In the application of vehicleroad 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 vehicleroad 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 vehicleroad 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 nonconnected 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 highdefinition map. Then the selflocalization 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 vehicleroad cooperative sensing method incorporating high definition map under high communication delay can effectively deal with the communication delay problem and improve the multivehicle perception accuracy of the edge computing unit, thus improving the accuracy, stability and continuity of the twin map dynamic data.

Multiclass 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 networkbased detection algorithm, DSODet, is proposed to handle the challenges of partial occlusion and smallscale targets in dense traffic environment. Firstly, a lightweight CSPDarkNet network is used to extract features from traffic images. Then, a multiscale feature fusion upsampling module is designed to enhance the representation capability for hardtodetect targets. Next, a highresolution detection branch is incorporated to improve detection accuracy for smallscale 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 stateoftheart 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/XMUTVsionLab.

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.

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 trajectoryplanning 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, realworld 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 realtime performance.

To improve the accuracy and stability of path tracking for light commercial vehicles under complex curvature conditions, in this paper a PredictivePure Pursuit (PPP) control method is proposed. Firstly, a PPP 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 cosimulation 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 Bayesianoptimized PPP controller is 0.113 m, 0.045 rad, and 153.2°, respectively, all of which are superior to the corresponding metrics of the PPP controller based on fuzzy control and the MPC controller, indicating that the proposed controller maintains good precision and stability under complex curvature conditions.

In the realm of vehicle dynamics, the sideslip angle is a critical parameter. For the challenges posed by the current modelbased methods, which heavily rely on the accuracy of dynamic models, and the poor robustness of datadriven methods in unfamiliar operating conditions, in this paper a sideslip angle estimation method based on a hybrid of physics and datadriven approaches (DeepPhy) is proposed. The aim is to combine the strength of physical modeling and datadriven 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 datadriven method, exhibiting robustness in scenarios with limited data. Realworld validation further confirms DeepPhy's exceptional generalization capabilities, as the models trained through simulation can be transferred to realworld conditions while maintaining highprecision estimation results.

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 hightemperature and highspeed 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 builtin 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 currentfrequency coupling excitation effect. Subsequently, the parameters frequencyvarying 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.

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.

Injection overmolding after compression for thermoplastic composites balances the low cost and high performance. It can quickly and stably achieve integration of continuous/discontinuous fiberreinforced 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 bimaterial 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 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.