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 fine-tuning， 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.

In the application of vehicle-road cooperative technology for dynamic display of the roadside twin maps， due to the delay problem of the communication between networked devices and the existence of the roadside perception error， the fusion perception accuracy of the roadside edge computing unit will be seriously affected， which will lead to the jitter and delay of the vehicle display track in the twin map. Hence， in this paper a vehicle-road cooperative sensing and localization method that fuses high definition map under high communication delay is proposed. The method first analyzes and models the communication delay between the vehicle end of the connected vehicle and the roadside edge processing unit in the vehicle-road cooperative system， divides the delay model into sensor synchronization delay and communication transmission delay， and proposes a synchronization optimization method for the delay. After the synchronization optimization， a collaborative multidimensional particle filter algorithm for swarm vehicles is proposed， where the states of the particles represent the pose of different connected vehicles and non-connected vehicles in the swarm vehicles. In the proposed multidimensional particle filter algorithm， the state of the particles is firstly updated using the observation of the state of the particles by utilizing the roadside RSU observation data and the curvature information of the lanes in the high-definition map. Then the self-localization information of the received delayed synchronized smart connected cars combined with the left and right lane line lateral constraint information and the lane line equations of the lanes in the high definition map are used to update the observation of the state portion of the particle that represents the smart connected cars. The experimental results show that the perceptual and localization accuracy of the edge server is improved by 59.4% in the low delay scenario with less communication interference， and its accuracy is improved by 38.6% in the high delay scenario with severe communication interference. Therefore， the proposed vehicle-road cooperative sensing method incorporating high definition map under high communication delay can effectively deal with the communication delay problem and improve the multi-vehicle perception accuracy of the edge computing unit， thus improving the accuracy， stability and continuity of the twin map dynamic data.

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 high-precision recognition and segmentation under limited computing resources， the algorithm itself is required to make full use of the extracted features. Meanwhile， although the one-stage instance segmentation algorithm has a relative fast inference speed， it has poor performance in accuracy. To this end， structural improvement based on the one-stage 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 three-scale feature fusion module is designed to overcome the problem of indirect interaction of cross-scale 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 real-time performance. To further verify the effectiveness of the improved algorithm， experiments using data collected from a real vehicle platform are conducted. When the input image resolution is 640×480， the model inference speed reaches 54 FPS， and the instance mask is segmented accurately.

For the problems of inaccurate speed prediction and poor SOC adaptability under the traditional model predictive control， the plug-in 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 real-time 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 short-term 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.

Multi-class traffic participant detection in dense traffic scenarios remains a challenging visual task， which is crucial for traffic management and safety. To address this， a deep neural network-based detection algorithm， DSODet， is proposed to handle the challenges of partial occlusion and small-scale targets in dense traffic environment. Firstly， a lightweight CSPDarkNet network is used to extract features from traffic images. Then， a multi-scale feature fusion upsampling module is designed to enhance the representation capability for hard-to-detect targets. Next， a high-resolution detection branch is incorporated to improve detection accuracy for small-scale targets. Finally， a histogram feature distillation training method is proposed， which effectively guides the student model's training by minimizing the intersection ratio of feature histograms between the teacher and student models at corresponding layers， thus enabling parameter optimization and model compression. The experimental results show that DSODet achieves an average detection accuracy of 66.9% for traffic participants and 13.0% for small targets with partial occlusion， outperforming current state-of-the-art algorithms. The model contains only 2.9 M parameters， demonstrating its friendliness for edge device. The related code will be shared at https：//github.com/XMUT-Vsion-Lab.

Unstructured road often has uneven surface and varying obstacle sizes. Neglecting the uneven terrain and handling obstacles improperly can lead to an imbalance between vehicle safety and travel efficiency. To cope with this challenge， in this paper a trajectory planning method that considers complex terrain and obstacle scales （TOTP） for unstructured road is proposed. Firstly， the trajectory-planning framework for unstructured road is established based on vehicle passability analysis， to determine the optimal travel pattern. Then， an operational risk field is established based on road roughness and obstacle’s size information. In addition， considering both operational risk and travel efficiency， an obstacle avoidance path planning method based on dynamic programming and an obstacle crossing path planning method based on improved A* are proposed. Furthermore， based on vehicle stability analysis， a speed planning method considering terrain constraints is proposed. Finally， real-world experiments are conducted， and the experimental results show that under unstructured road conditions， the trajectory planning method proposed in this paper increases the average vehicle speed by 15.8%， with the average absolute pitch angle and average absolute roll angle reduced by 68.1% and 73.6% respectively. This method can effectively coordinate the safety and efficiency of vehicle operation， demonstrating good generalization and meeting the requirements of real-time performance.

The dual-vehicle 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 constraint-following 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 dual-vehicle cooperative transportation system. In terms of controller design， the Udwadia-Kalaba （U-K） method is first used to obtain the norm-minimal 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 constraint-following 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.

