computing resources, the algorithm itself is required to make full use of the extracted features. Meanwhile, although the onestage instance segmentation algorithm has a relative fast inference speed, it has poor performance in accuracy. To this end, structural improvement based on the onestage instance segmentation algorithm SparseInst is conducted to enhance the model's utilization of effective features. Specifically, firstly, residual connection is added inside the basic building block of the backbone. Secondly, a threescale feature fusion module is designed to overcome the problem of indirect interaction of crossscale features in the encoder. A decoupled instance activation module is designed to enhance the model's ability to learn instance features. In addition, the improved algorithm makes full use of detail features to refine the mask features to improve the quality of the generated masks. Finally, the kernel is used to initialize the score of the target object, which improves the utilization rate of the extracted features. The improved algorithm surpasses similar algorithms in mask accuracy on multiple datasets and has strong realtime performance. To further verify the effectiveness of the improved algorithm, experiments using data

Tire matching selection is an important part of the vehicle development process. Currently, the mainstream subjective evaluation methods have problems such as poor consistency between enterprises, shortage of driver resources, and lagging evaluation nodes. In this paper, based on the subjective and objective test data of real vehicles, considering the inherent correlation between tire mechanical performance and structural parameters of vehicle handling stability, by extraction of subjective evaluation influencing factors and generation of the dimensionality reduction feature space based on correlation analysis, a subjective and objective fusion index system is established. Further, with the objective indicators and subjective rating prior trend relationship as the constraint penalty term, a tire and vehicle handling stability matching evaluation model based on subjective and objective fusion is constructed by designing an ensemble learning algorithm. The mean MSE on multi region test data is 0.247, and the predicted results show good consistency with the test results. The relevant achievements can build a quantitative evaluation system for the subjective and objective consistency of vehicle handling stability, providing support for precise selection of tire matching.

For the high energy consumption problem caused by the large torque fluctuations of inwheel motordriven vehicles on uneven roads and frequent shifting of vehicles, in this paper a method for energy consumption optimization of inwheel motordriven vehicles considering torque fluctuations is proposed. Firstly, based on the longitudinal drive dynamics equation of electric vehicles and the CarSim vehicle dynamics model, the motor energy consumption model, tire slip energy consumption model, and yaw torque tracking error model are established as the upper control target, and the inwheel motor dynamics model considering road surface excitation is established as the lower control target. Secondly, taking the upperlevel control target as the objective function of the torque optimization of the inwheel motor vehicle, the motor torque and speed energy limit as the inequality constraints, and the fuzzy control method for objective function weight distribution, the upperlevel torque optimization model is established based on NSGA II. At the same time, the lowerlevel inwheel motor vector control model of torque overshoot and delay caused by road surface excitation is established based on the sliding mode antidisturbance observer. Finally, the joint simulation of Simulink and CarSim of a fourwheel inwheel motordriven car is carried out, and the changes of the front and rear axle wheel torque, total energy consumption of the car, and the SOC of the car battery under different optimization methods are analyzed under WLTC operating conditions and CLTCP operating conditions. The inwheel motor bench test shows that the energy consumption optimization method of inwheel motordriven vehicles considering torque fluctuations can effectively reduce energy consumption under WLTC and CLTC-P operating conditions.

To investigate the impact of variation in tire tread depth on vehicle performance, performance tests are conducted using tires with differing tread depth as our subjects on dry, wet, and lowadhesion road surface at the proving ground. The results indicate that the braking distance decreases as tread depth diminishes on dry surface while the trend reverses compared to dry surface on wet surface, and tread depth variation has a minor impact on braking distance on lowadhesion surface. The maximum lateral acceleration and yaw rate of the vehicle initially increase and then decrease with reducing tread depth on dry surface, peaking at 4.0 mm. The maximum yaw rate follows a similar pattern to dry surface on wet surface, but there is no clear trend in maximum lateral acceleration.

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.

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 decisionmaking 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 driverinloop handling stability test experiment is conducted on the CarSimSimulink cosimulation 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 highspeed double lane change conditions and the peak average torque amplitude of the four wheels is reduced by 24.27%.