To improve the accuracy and stability of path tracking for light commercial vehicles under complex curvature conditions， in this paper a Predictive-Pure Pursuit （P-PP） control method is proposed. Firstly， a P-PP controller is designed based on the vehicle's discrete kinematic model， and a PID compensator is developed based on heading error to enhance tracking accuracy and stability. Secondly， to address the challenge of maintaining both accuracy and stability under complex curvature conditions with a fixed prediction horizon algorithm， a variable prediction horizon optimization algorithm is proposed. A cost function based on the lateral and curvature errors within the prediction horizon is established， and Bayesian optimization is used to determine the optimal prediction horizon， resolving the conflict between accuracy and stability. Finally， TruckSim/Simulink co-simulation and real vehicle tests are conducted. In the real vehicle tests， the root mean square values of the lateral error， heading error， and steering wheel angle for the Bayesian-optimized P-PP controller is 0.113 m， 0.045 rad， and 153.2°， respectively， all of which are superior to the corresponding metrics of the P-PP controller based on fuzzy control and the MPC controller， indicating that the proposed controller maintains good precision and stability under complex curvature conditions.

Three-dimensional 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 non-topographic fluid characteristics and three-dimensional complex terrain is proposed. This method encompasses initial global path planning， path inspection， and re-planning 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 inter-node 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 non-terrestrial fluid， and the impact of non-terrestrial fluid characteristics on path planning is verified. For path re-planning， an enhanced AHTR algorithm is proposed， which takes into account of both 3D terrain features and non-terrain 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.

Injection overmolding after compression for thermoplastic composites balances the low cost and high performance. It can quickly and stably achieve integration of continuous/discontinuous fiber-reinforced composites， meeting the requirements of the automotive industry. This paper reviews the key points of equipment selection， process control and molding simulation， introduces the types of domestic materials， summarizes the optimization design methods for anisotropic bi-material structures， generalizes the evaluation methods for interface and overall performance， and puts forward some critical scientific and technological issues in the integrated design of "material - process - structure - performance".

For the high energy consumption problem caused by the large torque fluctuations of in-wheel motor-driven vehicles on uneven roads and frequent shifting of vehicles， in this paper a method for energy consumption optimization of in-wheel motor-driven 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 in-wheel motor dynamics model considering road surface excitation is established as the lower control target. Secondly， taking the upper-level control target as the objective function of the torque optimization of the in-wheel motor vehicle， the motor torque and speed energy limit as the inequality constraints， and the fuzzy control method for objective function weight distribution， the upper-level torque optimization model is established based on NSGAⅡ. At the same time， the lower-level in-wheel motor vector control model of torque overshoot and delay caused by road surface excitation is established based on the sliding mode anti-disturbance observer. Finally， the joint simulation of Simulink and CarSim of a four-wheel in-wheel motor-driven 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 CLTC-P operating conditions. The in-wheel motor bench test shows that the energy consumption optimization method of in-wheel motor-driven vehicles considering torque fluctuations can effectively reduce energy consumption under WLTC and CLTC-P operating conditions.

In the realm of vehicle dynamics， the sideslip angle is a critical parameter. For the challenges posed by the current model-based methods， which heavily rely on the accuracy of dynamic models， and the poor robustness of data-driven methods in unfamiliar operating conditions， in this paper a sideslip angle estimation method based on a hybrid of physics and data-driven approaches （DeepPhy） is proposed. The aim is to combine the strength of physical modeling and data-driven techniques to achieve reliable and accurate estimation of the sideslip angle. DeepPhy integrates prior values of the sideslip angle obtained from the lateral force model of the rear axle tires with a deep neural network， enabling the learning of nonlinear mapping relationship not captured by the physical model， thereby enhancing the model's reliability in unfamiliar conditions. The simulation results indicate that under continuous DLC conditions， the RMSE of the estimation results from DeepPhy is reduced by 93% compared to the physical model method and by 63% compared to the data-driven method， exhibiting robustness in scenarios with limited data. Real-world validation further confirms DeepPhy's exceptional generalization capabilities， as the models trained through simulation can be transferred to real-world conditions while maintaining high-precision estimation results.

Distributed electrically-driven heavy-duty vehicles achieve compound steering through the differential between the steering assist motor and the wheel-end drive motor. By means of coordinated control of multiple motors， various active safety control functions are realized and the driver's operational burden is reduced. For the driving safety issues brought about by the failure of the drive motor and the steering assist motor， in this paper a fault-tolerant control strategy encompassing mode switching and fault-tolerant torque distribution is proposed. The proposed mode-switching strategy， based on the vehicle pose information， introduces in the yaw rate residual function as the switching condition for the fault-tolerant mode. The proposed fault-tolerant torque distribution strategy takes into account of the output redundancy and the vehicle's stability to solve for the target output torque of the drive motor and the steering assist motor. Finally， a hardware-in-the-loop simulation platform is established to verify the effectiveness and real-time performance of the control strategy.

Establishing an accurate air spring model is key and crucial for analyzing the vibration characteristics of air suspension electric buses. For the variation in the characteristics of air springs under different load， a comprehensive model of the air spring with dynamically adjustable parameters is proposed， considering the effect of rubber airbag force and changes in payload， taking a membrane-type air spring as the research object. Key parameters of the rubber airbag are identified through mechanical experiments， and the accuracy and effectiveness of the proposed model are verified. Based on the comprehensive air spring model， a 14-seat， 21-degree-of-freedom electric bus dynamics model is established. The model's validity is verified through simulation comparison with a CarSim model with identical parameters. Subsequently， the influence of air spring nonlinear characteristics， vehicle speed， road roughness， and passenger distribution on the dynamic performance of the electric bus system is analyzed. The study shows that the proposed comprehensive air spring model can dynamically adjust its parameters in response to variation in load and road excitation. The hysteretic mechanical characteristics of the air spring cannot be ignored. Compared with linear models， thermodynamic models without considering hysteresis， and equivalent model of air spring， the comprehensive air spring model significantly reduces suspension deflection， with reduction of 22.95%， 42.13%， and 18.20%， respectively. Increase of vehicle speed， lower road quality， and uneven passenger distribution negatively affect the ride comfort of the electric bus， with discomfort increasing for passengers seated farther from the bus's center of gravity.

There are many investigated parameters in the powertrain mounting system （PMS） of electric vehicles， and it involves multi-performance design. For the problem that the traditional single-output sensitivity analysis is difficult to accurately evaluate the influence of system parameters on the system comprehensive performance， the multi-output response sensitivity analysis of the PMS of electric vehicle is carried out by considering the uncertainty of system parameters. Firstly， a 13-degree-of-freedom analysis model of PMS is established， and the uncertain parameters of system are described by the random variables. Then， based on the summation of covariance decomposition， the first order index and the global sensitivity index of the multi-output response of system are derived. Next， a method of calculating the sensitivity indexes of multi-output response is proposed based on Monte Carlo analysis. Finally， the effectiveness of the proposed method is verified by the numerical example of the PMS of an electric vehicle. The analysis results show that the single output sensitivity analysis may not be able to accurately evaluate the comprehensive influence of parameters on the system response， and it may produce contradictory results. The proposed multi-output sensitivity analysis method can effectively evaluate the comprehensive influence of system parameters on the system response， and it can obtain more accurate sensitivity ranking for system parameters.

For the limitation of traditional type-1 Smith fuzzy control in terms of inadequate time delay compensation and insufficient robustness under varying parameter driving conditions， an interval type-2 Smith fuzzy time delay compensation control method is introduced for magnetorheological （MR） semi-active suspension systems. This approach incorporates the vehicle's vertical acceleration， suspension deflection， and tire dynamic displacement as the state input of the control system， enabling a comprehensive capture and response to dynamic vehicle changes. By introducing in upper and lower membership functions， the method defines clear membership intervals for fuzzy variables， which are then leveraged to calculate activation intervals under various fuzzy rules， significantly enhancing the system's anti-interference capability. Additionally， the Center-of-sets algorithm is innovatively introduced into the fuzzy reduction process， avoiding redundant normalization calculation during the type reduction of type-2 fuzzy sets， thereby improving the system's execution speed and real-time performance. Simulation results demonstrate that the proposed interval type-2 Smith fuzzy delay compensation control strategy achieves improvement in both control effectiveness and robustness for MR semi-active suspension systems， effectively tackling complex and varied driving environment.

The damper is a core component of the suspension system， exerting significant influence on both vehicle handling and ride comfort. Traditional damper exhibits unstable response and distorted characteristics under high-temperature and high-speed conditions. Moreover， the development process heavily relies on extensive experimentation， leading to prolonged design cycles and increased cost. For this， firstly， the scheme of the continuous damping control （CDC） damper with built-in combination valve is proposedd， and the response characteristics of the valve system are quantified based on finite element method. Secondly， the nonlinear features of the damper external characteristics are analyzed， with which the hybrid model that combines piecewise models and compensation models is established to effectively capture the nonlinear gas hysteresis characteristics. Finally， all damper model parameters are identified based on measured data under different current-frequency coupling excitation effect. Subsequently， the parameters frequency-varying characteristics and the model accuracy are verified. The results indicate that the accuracy of the proposed hybrid model is improved by 55.91% on average compared with the piecewise model， with the error less than 10% compared with the measured data. The proposed innovative damper structure along with its characteristic modeling method can significantly enhance damper performance while simultaneously reduce development cost.

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.

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 low-adhesion 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 low-adhesion 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.